ToK Essay 5 Nov 23: "The world is the way we understand it"

“the world isn’t just the way it is, it is how we understand it—and in understanding something, we bring something to it” (adapted from Life of Pi by Yann Martel)? 

Is this always the case ?

Discuss with reference to history and the natural sciences.

So at the start of the this essay we have a clear proposition that the world is constructed or created by our processes of understanding it. In philosophy this is called a rationalist argument, however you don't necessarily need (or want) to refer to the philosophical debate between rationalism and empiricism. That said this debate will very much underpin the discussion that I bring to this essay.

Let's start by quickly looking at some of the interesting words used in the prescribed title, and the first phrase of Interest here is the world isn't just the way it is. the use of the isn't just and later use of the phrase bring something to it, would indicate that the human construction of knowledge is an additive process, i.e. we add to that which is gained from the senses rather than essentially alter, or change, it.

 The use of the term understanding in the quote indicates that this process is one in which we bring meaning to the world that is presented to us. This is a rich area for further discussion in the essay should students choose to take this route. 

ToK Essay 5 Nov 23 - a few overview arguments

In these overview notes we will quickly and broadly look at to arguments for supporting the proposition in the prescribed title, and to arguments evaluating or opposing the proposition in the prescribed title. in the essay guidance notes “10 Arguments for essay #5 “ we go into a lot more detail on 10 arguments including knowledge arguments, evaluations or counterclaims, suggestions for real life situations, and implications arising from the knowledge arguments. Those notes are over 8,300 words long and give you a lot more than can be achieved in this web page. 

ToK Essay 5 Nov 23 - interpretation

Argument one for the proposition could be that The interpretation that we bring to the world doesn't radically change the external reality so much as give it internal meaning. This argument could be applied to either AoK History or AoK Natural Sciences. The essential argument here is that our additive interpretation (understanding) doesn’t radically change the world, but just makes it gives it a representative meaning so that we can label it and categorise it within pre-existing knowledge frameworks. Students who have studied Knowledge and Language as an optional theme can draw upon some of the debates covered in that unit. 

This argument is easier to apply to AOK Natural Sciences than it is to AOK history. in Natural Sciences we would be arguing that the scientific method produces objective and accurate data about an external reality, and then human interpretation of that data retains the essential features and characteristics of that external reality. To apply this argument to AoK history we need to develop an understanding of a process which minimise the role of perspective and subjective biases in the historiographical process. Well this is a hard argument to make, it is not an impossible argument. Students following this argument may want to look at the production of historical knowledge as a process of empiricism. This is explored in a lot more detail in the essay guidance notes.

ToK Essay 5 Nov 23 - interpretation (different perspective)

Argument two is that the world we experience is largely an interpreted world rather than a real world. in philosophy this would be called irrationalism however you don't have to use this term in your essay. The argument here is that we select, group, label, categorise, and add meaning, to the experiences of the world. As such we fundamentally change what we know about the world around us.

This argument can be applied to both AOK history and AoK Natural Sciences. however it is far more straightforward to apply to AOK history. Students who would like to follow this route should consider looking at history as a product of human construction, or history as a rationalist process. The argument in history is a constructionist argument that we select specific historical knowledge, and interpret it in ways that serve pre-existing knowledge, to confirm a preferred world view. The arguments in the Natural Sciences would be that the operationalization of variables and the interpretation of scientific result fundamentally changes that which is observed. again, we go into this in a lot more detail in the detailed essay guidance notes .

ToK Essay 5 Nov 23 - reality

Turning towards arguments against the proposition in the prescribed title. The first arguments would be that the external world is how we experience it, we do not add things in through interpretation. As such we are arguing that our knowledge of the world is objective and accurate. This is an empiricist argument (however you do not have to use that term in your essay). This argument could be used both for AOK history and AOK Natural Sciences, however it is far easier to make for AOK natural sciences. The hypothetical deductive scientific method is essentially an empirical method which is designed to minimize human interpretation and subjectivity. 

ToK Essay 5 Nov 23 - context

The final argument covered here, concerns the role of context in our understanding of the world. An argument could be developed that the degree to which we interpret external reality is dependent upon the context of the knower, and the knowledge that they are acquiring at that time. Context can include a very wide range of factors including the cultural perspectives of the knower, the intention and purpose of the knower, the type of knowledge that is being acquired, and the pre-existing knowledge frameworks. 

This argument lends itself particularly well to AOK history, but can also be applied in AOK natural sciences. Contrasts could be drawn using real life situations in AoK history. We could consider historical knowledge which has been produced for different purposes, or within different cultures, or by historians with different perspectives. This will show the role of context influencing the different ways that the same historical event has meaning (“understanding”) attached to it. This argument is developed and a lot more detail, including real life examples that you could draw upon, in the detailed guidance essay notes.

We also have 25 questions that you could ask artificial intelligence (such as ChatGPT, or Bard) to help you to write this essay.

This is just a brief overview of four of the arguments that could be used in this essay.  Our detailed guidance notes have a lot more detail on these, and six other arguments.

ToK Essay #4 Nov 23: Values problematic

PT#4Nov23: Is it problematic that knowledge can be shaped by the values of knowledge producers ?Discuss with reference to any two areas of knowledge.

Choice of AoK

You have the choice of any areas of knowledge, therefore We recommend that you choose two areas that have good contrasting methodologies in the production of knowledge. you're going to be looking at the influence of values on the production of knowledge and therefore the role of the knowledge producer will be crucial. if they have different roles then you can contrast the influence of values. a typical good contrast might be between area of knowledge the Arts and area of knowledge natural science.

ToK Concepts

 This essay lends itself perfectly to the use of the 12 ToK Concepts.The May 2022 subject report recommends that students use the concepts in their essays. any of the 12 Concepts can be applied in this essay. I have outlined four of the concepts below, and I go into the details of 10 of the concepts in the essay guidance notes 10 arguments for essay number four, available at this link.

Problematic

Students will need to define the term problematic or problem as it's at the center of the prescribed title. you may want to think about who is defining the idea that it may be a problem, what type of a problem, a problem for whom, or what? you may also want to consider whether problems are universal or context bound, Etc

Let's move on to look at how four of the ToK Concepts could be used to answer this prescribed title.

Interpretation

In any area of knowledge the knowledge producer interprets  many stages of the knowledge production process including  the object to be produced, the way in which it is to be produced, and the evidence arising from the method of production. the ways in which knowledge is produced varies by area of knowledge and knowledge producer but it all includes interpretation. the important thing about interpretation is that it is informed by values. the values of the knowledge producer influence the way in which all stages of the knowledge production process are interpreted. This argument can be developed in various ways depending upon the area of knowledge chosen. 

Whether the interpretation of value-based knowledge is a problem or not depends upon a range of factors such as the values held by the person who is interpreting the knowledge, the values of the context within which the knowledge is interpreted, and interpretation of the purpose of the knowledge and it's alignment with the knower.

(I go into a lot more detail on this, including real life examples, in the essay Guidance Notes).

Culture

Culture could be described as a set of values, interwoven with a system of Symbols and meanings. as such, a fairly coherent argument can be developed that culture (& cultural values) influences the production of knowledge. Whether the influence of  cultural values is a problem very much depends upon a wide range of issues including the cultural alignment of the person who is defining the problem, the culturally defined purpose and value of the knowledge produced, and the resultant evolution of culture over time resulting from the knowledge produced.

Evidence

The values of the knowledge producer can affect both the production, identification and interpretation of evidence. that which is considered evidence is, arguably, very much influenced by the values of the person considering it. Different areas of knowledge place different emphases on the nature of evidence, different types of knowledge constitute evidence in different ways according to the area of knowledge. As such, students who are writing this essay could contrast what evidence looks like in for example AoK mathematics with what evidence looks like in for example AoK The Arts. 

Potentially this is problematic in terms of the objectivity and purpose of the knowledge produced. Again, this will very much depend upon the values of the person who is making the judgement on whether it is problematic. 

Justification

Arguably values influence the justification for producing new knowledge, and then the justification for the use of that new knowledge. such justification may be based upon the values of the knowledge producer, the institution to which they may be long, the academic discipline that they are working in, or wider society. values based justification is potentially problematic for a range of reasons including the hierarchical use of knowledge for the articulation of power, Producing biased perspectives, and disregarding aspects of knowledge. In a typical ends versus means type argument justification can lead to legitimisation, if the values underpinning the justification are not shared by a large sector of society this could be problematic at ethical, moral and cohesive levels.

The Essay Guidance notes "10 Arguments for essay 4" go into a lot more detail on the four concepts above, and on six other concepts. Those notes also include

• definitions of terms

• real life examples

• evaluation

• implication points.

We also have 25 prompt questions that you can ask artificial intelligence such as chat GPT to give you the detailed and specific content that can be appropriately applied in your essay. 

ToK Essay 3 Nov 23 "Dangerous Experts"

ToK Essay 3 Nov 23 "Dangerous Experts":

In the acquisition of knowledge, is unquestioningly following experts just as dangerous as completely ignoring them? Discuss with reference to the human sciences and one other area of knowledge.

The Acquisition of knowledge is generally taken as gaining knowledge, or becoming knowledgeable. Whilst this term is not defined in the ToK Study guide it can be assumed that it refers to the process of becoming a knower. Learning, in both formal and informal senses, is a process of knowledge acquisition. 

Choice of Area of Knowledge - ToK Essay 3 Nov 23 Dangerous Experts.

You are directed to use Human Sciences, and are given a free choice on the other AoK. You may want to pick an AoK which gives a contrast to ways of acquiring knowledge in Human Sciences as your second AoK. This could also be (indirectly) linked to a contrast method of producing knowledge. Consider whether ways of acquiring knowledge in AoK Maths, History or The Arts contrasts well with AoK Human Sciences ? A good contrast in the processes of acquiring knowledge will give you greater potential for developing good evaluation, and implication, points in the essay.

A hard choice.

We are offered a choice between “following unquestioningly” or “ignoring completely”. Obviously we want to sometimes accept, sometimes ignore, sometimes accept critically, and sometimes ignore in an informed manner. However, these choices are not given to us. It is advised that students directly address the choices given in the PT before arguing for any of the nuanced positions between these two choices. You will need to explain to the examiner why you are rejecting the two positions given if you want to argue for an ‘in-between’ position.

Further, the focus of the question is actually asking us which is the more dangerous of the two choices given. As such, we can assume that the safer nuanced (in between) position is less important than the relative dangers posed by the two positions given. It is advised that students focus discussion on the relative dangers of the two positions rather than the in-between preferred positions.

Experts

Consider who these ‘experts’ are in each area of knowledge. Questions that could lead to knowledge points include:

• How did they become to be labelled as ‘experts’ ? 

• Do ‘experts’ all share the same perspective in a discipline / AoK ? 

• Are there competing ‘experts’ ? 

• Why are they labelled ‘experts’ ? 

• Are we considering the expert themselves, or their knowledge ?

Danger.

You will need to think about what these ‘dangers’ are that could arise from following / completely ignoring these experts. Danger to what / whom ? Danger for what ? You may want to consider the development of the Area of Knowledge, the type of knowledge produced, or the uses of that knowledge. There can be obvious links to ethics here, which could be contrasted with arguments regarding objectivity.  

Objectivity.

In directing us to consider AoK Human Sciences we are offered the opportunity to consider the function / purposes of the Human Sciences. This is a rich area for debate and discussion. There is a potential debate between the objectivity of the Human Sciences vs the ethical implications of knowledge developed in the Human Sciences. If you study Economics, Environmental Systems, Geography or Psychology this debate will be evident to you. Students taking Business Management can also develop such debates regarding ethical business practices etc. This is an area where there are clear opportunities for you to draw upon the content of your Group 3 Diploma Subject. Ask your Grp 3 teacher for advice if you are unsure of the debate between ethics and objectivity in your essay.

Ethics.

The discussion concerning ethics could occur at 2 levels:

(i) The ethical consequences of ignoring / unquestioningly accepting the application of the knowledge that experts produce.

(ii) The ethical consequences for the development of knowledge in the discipline / AoK of ignoring / unquestioningly accepting the knowledge that experts produce.

Context

The context of the expert’s knowledge, the acceptance / rejection of experts, and the application of the expert’s knowledge will change. As such the dangers posed by accepting / rejecting experts will also change. This provides a rich seam of discussion in any area of knowledge. Context provides great evaluation and implication points for any Area of Knowledge.

Confirmation Bias

Following people unquestioningly, and ignoring them completely, potentially gives rise to a range of fallacies (see this post on fallacies), particularly confirmation bias. Any AoK can give great opportunity for a discussion on confirmation bias in the acquisition of knowledge, and its consequences for the development of biased perspectives of the knower.

Foundational / Definitive Knowledge.

There is a potential discussion around the scope, or definition, of a discipline / AoK. Is there a set of ‘expert knowledge’ which must be acquired in order to develop an understanding of that discipline ? For example, can you study economics without learning about theoretical vs empirical models, Macro & Micro Economics, Pluralist vs Free Market models etc ? Obviously economics students are not taught to accept this knowledge ‘unquestioningly’, they are taught how to evaluate this knowledge. However, arguably they are following the evaluative knowledge unquestioningly as well…,

Innovation / development of new knowledge

One way of thinking about AoKs is whether the acquisition of knowledge in that AoK is more “top-down” or “bottom-up”. Top-down processes are more hierarchical in which the knower is discouraged from developing critical, personal, perspectives. Bottom up processes of acquisition are led more by enquiry, in which the knower is encouraged to develop their own perspectives. Contrasting two AoKs in this way will allow the student to develop an argument about the dangers which may be inherent to, or arise from, either type of knowledge acquisition process.

These arguments, and many more developed in far more detail in the notes: 10 Arguments for ToK Essay #3 available from TokToday - those notes contain

• knowledge arguments

• evaluation points

• Implications of knowledge arguments

• suggested real life situations (with references)

We also have a list of 25 questions that you can ask Artificial Intelligence (such as ChatGPT) about ToK Essay #3. These questions are designed to get relevant content which is appropriate for this ToK Essay.

ToK Essay 2 Nov 23: Beautiful Patterns

PT#2 N23  If “the mathematician’s patterns, like the painter’s and the poet’s, must be beautiful” - what would be the impact on the production of mathematic and artistic knowledge?”

ToK Essay 2 Nov 23 beautiful patterns:

This question could be approached as a consideration of the ways in which knowledge is made (produced, constructed, discovered) rather than about the knowledge itself. The PT does direct us to consider the “impact on the production of knowledge”.

Beautiful subject rather than object.

If it’s about the production of knowledge, rather than the knowledge produced, its about subject rather than object. The word “beauty” could lead a lot of people to think that it’s about the object of knowledge production, however we’d advise students to focus on subject rather than object.

Obviously, subject and object are linked, and the former may lead to the latter. Maybe a good place to start is to think about the reasons why mathematicians make knowledge, and compare it with the possible reasons for artists making knowledge. 

Purposes of knowledge production.

There’s potentially a nice contrast in the purposes for knowledge production in The Arts and Mathematics. We could compare the debate in Mathematics between Pure and Applied Maths with the debate in The Arts over whether artists make knowledge for themselves or for their audience.

That debate in Maths would be that Pure Maths is made purely to extend mathematical knowledge, whilst Applied Maths is made to solve real world problems. Conversely, the debate in the Arts would be that some art is made just for the artist to express their inner world, whilst other art is made to engage the audience  (“the knower”).

Link "beautiful patterns" and interpretation. 

Beauty is often thought of as a relativist concept (“beauty is in the eye of the holder”).

We’d advise students to be wary of relativist arguments as they are rarely sufficient to attract high marks in the ToK Essay. 

Potentially, a more substantial argument would be that true beauty in the production of knowledge should be free from / independent of external constraints such as audience preferences in the arts, and real world problems in Mathematics. 

A further argument could be developed around the role of interpretation in the production of knowledge. Processes of interpretation of knowledge in the arts could change both how patterns are identified and how they are used in the production of knowledge. 

As such the role of interpretation in the production of artistic knowledge could change the explanations, justifications and perspectives pertaining to that knowledge - which in itself could change the definition and attribution of beauty.

In Maths the interpretation of pre-existing knowledge (e.g. axioms, theorems and models) could affect both the justifications and evidence used by mathematicians. This could be further applied to the discussion of the relationship between forms, objects and theorems in Maths. 

The role of context in the production of (beautiful) mathematical and artistic knowledge.

There’s a really interesting debate to be had about whether the patterns are merely context bound, or are they universal. This debate could be developed to consider whether beauty is also context bound or universal. This could be further developed to consider whether the methodologies of knowledge production are context bound or universal.

If the patterns, beauty and even the methodology are context bound then there may not be an underlying near aesthetic structure to mathematical and artistic knowledge (as implied by Mr Hardy) because that definition of beauty is ever changing. 

These arguments, and many more developed in far more detail in the notes: 10 Arguments for ToK Essay #2 available from TokToday - those notes contain

• Over 9000 words of ToK content.

• knowledge arguments

• evaluation points

• Implications of knowledge arguments

• suggested real life situations (with references)

We also have a list of 25 questions that you can ask Artificial Intelligence (such as ChatGPT) about ToK Essay #2. These questions are designed to get relevant content which is appropriate for this ToK Essay.

Essay 1 Nov 23: Facts alone - Enough?

ToK Essay 1 Nov 23 Facts alone - are they enough to prove a claim ? This question gives you the freedom to choose any two areas of knowledge to discuss. Choose wisely as this will make writing the essay far easier. 

Structure - it's a fact!

Students are advised to choose two areas of knowledge which give them good contrasts in both the production of knowledge, and have contrasting methodologies for proving claims.

If your two areas of knowledge differ a lot in these areas then it will be easier to develop evaluation points (giving you higher marks in your ToK essay).

View fullsize

In this essay we’re going to try to develop a continuum of arguments. We want to make some arguments that facts are enough to prove claims. In doing so we’re going to be interested in different ways of proving claims, different types of proof, and varying definitions of proof.

We are also going to want to make some arguments that facts are not enough to prove claims, and we’ll consider what other things might be needed to prove claims (and in doing so we’re going to bring in ToK concepts like evidence, justification, interpretation and maybe even TRUTH).

The command term is “Discuss”, therefore you need to consider different perspectives in the essay. (We have produced 10 Arguments for Essay #1 November 2023 that cover a range of different knowledge arguments that could be used - you can pick those notes up from this link).

What makes a fact: necessary or sufficient?

We could think about this question in terms of whether facts are necessary or sufficient to prove a claim. At a more sophisticated level we could consider what is necessary, and what is sufficient, to establish a fact in the first place. 

Necessary conditions are conditions that must be met in order for a particular outcome to occur, while sufficient conditions are conditions that, if met, will guarantee that the outcome will occur. Here are some examples that illustrate the difference between necessary and sufficient conditions:

1. Necessary but not sufficient: In order to pass a maths test, it is necessary to know the material. However, knowing the material is not sufficient to guarantee that one will pass the test. Other factors, such as test-taking skills and time management, may also be necessary to pass the test.

2. Sufficient but not necessary: If a person has a college degree, it may be sufficient to qualify for a particular job. However, having a college degree is not a necessary condition for all jobs, as some may require other qualifications or skills.

3. Both necessary and sufficient: In order to become a licensed physician, it is both necessary and sufficient to graduate from medical school and complete a residency program. This means that without completing these requirements, one cannot become a licensed physician, and completing them guarantees that one will become a licensed physician.

4. Neither necessary nor sufficient: Having a driver's licence is neither a necessary nor a sufficient condition for owning a car. While having a licence may be helpful, it is not necessary as some people may choose to hire a driver or use public transportation. Additionally, having a licence is not sufficient as owning a car also requires purchasing or leasing a vehicle.

Applying different types of facts to Areas of Knowledge

Setting up the different conditions for what is sufficient, and what is necessary, to prove a claim can change whether a claim is proven. Different Areas of Knowledge will have different criteria for defining what is necessary, and what is sufficient, for proof. Do these constitute "facts alone?"

Turning to some of the arguments that facts alone are not sufficient to prove a claim. When we look at some of the more qualitative Areas of Knowledge such as The Arts or History, facts are not quite as definitive as they are in the Sciences or Maths. This can give us a bit more freedom to debate whether facts can stand alone as proof of a claim.

In both AoK The Arts and AoK History we could have a good discussion about what a fact is, you could consider:

• Who is constructing the fact.

• Their intention / purpose for constructing it.

• Who validates it as a fact.

• What knowledge was included, and what was excluded in the establishment of the fact.

• What are the perspectives which both led to, and arise from, the fact.

• What values underlie the fact.

If you follow this argument you need to remember that the essay title is not whether facts exist, but whether they can be used alone to prove a claim. As such, this argument is that no fact exists entirely on its own, but all facts are subject to a knowledge construction process, and the degree to which a fact proves something depends upon the degree to which the knowledge production process is accepted as objective.

The role of perspectives is crucial in the construction of facts in both AoK The Arts and AoK History. Further, different methods of knowledge construction can produce different facts. This means that maybe different types of proof are needed for different types of evidence, maybe we could have differing thresholds of proof, or maybe proof isn’t possible at all, despite the so-called “facts”. We could consider this in terms of a hierarchical construction of proof in a power based value system

These arguments can also apply to AoK Maths, Human Sciences, and Natural Sciences.

Considering AoK The Arts in a bit more detail. We may want to consider what constitutes a fact in artistic knowledge. Is the meaning of artistic knowledge decided by the audience or by the artist? 

Knowledge arguments could be developed around the connotation and denotation of knowledge. Part of the essay could be based on the debate between artistic knowledge as object vs artistic knowledge as a process of subject. 

The role of context in the production of knowledge (in any AoK) could also be considered. Context can be applied to any of the areas of knowledge, it can change both the definition and labelling of facts, the production of knowledge, and the interpretation of knowledge. Context opens up a range of ToK concepts such as Culture, Interpretation, Justification, Explanation and Objectivity. 

This is just a very brief overview of a few of the issues that we explore in detail in 10 Knowledge Arguments for Essay#1 November 23. Those notes give you:

• detailed knowledge arguments

• definitions of terms

• evaluation points

• implications

• suggestions for real life situations. 

Those notes are over 10,000 words long, so there’s more than enough there to help you with your essay.

Secondly, We have “25 questions for Chat GPT to help you with your ToK Essay”. IB are allowing you to use ChatGPT (and other AI’s) in your ToK Essay, so long as you properly reference content that it produces. The thing with ChatGPT is that you have to know exactly the right questions to ask it to get the right content and answers out of it. This document will help you to ask it the right questions.

Fallacies in ToK

In ToK we are concerned with questions such as how knowledge is acquired, the nature of truth, and the extent of our knowledge. One of the key challenges in ToK is identifying and avoiding fallacies – errors in reasoning that can lead us to false conclusions. In this blog post, we will explore the main types of fallacies found in ToK.

1. Ad Hominem Fallacy

The ad hominem fallacy is a type of fallacy in which the arguer attacks the person making the argument rather than the argument itself. In ToK, this fallacy might take the form of dismissing an argument because of the person making it rather than addressing the merits of the argument. For example, if someone argues that climate change is real, and someone else dismisses the argument by saying that the person making the argument is a liberal, that would be an ad hominem fallacy.

2. Straw Man Fallacy

The straw man fallacy is a type of fallacy in which the arguer misrepresents the opponent's argument in order to make it easier to attack. In ToK, this might occur when someone misrepresents an opposing view in order to make their own view appear stronger. For example, if someone argues that atheism is the belief that there is no god, and an atheist argues that atheism is simply the absence of belief in a god, the theist would be committing a straw man fallacy by misrepresenting the atheist's position.

3. Appeal to Authority Fallacy

The appeal to authority fallacy is a type of fallacy in which the arguer cites an authority figure in order to support their argument, without providing any further evidence or argumentation. In ToK, this might occur when someone argues that a particular belief is true simply because an expert or authority figure says it is true. However, this is not a valid argument, as experts and authority figures can also be wrong or biased.

4. False Dilemma Fallacy

The false dilemma fallacy is a type of fallacy in which the arguer presents only two options as though they are the only options, when in fact there may be other possibilities. In ToK, this might occur when someone argues that either science or religion can provide us with the truth about the world, ignoring the possibility that both may be useful in different ways.

5. Circular Reasoning Fallacy

The circular reasoning fallacy is a type of fallacy in which the arguer uses the conclusion of the argument as one of the premises. In ToK, this might occur when someone argues that a particular belief is true because it is supported by scripture, and then uses the belief in scripture as evidence for the truth of the belief. This is not a valid argument, as it simply assumes the truth of the conclusion.

In conclusion, fallacies can be a major obstacle to gaining knowledge and understanding in ToK. By being aware of the most common types of fallacies, we can better identify them and avoid them in our own reasoning and arguments. This, in turn, can help us to arrive at more accurate and well-supported conclusions about Knowledge acquisition and production.

Daniel, Lisbon, March 2023

Are all swans white? (Falsification)

The Principle of Falsification in Theory of Knowledge

The Falsification Principle is a method used in science to test the validity of scientific statements or theories. It was first introduced by philosopher Karl Popper, who argued that scientific knowledge must be testable and falsifiable, meaning that it must be possible to demonstrate that it is false. In other words, a scientific statement or theory can only be considered true if it is possible to prove it false.

To illustrate the Falsification Principle, let us consider the statement "all swans are white". If this statement is true, then every swan that has ever existed or will exist must be white. However, this statement can be falsified if just one black swan is found. The discovery of a black swan would prove that the statement "all swans are white" is false, as it would contradict the statement's claim that all swans are white. This example demonstrates the power of the Falsification Principle, as it shows how a single observation can disprove a theory or statement.

The Falsification Principle is important for establishing objective knowledge in science because it provides a way to test scientific statements and theories. By attempting to falsify a theory, scientists can determine whether it is true or not. If a theory withstands numerous attempts at falsification, it becomes more likely to be true. This process of testing and refining scientific knowledge helps to establish a strong foundation of objective knowledge that can be relied upon for future research.

One of the key benefits of the Falsification Principle is that it prevents scientists from making unfalsifiable claims. An unfalsifiable claim is one that cannot be proven false, and therefore cannot be tested using the scientific method. For example, the claim that "God exists" is unfalsifiable, as it is not possible to prove that God does not exist. Since this claim cannot be tested, it falls outside the realm of science.

The Falsification Principle also helps to prevent scientists from making unjustified claims. By requiring that scientific statements and theories be testable and falsifiable, the Falsification Principle ensures that human and natural scientists do not make claims that cannot be supported by evidence. This helps to maintain the integrity of scientific research and ensures that scientific knowledge is based on sound evidence.

In conclusion, the Falsification Principle is an important tool in AoK Human Science and Natural Science for establishing objective knowledge. By requiring that scientific statements and theories be testable and falsifiable, the Falsification Principle ensures that scientific knowledge is based on sound evidence and prevents scientists from making unfalsifiable or unjustified claims. The example of "all swans are white" demonstrates how the Falsification Principle can be used to test scientific statements and theories, and how it can help to establish a strong foundation of objective knowledge in science.

Daniel, Lisbon, March 2023

Further related posts can be found at:

Historical Truth

Applying the scientific method.

Priest's religious knowledge - do they believe in God?

Today's post can be used as an RLS for The Core Theme Knowledge & The Knower, and as RLS for AoK Human Sciences, and the Optional Theme Knowledge and Religion. It's about Priests who don't believe in God, and was the most popular post on my old ToK Blog (ToKTrump). There are obvious links with the role of Religious Knowledge in this research.

The Core Theme: Knowledge and The Knower is a very broad unit encompassing a wide range of knowledge questions. It can be a little unwieldy if not focussed onto some key knowledge questions, or a set of themes. I have slowly developed a sense that my student's most illuminating learning in this unit is firstly that knowledge is constructed rather than give, secondly that that process of construction is highly contextualised, and finally that it is not experienced as contextualised by the knower.

It's difficult to find the original study today, however I did find:

A review in The Atheist's Quarterly on JSTOR linked.

A summary on the website Why Evolution is True linked.

The world view of the knower is not experienced as contextualised, but is their "known world". We can draw upon Husserl's view of "Lebenswelt" or lived world here.

Why is The Priests who don't believe in God pertinent to ToK ?

Unstructured interviews of 5 non-believing priests carried out by Dennett & LaScola (2010) are a fascinating, and rare, insight into people who hold one set of beliefs, and yet live their lives by another set of beliefs. This dissonant state gives rise to a compelling set of insights for ToK. Whilst this example may not be 'typical' for most knowers, arguably it is in this somewhat extreme, contrast that we can uncover some of the processes of knowing that are experienced by all of us as knowers. Some of these implications include:

• We can hold contradictory knowledge (and beliefs) at the same time.

• Performativity of knowledge is both evidential and significant ( a behavioural element of knowledge).

• Internal ethical justification of knowledge occurs when the knower is presented with contradictory or inimical knowledge/beliefs/values.

• Even deeply held beliefs and values can change when the knower is challenged with opposing arguments/beliefs/values.

• When deeply held beliefs/values are changed the knower may not change their public behaviours according to the newly held beliefs.

• Beliefs & values (as forms of knowledge) can be known in many different ways by different knowers.

How to use this in ToK:

Core Theme: Knowledge & The Knower.

A quick skim through the KQs of the Core theme Knowledge & The Knower we can immediately see links to many KQs, particularly those dealing with the knower's knowledge in relation to others through interactions. I have allocated KQs to groups of students and asked them to use the research to explore their allocated KQ.

AoK Human Sciences.

The study can be relevant to all of the Hum Sci Knowledge Framework. Of particular interest to me is the link to perspectives and research methods. Specifically the validity vs reliability debate, and the value of extrapolation from a small (& we assume unrepresentative) sample.

Optional Theme - Knowledge & Religion.

Obviously there are a range of interesting KQs which could be explored using the Dennett & La Scola study. Of particular interest is the link between faith & religious beliefs, the role of culture's influence on religious beliefs, the relationship between reason and religious beliefs, etc.

For more ToK Lesson content for Knowledge and the Knower try this link.

For more ToK Lesson content on AoK Human Sciences try this link.

Conclusion.

The Dennett & LaScola research focuses on an atypical and unusual situation in knowledge. However, maybe it is in the strong contrasts found in the unusual cases that we can better understanding the framework and underlying processes of the knowledge held in all other cases.

If you would like more content like this (focussing on useful RLS), or have suggestions for further content please don't hesitate to contact me - Daniel@TokToday.com

Wishing you a great day!
Daniel, Lisbon, Jan 2023

Bibliography & References.

• “Atheists Anonymous.” The Wilson Quarterly (1976-), vol. 34, no. 3, 2010, pp. 77–78. JSTOR, http://www.jstor.org/stable/41000971. Accessed 11 Jan. 2023.

• whyevolutionistrue. “Dennett and LaScola Study of Nonbelieving Clergy.” Why Evolution Is True, 18 Mar. 2010, whyevolutionistrue.com/2010/03/18/dennett-and-lascola-study-on-nonbelieving-clergy/. Accessed 11 Jan. 2023.

Choosing your ToK Essay Question

As the November 2023 ToK Essay title starts it is a good time to revisit advice on choosing your ToK Essay title. During the 10 years that I marked ToK Essays as an IB Examiner I learned a lot about what makes a good ToK Essay. More importantly, how students can write a good essay with minimum stress.

Choosing your ToK Essay Question.

• The questions are called "Prescribed Titles"(aka PT), as they're not actually questions per se. They are knowledge statements, or knowledge questions, which you are invited to "discuss". This means that you need to consider a range of different perspectives arising from the title. When choosing your ToK Essay Question consider whether you have (or can develop) a range of perspectives on the title.

• Do not change, nor amend, a single word of the PT. You must address the question exactly as IB give it to you.

Ensure that you get the exact title from your teacher. Non-IB Sites (such as TokToday) are not supposed to publish the exact titles (they're copyrighted by IB).

Take your time choosing.

Choosing the ToK Essay title which is right for you is at least 50% of the 'battle for success' in the ToK Essay, so take your time at this stage. My students spend 4-6 weeks on choosing the title, it's super important to get this stage correct. In deciding which title to write you are should be trying to clarify:

• What does this question mean to me ?

• Do I have an initial instinctive view about this question ?

• Do I have some ideas about arguments that would help me to answer this question ?

• Do I have a destination for my answer ? (this may change later on, but something at an initial stage will be helpful).

These questions smoothly segue into our second tip on how to choose your ToK Essay Question: Blank Slate.

Know Yourself: Blank Slate those titles.

Try not to be too influenced by other people's voices at this stage of your essay writing process, try to hear your own voice.

Know your own mind, try not to be influenced by the voices of others. Approach the titles as a 'blank slate' - ie no pre-judgment.

Eventually you will have to write your own, original, response to the question. Therefore you don't want to be too influenced by other people's views at this stage (you can explore their views later). You need to be developing your own view(s) at this stage.

Be original.

Many of the best essays that I have read have been where the student developed their own original, and quite novel, argument at this early stage. Now, it may seem rather self defeating for me to tell you to stay away from internet advice sites either before or during the essay, however the particular type of content that I think you should be wary of is content that tells you what the arguments (claims / counterclaims) could/should be, or what real life examples you should use. This directive content doesn't improve your skills & understanding in ToK because you don't have to think for yourself.

Develop your own arguments, and think of possible real life examples to illustrate these arguments, before you start exploring the internet. Once you have your own original framework down you'll be in a good place to start further research. You can now use academic sources, non-academic sources and ToK specific sources to further develop your ideas and range of sources cited. If you wait until you have developed your own ideas before you go to the Internet (& other sources) then you won't be negatively influenced / swayed by the sources that you find. By developing your own ideas you will find writing the essay far easier than trying to develop other peoples ideas. This is why it's so important to spend time early in the essay writing process working on your own claims counterclaims and real life examples.

Know your destination.

Before you finally decide it is useful to have a rough idea of how you will resolve that question. That is a vague idea of what your final answer to the question might be (ie your "destination"). You don't have to know exactly how you are going to resolve the question before you choose the question (as many new ideas and perspectives will be developed during the planning and writing stages.

A rough idea of destination guides the writer like it guides the walker

A rough idea of destination guides the writer, like it guides the walker.

As you write the essay you will develop new ideas, make new connections and develop new perspectives. You will refine your arguments, and you may even change your arguments. This is a normal, and healthy, aspect of the writing process. You may even change your final destination, the important thing when choosing a question - have direction and destination in mind. Far too often I meet students who are "stuck", usually because they are unsure of their approximate final destination. They didn't work on a solution or resolution before they chose a question. - not a good place to be.

A few 'easy ways' to check your understanding of the title:

• Explain the question to a non-ToK student.

• Bring in your Mum, Dad, sibling (or even dog) and explain the title to them.

When they can understand your explanation you can be sure that you now have a solid understanding of the question.

More help is available:

• Check out the ToKToday YouTube Channel for tips (especially the Student Playlist).

• Pick up the E-book How to Write the ToK Essay in 6 Easy Steps.

If you need more help to choose your question, or to develop your question then get in touch (daniel@toktoday.com). Click here to book a ToK Coaching session.

Daniel,
Lisboa, Portugal, March 2023

Reflections on May 23 ToK Essay Session

Why write reflections on M23 ToK Essay session ? Well, I’ve been working with many students from all over the world with their May 23 ToK Essays. This is the first time that I’ve worked with students who are not in my school, nor in my ToK class. And that's an interesting learning experience for me - because I don’t know what they’ve been taught, how they’ve been taught, what their teacher’s approach to ToK is, nor where the emphases and reference points are in their ToK knowledge. So, this post is partly a consolidation of reflection for me, but it should also be useful for other ToK teachers, and maybe for ToK students who are about to start their learning.

In essence, in this essay session I’ve gone from Goffman’s participant-observer to observer-participant.

So, what are the reflections on M23 ToK Essay Session (main learning points) ?:

1. Making or building the argument.

A significant issue for many of the students with whom I worked was that they lacked the skills, or knowledge, to build a ToK argument. And this causes many consequent issues. It leads to:

Problems with definitions, I heard a lot of questions such as - “How do I define this key term ? or that key term?” , “I can’t think of a definition for…,” etc

Developing claims or counterclaims, I got questions such as “I can;t think of a counterclaim for this”, or “how can I make this into a claim ?”

And finally problems with identifying RLS - “is this a good RLS for____?”

These 3 problems (definitions, claims/counterclaims, and RLS) come from not having the skills to build a knowledge argument. Let me, briefly, take each one in turn.

Definitions:

In this session we wrangled with definition such as “cannot be explained”, “replicability”, “bubbles” etc

Obviously students should not be using dictionary definitions, but some students are still using dictionary definitions. The definitions are often the basis of the whole essay, if you can’t develop the definitions then writing the essay is problematic. 

There’s a mutually reciprocal relationship between developing the definitions of key terms and devising the knowledge claims / counterclaims.

There's also the problem of some students rewriting the key concepts - I particularly saw this with essay #2 - the vast majority of students I worked with doing this essay had redefined "cannot be explained" as "has not been explained". - there’s an important difference between the two,

Developing claims / counterclaims.

Some students seemed to be stuck in fairly rigid thinking when it came to devising claims / counterclaims. From some students there was a lack of flexibility / creativity in the interpretation of the title. This has made me go back to using more debate in the classroom with my own students, a sort of quasi application of De bono’s thinking hats.

Obviously, difficulty with developing claims / counterclaims can be partly due to a lack of clarity of definitions of key terms, or having dictionary based definitions of key terms.

Problems of identifying or applying RLS.

So this is the question “is X a good RLS for this claim ?”. Some students found it very difficult to identify appropriate RLS to demonstrate their knowledge claims.

Obviously if the claim is not fully understood then it's difficult to find RLS to demonstrate it.

It’s not about the RLS, it’s about the argument that’s being built. I believe that nearly ANY RLS can be used for ANY claim / counterclaim if the argument is well made.  I’ll make a future video where we can take claims at random and match them to random RLS to show how any RLS can be used for any claim if you know how to make the argument.

further, and wider, reflections on M23 ToK Essay Session include:

2. Question Choice. 

I think that essay # 6 on Methodology is by far the easiest prescribed title in this session, followed by essay # 1 on replicability. I won’t go into why I think they’re the easiest in this video, I made an earlier post & video about this linked here

Looking at all the data points that I have Essay #6 and Essay #1 are the LEAST popular titles in the session. I think that Essay #5 (visual representations) is probably the most popular.

Now, I certainly don’t think that we should tell students which essay to take - it’s meant to be their personal authentic reflection. However, Essay #6 & #1 have really straightforward structures, they don’t have multiple assumptions - they’re just straightforward. I don’t know how we get it over to students that they should consider the straightforward essays as little gifts from the examiners ! I find it particularly frustrating that we shouldn't direct students to questions, but those who need the most help often choose the hardest questions !

3. Use of the 12 ToK Concepts.  

Most students with whom I worked were not intentionally using, or referring, to the 12 ToK Concepts. Some of the students didn't seem to be aware that there were 12 core ToK Concepts.

If we put them front & centre it helps to improve focus of the essay, Obviously any of the 12 concepts could be applied to any of the essays. So we need to get students to focus in on 2-3 specific concepts.

I try to get students to identify at least 2 concepts at the beginning of the devising process.

4. Questioning the title for Evaluation Points.

Some students didn’t realise that they can develop strong evaluation points by directly challenging the assumptions in the title.

The most obvious examples of this could be:

Essay #2: what can and cannot be explained may not be exclusive and consistent categories. That which can be explained in one context may not fall into cannot be explained in another context. 

And, what we think can be explained today may become ‘cannot be explained’ as we gain new knowledge.

And I also saw this with Essay #4 (“so little knowledge so much power”), where some students didn’t understand that they are meant to challenge Russell’s assertion, that they need to make the argument that we either have a lot of knowledge, or little power, and all possible combinations thereof.

5. Impact of AI, esp ChatGPT.

I started to see content generated by AI coming through, much of this is easy to spot because the AI tends to write with sweeping introductory sections, and uses fairly vague generalisations with lots of hedging words. Obviously we also know it when we see a change in the tone of language used, or the sudden switch to American spelling and grammar.

There’s a lot to say about AI and the ToK Essay, probably in another video, but suffice to say here that currently AI can’t give us anything sufficiently precise to score well on a ToK essay without asking it precise and directed questions. The skills and knowledge required to frame those questions are at least as demanding as just writing the essay yourself. Currently it’s one of those situations in which it is more effort to use the AI than it is just to do it yourself. However, that may change in the coming years. 

On 27th February 2023 IB gave guidance that AI generated content can be used, it should be cited just like any other secondary source.

6. Too much description of RLS. - link back to building the argument.

And finally, we come to our favourite old chestnut - description of the RLS. I still saw lots and lots of description of RLS which was largely unrelated to the PT, or knowledge claims being developed within the PT. This remains the most common problem at the Essay Draft stage in my experience, however I think that the latest Subject Report said that most of the essays submitted are now focussed on the PT, so teachers must be working hard to iron this out before final submission - well done teachers ! 

If you’re a ToK student , and you’re concerned about too much focus on the PT, pick up my e-book How to write the ToK Essay in 6 Easy Steps linked here. The book includes worked examples of how to make an overly descriptive essay more analytical.

So there we have it, reflections on M23 ToK essay session, a bit of learning from the May 23 Essay Session.

I’m looking forward to the Nov 23 titles coming out next week,n Have a great day, stay tok-tastic !

Daniel, Lisbon, March 2023

For other thoughts on ToK Essay:

Why do the best ToK Essays get Mediocre grades?

Unsubstantiated Assertions in The ToK Essay

ToK2022.Net has a good blog linked here

IB's public page on ToK Essay

What is the most important factor in ToK Exhibition ?

A video version of this blog post is available at this link.

The latest ToK Exhibition Exemplars have been published by IB, and we have scrutinised and analysed them to work out what it is they’re looking for so that you can get a great grade in your ToK Exhibition. Today we’re going to tell you what is the most important factor in the ToK Exhibition.

The difference between a good mark and a mediocre mark in the ToK Exhibition mainly rests on one factor, and that one factor is specificity !

Yep, we’ve analysed the IB exemplars, and the examiner’s report, and found that the key factor which moves your marks above 5/10 is specificity.

Let’s look in more detail about what we mean by specificity, we’ll identify 2 main areas of the ToK Exhibition which require specificity.:

Area number 1: Specificity of the object.

Let’s just start with an example: A dictionary is a generic object, however the dictionary that was used to agree a peace treaty between countries A & B in a particular year is a specific object.

The purpose of the ToK Exhibition is for you to explain how ToK is manifested in the world around you, using physical objects. Therefore you need to be able to identify individual objects and say what it is about that object that answers the prompt. 

Another example - A Biology textbook is just a generic object, but your IB Biology textbook which made you question the relationship between mind and matter is a specific object.

A pea plant is a generic object, but the first pea plant that Mendel cross pollinated to test a genetic law is a specific object. 

I don’t need to go on, you get the idea.

The need for a specific object is why some people think that you have to have a personal link to the objects used. This is a misunderstanding. The objects have to be specific, but you do not have to have a personal link to the object. It’s just that if you do have a personal link you are more likely to choose a specific object. However, a specific object is not sufficient to get a good mark, which conveniently takes us onto our second area of specificity.

Specificity of an is defined by IB as “particular context in time and space is identified” (pg 10 Subject Report, May 2022).

Area number 2: Specificity in what the object contributes to the exhibition.

You have to show how each specific object specifically contributes to the exhibition. Let’s look at an example. If I were answering Prompt #1 “What Counts as knowledge ?”, and I identified 3 specific books, and I argued that each book counts as knowledge because they contain facts, I would not be showing specificity in each object’s contribution to the exhibition. However, if my first object was a historical record going back to 1750 of daily air pressure recorded at the Royal Observatory in Greenwich UK, and I argued that this counts as knowledge because sometimes knowledge is recorded but not observed, and my second object was Darwin’s notes from the Beagle, and I argued that this counts as knowledge because knowledge is sometimes observed but not yet labelled, then I would be showing specificity in the object’s contribution to the exhibition.

This is why I believe that it is best if you identify 3 distinct arguments relating to the prompt, 1 argument for each object. You can see this in this Exhibition commentary I gave last year. In this commentary the objects are not specific enough, but the 3 arguments are clear.

The prompt is “Who owns knowledge ?”, my 3 arguments are:

1. Knowledge Producers own knowledge.

2. Knowers own knowledge.

3. Intention (or context) owns knowledge.

Another example is seen in this recent commentary that I gave.

The prompt is “What counts as knowledge ?”. My 3 arguments are:

1. Knowledge is that which has meaning for a restricted community of knowers.

2. Knowledge is that which has meaning for everyone and anyone.

3. Knowledge is that which only has meaning for the individual. 

Ensuring that you clearly explain the knowledge link between that specific object and the prompt is important. That link should be different for each individual object, and it should be a knowledge link, if you want to get a high mark. There are 2 points to bear in mind here:

Firstly, don’t repeat the same link for all 3 objects. The link needs to be different for each individual object.

Secondly, and more importantly, the link needs to be a knowledge link, not a real world context link. Let’s look at another example. If you were answering the prompt “What is the relationship between knowledge and culture?”. Your object may be an auto-rickshaw used in a UK advert promoting tourism to India. The link should not be limited to the observation that the auto-rickshaw is characteristic of transport in India. It needs to go on to make a more generalised knowledge point. Such as individual objects can represent wider bodies of knowledge or meaning particularly when those meanings are associated into a system like a culture. 

So, there we have it - keep it specific, and you will improve your chances of getting high marks on the ToK Exhibition.

Enjoy your ToK learning, stay tok-tastic my friends.
Daniel, Lisbon, Feb 23

Other ToK Exhibition Resources:

Latest ToK Commentary : What counts as knowledge?

What are the ToK Exhibition Examiners thinking?

ToK Exhibition Skills Builder Part 1.

ToK Exhibition Skills Builder Part 2.

ToK Exhibition Commentary - what counts as knowledge ?

Here's the latest attempt at a ToK Exhibition written after the analysis of the latest Exemplars (uploaded to the PRC on 7th Feb 2023), and a re-reading of the May 2023 subject report. I was trying to focus on developing the specificity of the object links, and contributions to the Exhibition.

Prompt #1: What counts as Knowledge ?

My first object is a childhood note from me to my brother written in a code that we devised as children. The code is meant to be a secret language between my brother and I. The note conveys knowledge which is exclusive to the two of us as the code was devised by, and only understood by, the two of us. The note counts as knowledge because it contains meaning which can only be interpreted by a specific community of knowers. Examples of similar knowledge are the designated areas used in the UK Shipping Forecast, code signs used by military or police personnel, or symbolic meaning within a youth subculture like the Mori Kei in Japan . The significance of the exclusivity of such knowledge is that it either evolved, or was devised, for a specific purpose - communicating meaning in an abbreviated, or telegraphed form. The exclusivity of the knowledge could be one of the purposes of its design (as is the case with the code that I devised with my brother), or it could be an unintended function of the knowledge (as is the case with the areas in the shipping forecast). This counts as knowledge because meaning is derived from membership of a specific group of knowers.

The note is included in the exhibition because what counts as knowledge is both contextual and purposeful. Taken out of context our code becomes little more than a set of squiggles on paper. The context of knowledge comprises those who produce the knowledge, those who acquire the knowledge, and the purpose of the knowledge. Without an understanding of context (usually acquired through membership of a group of knowers) the knowledge can be misinterpreted, or even meaningless (ie no longer knowledge). 

However, knowledge which is created to remain exclusive can be understood by knowers beyond the target community if they develop either a deep understanding of the context, the purpose, or tools of deciphering the code. This indicates that what counts as knowledge may not be the content / meaning, but may be the context and purpose of the knowledge community.

The second object is an MRI scan of my left knee, showing a tear in a ligament. The MRI scan was looked at by 2 unrelated medical professionals working in separate hospitals, both medics interpreted the scan in exactly the same way. This image counts as knowledge as it is knowledge produced by a community of knowers who share a standardised method of knowledge production. This community of knowers also share a standardised threshold for what constitutes knowledge. However, what sets this knowledge apart from Object 1 is that this knowledge is designed to be understood beyond it’s originating community of knowers. The MRI scan is knowledge produced using the scientific method, as such it counts as knowledge because of its method of production, its objectivity, reliability, validity and universality. As such the MRI scan is knowledge based on facts.

The MRI scan is included in the Exhibition because some knowledge is not based on opinion, nor meaning which is solely specific to a community of knowers. Such knowledge is produced using methods which are designed for universality across community of knowers, and replication for the purposes of validation, Eg The scientific method. The generalisability, and aspiration for universality, of such knowledge is why this counts as knowledge. This is knowledge which has standardised meaning regardless of context, the knower, nor the purpose of the use of the knowledge. Human and Natural Scientific knowledge should be understood, and interpreted, in the same way regardless of the context of the knower. This is particularly important when we are considering knowledge pertaining to the organisation and maintenance of human life such as engineering, medical and economic knowledge.

However, the objectivity of such knowledge may detract from the individualised experience of the knower. Whilst two MRI scans may show the same condition in two separate people the individuals may experience that condition in very different ways. Knowledge which is designed for objective generalisability runs the risk of losing the meaning of that knowledge which is specific to the individual knower.  

The third object is a ticket from a music concert that I attended in May 2022. At the concert I experienced strong emotions of elation, freedom and near transcendence. This was a ‘peak experience’, the closest that I have come to a sublime state. The ticket counts as knowledge as an experience which was the most truthful that I have experienced, but could not be externally validated, nor necessarily shared with other knowers.

Internal knowledge, sometimes called self knowledge represents a form of internal truth. This counts as knowledge to the knower as it can be a very strong form of knowledge.

However, it is not necessarily known to other knowers, nor is it necessarily validated or even agreed by others. As such the ticket is included in this Exhibition because it represents a wholly individual form of knowledge unlike objects 1 and 2. Such internal knowledge can include emotions, experiences, intuition and memories. The internal nature of such knowledge can be difficult to communicate to other knowers, making external validation even more difficult. However, such internal knowledge can have significant influence on the interpretation, explanation and perspectives that knowers form regarding new externally produced knowledge. For example, I will now be more receptive to knowledge which is aligned with the musicians who played at the concert (eg adverts using music from the same band). 

Arguably values are a form of such internal knowledge, they may have external labels but are experienced at a near internal level. As values are the basis of interpretation, and perspectives the consideration of such internal knowledge is important if we are to understand what counts as knowledge. Forms of knowledge production designed for a wider community of knowers are influenced by the values and perspectives of the knowledge producer. For example, bio-chemists have to decide which disease to study when formulating treatments, this initial decision can be influenced by values. 

Therefore, it is argued that internal, unvalidated, knower specific knowledge (aka “Truth”) is the basis of all knowledge, and therefore is the essence of what counts as knowledge.

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I have many more ToK Exhibition Resources, including:

ToK Exhibition What do we know in Feb 2023?

ToK Exhibition Skills Builder Part 1

ToK Exhibition Skills Builder Part 2

What are the ToK Exhibition Examiners thinking?

Daniel, Lisbon, Feb 23

ToK Exhibition - what do we know Feb 23?

In Feb 2023 IB posted 10 new Exemplars of ToK Exhibitions. I've closely analysed them to see if we can learn anything new from them, or if they further confirm things that we already know. I have summarised the main findings in the table linked below.

What we Know - Exhibition Feb 23

3 key findings:

1. Specificity of objects, and specificity of knowledge links between the objects & the IA prompt are key to doing well.

2. Three perspectives / arguments is key to developing the specificity required in the link and justification for inclusion of the object.

3. There's still some ambiguity as to whether objects symbolising knowledge / meaning / culture are allowed.

There are many other findings, clarifications and confirmations in the document - check it out.

If you have any thoughts, or corrections, I'd love to hear them in the comments below.

Have a great day,
Daniel, Lisbon, Feb 23

Further information on The ToK Exhibition can be found at:

What are the examiners thinking?

Ethics & Technology.

When thinking about the possible ethical issues arising from technology in a ToK context I start from the perspective of ethics rather than technology. In doing so I will be asking 4 big questions:

• What are the potential ethical issues arising from technology?

• Where do these ethics and technology issues sit on a ‘good-bad’ continuum?

• What are the implications of these ethics & technology issues for the construction and acquisition of knowledge?

• How might these ethics & technology issues evolve in the future?

What are the potential ethical issues arising from technology?

We tend to think of ethical issues arising from technology in modern terms, ie issues arising from modern digital technologies. However there have been ethical issues arising from technology since humans first started to utilise technology. In the modern age such issues rose to greatest prominence during the first era of industrialisation in the 19th Century.

I will look at ethical issues arising from technology through 4 main lenses / perspectives. Each perspective has both positive and negative ethical consequences within it.

4 lenses that have ethical consequences arising from the application of technology:

• Accessibility

• Health, productivity and fulfilment.

• Diversification - Homogenisation

• Privacy - Autonomy - Control

Accessibility.

Positive ethical consequences of improved access afforded by technology.

Technology both increases and decreases accessibility across a wide range of fields, and in multiple ways. A starting point to think about this is that technology should increase our capacity to manipulate the environment. The outcomes of such manipulation in terms of accessibility will reflect the value basis of those in charge of the technology. Whether this is a negative, or positive, ethical outcome will also largely depend on the value basis of those making the judgement. Let’s take the application of technology in a mediaeval village as a real world example. The application of technology to crop production (eg ploughing, irrigation, crop rotation etc) increases the yield of production. Thus increasing access to food, and the time available to residents to access other ways to spend their time. There are subsequent consequent improved access improvements such as access to healthcare, culture, further technological innovation etc.

The most obvious improvements in access realised by modern technologies are access to knowledge acquisition and knowledge production. The development of public libraries, public education, and the internet have significantly improved the access that people have to knowledge acquisition. The development of universities, and more recently Web 2.0, significantly improves the access that people have to knowledge construction.

Schutz: The Social Distribution of Knowledge.

The positive ethical benefits of such improved access are wide ranging, and potentially profound. The expansion of human and civil rights seen in many areas of the world can be understood as being augmented by the improved access to knowledge realised by technology. In his article The Well- Informed Citizen: An Essay on the Social Distribution of Knowledge Alfred Schutz explains how improved access to knowledge changes the classification of an elite ‘expert’ group who are afforded the right to produce ‘socially approved’ knowledge (that is knowledge afforded prestige and influence). Technology allows more people to join this group, adding a more diverse set of perspectives to socially approved knowledge. Schulz uses the models of social identity theory from Social Psychology to understand the possibly liberating effects that wider knowledge access can have for individuals in breaking down in group-out group stratification. Further, Schulz explains how wider access to knowledge increases people’s opportunities to question taken for granted assumptions, and therefore increasing the potential to develop ‘better’ knowledge, ideas that have built upon, and evolved from that which is pre-existing.

Friedman: The World is Flat.

Schutz rather prophetically wrote his article in 1946, many of his ideas were updated in 2005 by Thomas Friedman in his book The World is Flat: A Brief History of the Twenty First Century. Friedman describes how technology (mainly Web 2.0 tech) has increased access to knowledge acquisition and construction which, in turn, has enabled those in developing countries to compete financially, technologically, and culturally with those in the developed world. Here we see the argument that access afforded by technology has significant positive ethical consequences. 

Schutz rather prophetically wrote his article in 1946, many of his ideas were updated in 2005 by Thomas Friedman in his book The World is Flat: A Brief History of the Twenty First Century. Friedman describes how technology (mainly Web 2.0 tech) has increased access to knowledge acquisition and construction which, in turn, has enabled those in developing countries to compete financially, technologically, and culturally with those in the developed world. Here we see the argument that access afforded by technology has significant positive ethical consequences. 

Negative ethical consequences of improved access afforded by technology.

The most common concern raised with the increased access that technology allows is applied to digital technologies - the digital divide. We will come to that later in this section, but let’s first take a step back and consider the wider knowledge implications of increased access to technology.

The argument for the positive ethical benefits of increased access to technology is essentially that more of things (knowledge & its consequences) is a good thing. However, this doesn’t account for the context within which this change occurs, specifically the values and purpose of the wider society. That increased access will have outcomes which are, arguably, largely shaped by the value structures and power relationships of the wider society within which they operate. Namely, the value structure of the wider society. If the value structure is one in which power is used to restrict  the freedoms, or privacy, of the individual then this could be reflected in the wider access to knowledge afforded by technology. Not only does the mass populace have more access to knowledge, but powerful actors now have more access to the thoughts and behaviours of the population. We will come back to this in the section on privacy.

The concept of ‘more’ isn’t necessarily a positive ethical outcome, let’s go back to our mediaeval village. After the application of agricultural technology the people have more time to do things other than farming. However, if there’s a power structure which can direct how people spend their time (eg a Lord of the Manor) this extra time could be used for negative ethical outcomes such as warfare, environmental destruction etc.

The Digital Divide.

The digital divide refers to the unequal distribution of technology and internet access between different groups of people globally.  The divide creates a gap between those who have access to the knowledge resources opened up by the internet and those who do not, leading to unequal opportunities for growth, development, and success. For example Northern Europe has an ‘internet penetration’ (people who have access to the internet) of 95% whilst Africa has an ‘internet penetration’ of approximately 40% (“List of countries by number of Internet users”)

The digital divide affects individuals from different regions, socio-economic backgrounds, age groups, and cultures. People who live in rural areas, low-income communities, and developing countries often lack access to technology and the internet. This limits their opportunities for education, job training, and accessing information, which can lead to lower wages and limited opportunities for social and economic mobility.

The digital divide also affects businesses, as those without access to technology may struggle to compete in the global market. Moreover, it perpetuates existing inequalities, such as gender and race, as marginalised communities are often the ones who lack access to technology and internet.

The Digital Divide is an example of technology widening the gap of power between those with and without access to the technology. This is a recurring theme that we will see arising from many different types of technology across history.

Health, productivity and fulfilment.

I am rather uncertain about including a section on the applied ethical effects of technology as I run into danger of describing the real world ethical effects of technology rather than focussing on the ethical effects of technology on knowledge acquisition, construction and interpretation. However, as this is somewhat a false division, and students need to draw upon real world examples for both ToK assessments, I’ll go ahead and include this section anyway.

At prima facie level the immediate effect of the application of technology in area of life should be to increase productivity - this applies equally to the production of knowledge as it does to the production of ceramic vases, cars or pizzas. This increase in productivity has significant ethical effects. There is a long tradition of writers (eg sociologists, artists, economists etc) describing the negative effects of technology in the workplace. There is a significant body of research on the alienation and deindividuation experienced in the industrialised workplace. In his paper on Technology and Human Relations Carleton Coone argues that increased application of technology requires a more pressing focus on designing for human relations if we are to avoid the negative effects of that technology (such as alienation). Students can review the paper (referenced below) for real world examples.

On the other hand, Jon Shepard, in his 1973 research in the oil industry, found that the application of technology did not inevitably lead to powerlessness and alienation. This finding was underpinned by two main processes (i) technology increases productivity, and therefore increases the free time that workers have to spend doing things that they find intrinsically fulfilling. (ii) that increased specialisation of roles afforded by technology can actually increase the autonomy and personal involvement that a worker has in their job.

The ethical consequences of medical technology.

The positive ethical benefits of the application of technology in medical sciences are both obvious, and seem to be indisputable. Students and teachers can draw from a vast range of real world examples including the development of vaccine technology, the application of medical imaging technology, the application of pharmaceutical technology etc. However, there are, arguably, some negative ethical consequences of the application of technology in the medical sciences. The application of technology to medical knowledge has the greatest possible ethical consequence - that of maintaining, prolonging or ending life itself. As such the responsibility of the practitioner, and the accountability of those practitioners, is brought to the forefront. The ethical demands upon practitioners is greatly increased by the increased efficacy of the technology.

Peter Singer (2000) identifies 5 areas in which the ethical considerations have greatly increased in recent years:

• Quality of end of life care.

• Tavistock Principles to improve medical error.

• Prioritisation and access to resources.

• Stem Cell Research.

• E-health and bio-global ethics.

I include these here to demonstrate the nuanced range of ethics and technology issues arising from the application of technology in healthcare and medicine. If ToK students want to draw real life examples from these 5 areas they could follow the article referenced below, or search JSTOR for research related to the area of their choosing.

Ethical concerns of medical technology.

In his article Too Much Technology Bjorn Hofmann argues that the overuse of, and over-reliance on,  technology is starting to have negative ethical consequences for the application of medical knowledge. He argues that the application of medical technology can actually be more harmful than positive, draws funding and resources away from functions which could have more positive effects, reduces efficiency, and leads to overdiagnosis. This last point is of direct relevance in ToK, and requires a little more examination.

Technology enables us to better identify disease and the causes of illness, further it helps us to better understand the interpolation of causal factors. As such it can allow us to develop new categories, and classifications, of medical conditions and illness. Therefore, the application of technology actually increases our construction of knowledge. Technology allows us to create more knowledge. An example of this from medical sciences is the recent development of a machine learning model which can predict rare diseases, even when these diseases aren’t represented in the data sample used (Singh).

Consequences

A consequence of improved medical technology is obviously increased human lifespan, which also has ethical consequences. The ethics of increasing human population on resource allocation and competition are well rehearsed, and easy to find sources for. As such I won’t detail them here. However, of direct relevance to ToK is the consequent increase in non-working (leisure) time, particularly the effects of the 4th industrial revolution with the application of artificial intelligence and machine learning.  Jandrić, Petar, and Sarah Hayes (2020) look at the range of arguments whether the 4th industrial revolution will result in fewer jobs (“technological unemployment”) and more leisure time, or lead to the creation of a whole new range of jobs (as has happened in previous periods of technological innovation). Both scenarios will lead to new forms of knowledge production, acquisition, and interpretation as they further shape, and redefine, the human-machine interaction.

Further references to the work of Heidegger here will be useful for those who wish to follow this line of argument. There are ancillary ethics and technology issues in terms of the varied access to these forms of technology, and the consequent implications for power distribution, and life fulfilment. 

Diversification - Homogenisation

Of more direct relevance to our Theory of Knowledge is the effect of technology on the type of knowledge produced, and the distribution of that knowledge. These arguments are most accessible to us in 2023 by considering the rise of digital technologies in the last 40 years, however the same principles apply to all previous forms of knowledge technology.

The oft cited promise during the mass adoption of the internet, and world wide web, in the mid 1990s was that many more people would have access to far more information faster and cheaper than ever before. Even taking into account the inequalities of The Digital Divide (discussed earlier in this article) this promise of mass access to mass information appeared to be accurate - this is the increased acquisition and interpretation of knowledge afforded by new technologies. The dawn of Web 2.0 in the early 2000s promised that many more people would be able to produce / construct knowledge through self publishing in written, visual, music etc form. Again, the rise of sites such as YouTube, Wordpress and TikTok would seem to have borne this promise out.

Concerns.

However, many writers have taken a more critical approach  to this apparent diversification in the production, acquisition and interpretation of knowledge. Kumar Sashi (2011) uses a Gramscian framework to understand the operational processes and effects of digital technology on knowledge production and acquisition. His argument is that digital platforms operate like conventional markets, making the knower a consumer, and producer’s effectiveness is determined by their power. As such production of, and exposure to, agglomerates to the most powerful actors (knowers). The effect of power based agglomeration is the homogenisation of knowledge. Rather than horizontal demarcation of knowledge producers we have vertical integration and monopolisation of producers. This process is exacerbated by internal promotion systems (such as Google search algorithms, or YouTube’s “Like” algorithms). This is even further exacerbated by globalising forces which enable knowledge producers to transcend national boundaries, time zones and localised practices.

The ethical consequences of homogenisation.

The ethical consequences of such homogenisation of knowledge production and acquisition are equity and accessibility issues (as touched on earlier). There will be amplification of certain knowledge producers (and their content) beyond their real world organic functional niche. As such, the homogenisation of knowledge production and acquisition means that a narrower range of people’s content will be seen, causing an unequal distribution of influence in the social and political spheres - some people have more influence in political processes than others (links to the rise of populist movements can be made here, there is substantial literature on eg the rise of Trump, Bolsonaro, Black Lives Matters Movement etc. Students who take DP Global Politics will be able to make clear links to their units on social movements in politics). 

Akin Unver, in the paper Politics of Automation, Attention and Engagement, argues that digital media platforms (eg Twitter, Tik Tok, etc) have become “political governance systems”. They allow both politicians and the electorate to bypass the traditional institutional gatekeepers such as the established media, institutions (eg Parliament), and opinion polling systems to communicate more directly with each other. Professor Unver explains the commodification of user attention, the rentier economic model of the private owned for profit platforms, and the effects of content control being moved beyond the traditional nation state. It is argued that this can lead to new forms of political ideology / techno-politico expression such as “cyber communism”, “networked feudalism”, and “Authoritarianism 2.0 and 3.0”. However, he argues that digital platforms are an imperfect democratic space which are playing the role of “saving democracy” in the new digital age. Their primary advantage over older forms of democracy is that they give users ‘sovereignty’ over their data and political voice.

Privacy - Autonomy - Control

There are well known, and widely discussed, concerns over the potential threats to the right to privacy raised by digital technology. The sharing of various forms of data with corporations and governments is an obvious consequence of digitalisation. However, similar concerns regarding  privacy also apply to earlier forms of technology. An example could be the invention of the camera in the 19th century meant that people’s public behaviours could now be permanently recorded. As the discussions regarding privacy are well rehearsed elsewhere I won’t focus on them here, rather I will look at the effects of technology on individual autonomy, and on social control.

Most technology shapes the behaviours that individuals are undertaking, this is especially true in the workplace. Probably best exemplified by the advent of the production line during the 19th century industrial revolution in which people were often employed to carry out a small range of tasks with little or no latitude for variation. The increase in control over our environment afforded by technology somewhat paradoxically leads to a decrease in our freedom of choice of behaviours (eg hunter gatherers used a fire for warmth and protection at night, but now they are tied to staying near the fire as it provides so much more warmth and protection than any other resources available at that time). As such it can be argued that technology decreases our physical autonomy. 

The ethics of the production line.

We can develop the physical autonomy argument further when we consider the role of control afforded by technology. The production line controls the pace of the individual’s work, and some corporations require their workers to wear tracking devices so that they know where they are at all times, etc . The examples are myriad. However, for our theory of knowledge we need to consider whether such control can also be applied to knowledge and thinking. In this we can refer back to the section re. Homogenisation and selection of messages in digital media earlier this article. We can apply the same models to earlier technologies - eg book publishers, religious organisations, even theatre producers have all been powerful gatekeepers during earlier stages of history. The important emphasis of the argument here is that technology amplifies their control.

Biotechnology and autonomy.

Further, the ethics and technology issues pertaining to autonomy and control are further exacerbated when we consider medical technology, particularly recent developments in bio-technologies. Some companies have been making employees wear fitness trackers for the past few years. The reasons given are the health benefits, but this also, undoubtedly encroaches on the individual’s privacy, and potentially their autonomy. There is now increased used of internal biological monitoring systems (eg monitoring cardio-vascular metrics), again the positive ethical benefits of health come with concerns over possible infringements of autonomy and control.

A further concern for autonomy and control arises from the increasing use of artificial intelligence and machine learning (see earlier post on AI). Artificial Intelligence is increasingly performing processes and functions that were previously within human control. Whilst the initial coding of the AI may determine how it performs those functions, machine learning enables the AI to learn, and adapt, it’s processes. As such, many argue that AI is reducing our autonomy over the functions that we give it (this is inherently the very point of AI). The ethical issues arising from AI include all of the above (autonomy, control, privacy, productivity, democratic, access). 

Conclusions.

This article is an overview of some of the ethical issues potentially arising from the application of technology for ToK students studying the Knowledge & Technology optional unit. The article doesn’t intend to look in depth at the particular ethical issues, this would be better achieved by students who have chosen to investigate further those issues. In summary we have broadly described that whilst technology may bring positive ethical outcomes the nature of its application can also lead to some ethical concerns.

Daniel, Lisbon, Feb 2023

Other posts on technology in this series:

We need to talk about Pune India.

What is Technology?

How does Technology change the pursuit of knowledge?

Does my thermostat have feelings? (Artificial Intelligence)

Bibliography

• Coon, Carleton S. “Technology and Human Relations.” Proceedings of the American Academy of Arts and Sciences, vol. 75, no. 1, 1942, pp. 23–27. JSTOR, https://doi.org/10.2307/20023443. Accessed 7 Feb. 2023.

• Friedman, Thomas L. The World Is Flat: A Brief History of the Twenty-First Century. New York, Farrar, Straus and Giroux, 2005.

• Hofmann, Bjørn Morten. “Too Much Technology.” BMJ: British Medical Journal, vol. 350, 2015. JSTOR, https://www.jstor.org/stable/26518356. Accessed 7 Feb. 2023.

• Jandrić, Petar, and Sarah Hayes. “Technological Unemployment and Its Educational Discontents.” The Digital Age and Its Discontents: Critical Reflections in Education, edited by Matteo Stocchetti, Helsinki University Press, 2020, pp. 161–82. JSTOR, https://doi.org/10.2307/j.ctv16c9hdw.13. Accessed 7 Feb. 2023.

• Kass, Leon R. “Ageless Bodies, Happy Souls: Biotechnology and the Pursuit of Perfection.” The New Atlantis, no. 1, 2003, pp. 9–28. JSTOR, http://www.jstor.org/stable/43152849. Accessed 8 Feb. 2023.

• KUMAR, SASHI. “Hegemony in Contemporary Culture and Media and the Need for a Counter Initiative.” Economic and Political Weekly, vol. 46, no. 51, 2011, pp. 38–43. JSTOR, http://www.jstor.org/stable/23065547. Accessed 7 Feb. 2023.

• SCHÜTZ, ALFRED. “THE WELL-INFORMED CITIZEN: An Essay on the Social Distribution of Knowledge.” Social Research, vol. 13, no. 4, 1946, pp. 463–78. JSTOR, http://www.jstor.org/stable/40958880. Accessed 3 Feb. 2023.

• Shepard, Jon M. “Technology, Division of Labor, and Alienation.” The Pacific Sociological Review, vol. 16, no. 1, 1973, pp. 61–88. JSTOR, https://doi.org/10.2307/1388654. Accessed 7 Feb. 2023.

• Singh, Niharika. “Stanford Researchers Developed a Machine Learning Model Called POPDx to Predict Rare Diseases, Including Diseases That Aren’t Present in the Training Data.” MarkTechPost, 6 Feb. 2023, www.marktechpost.com/2023/02/06/stanford-researchers-developed-a-machine-learning-model-called-popdx-to-predict-rare-diseases-including-diseases-that-arent-present-in-the-training-data/. Accessed 7 Feb. 2023.

• Singer, Peter A. “Recent Advances: Medical Ethics.” BMJ: British Medical Journal, vol. 321, no. 7256, 2000, pp. 282–85. JSTOR, http://www.jstor.org/stable/25225223. Accessed 7 Feb. 2023.

• Unver, H. Akin. “DIGITAL CHALLENGES TO DEMOCRACY: POLITICS OF AUTOMATION, ATTENTION, AND ENGAGEMENT.” Journal of International Affairs, vol. 71, no. 1, 2017, pp. 127–46. JSTOR, https://www.jstor.org/stable/26494368. Accessed 8 Feb. 2023.

• “List of countries by number of Internet users.” Wikipedia, https://en.wikipedia.org/wiki/List_of_countries_by_number_of_Internet_users. Accessed 3 February 2023.

Does my thermostat have feelings?

in other words : Does Artificial Intelligence 'Know' in the same way that humans know ?

Starting Points.

Artificial Intelligence (AI) has made remarkable progress in recent years, but it still remains an open question whether AI can know in a similar way to humans. The philosophical debate about AI and its capacity for knowledge has been ongoing for decades, with some arguing that AI can truly know, while others claim that AI lacks the necessary cognitive faculties.

The first person to use the phrase “Artificial Intelligence” was mathematician and computer scientist John McCarthy at The Dartmouth Conference of 1956.In 1979 McCarthy wrote an insightful, and prophetic, paper directly addressing the relationship between AI and human knowledge titled “Ascribing Mental Qualities to Machines”. This blog will draw upon McCarthy’s paper which seems more pertinent today than ever, whilst also offering alternative perspectives.

Ascribing Mental Qualities to Machines

John McCarthy's 1979 paper "Ascribing Mental Qualities to Machines" discusses the idea of machines having mental qualities, such as beliefs and desires. McCarthy argued that machines can have mental states and that it is possible to describe them using a system of mental attribution. He proposes that mental attribution should be based on the machine's behaviour and its goals, rather than its physical structure. The paper highlights the importance of considering the relationship between mental states and physical systems in artificial intelligence research.

McCarthy explains that the "beliefs" that a thermostat may be said to have relate to its operational functions ie to calculate whether it should switch the heater on to make a room warmer, or whether to switch the heater off to make a room colder. McCarthy further explains that the thermostat could not be said to have "beliefs about beliefs" ie, it doesn't have beliefs about what temperature the room should be, or whether it believes its beliefs. It certainly doesn't have beliefs outside of its operational (programmed) sphere such as who won the Battle of Waterloo.

Download Ascribing Mental Qualities to Machines

Some common criticisms of McCarthy’s position (as outlined in his 1979 paper) include the problems of defining mental states and attributing them to machines, the difficulty in creating a systematic method for mental attribution, and the concern that attributing mental states to machines may lead to anthropomorphizing them and losing sight of their true nature as mechanical systems.

Daniel Dennett’s critique of McCarthy

Daniel Dennett, philosopher and cognitive scientist,  has written extensively on the topic of artificial intelligence and the idea of ascribing mental qualities to machines. Dennett has been critical of the idea that machines can have mental states, and he has put forward the argument that mental states are inherently subjective and cannot be reduced to purely physical or computational processes.

In his book "Consciousness Explained", Dennett argues that consciousness is an emergent property of complex systems, and that it is not possible to ascribe mental states to machines in the same way that we ascribe mental states to people. He proposes that the idea of mental states in machines is a form of anthropomorphism, and that it is more productive to focus on understanding the computational processes that underlie machine behaviour, rather than attributing mental qualities to them.

Dennett's views on this issue are significant because they challenge the idea that machines can have mental states and provide an alternative perspective on the relationship between mental states and physical systems in artificial intelligence.

Knowledge as Justified True Belief.

Questions on Artificial Intelligence in ToK inevitably start with the question of what knowledge is, and what it means to know. Many students are attracted to Gettier’s definition of knowledge as “Justified true belief”. Obviously, this definition has a number of implications for our studies in ToK. However, it's a useful place to start exploring the question of whether AI knows in the same way that humans know.

From a philosophical perspective, the concept of knowledge requires the existence of beliefs and understanding, as well as the ability to justify those beliefs through reasoning and evidence. Some argue that AI, with its vast amount of data and sophisticated algorithms, can possess knowledge in this sense. However, others argue that AI lacks the consciousness, introspection, and self-awareness that are necessary for true knowledge.

It's difficult to be rigorous about whether a machine really 'knows', 'thinks', etc., because we're hard put to define these things. We understand human mental processes only slightly better than a fish understands swimming.

John McCarthy "The Little Thoughts of Thinking Machines", Psychology Today, December 1983, pp. 46–49. Reprinted in Formalizing Common Sense: Papers By John McCarthy, 1990, ISBN 0893915351

Characteristics of Knowing

(from McCarthy’s article The little thoughts of thinking machines):

• Intention

• Tries

• Likes & Dislikes

• Self conscious.

Despite being written over 40 yrs ago McCarthy’s work is highly relevant today, and very easily accessible. (I strongly recommend both articles cited in this blog). His 4 characteristics of knowing provide a practicable starting point for ToK students who are exploring questions on AI and knowing. Whilst, prima facie, his characteristics may seem rather reductionist they are both pertinent to human and machine knowledge as they encompass goals, trial and error, reward and reflection.

Belief.

The difference between human belief and AI algorithms lies in the nature of their cognitive processes and the sources of their knowledge.

Human beliefs are shaped by a combination of personal experiences, cultural influences, emotions, and reasoning. They are based on a subjective understanding of the world and can change over time as new information becomes available.

In contrast, AI algorithms are based on objective rules and mathematical models that process data in a systematic and impartial way. They make decisions based on patterns in the data they have been trained on and do not have personal experiences or emotions that can influence their beliefs.

Furthermore, human beliefs often rely on intuition, speculation, and assumptions that may not be grounded in evidence, while AI algorithms can only make decisions based on the data they have been trained on and the rules they have been programmed with.

Machines as simple as thermostats can be said to have beliefs, and having beliefs seems to be a characteristic of most machines capable of problem solving performance. However, the machines mankind has so far found it useful to construct rarely have beliefs about beliefs, although such beliefs will be needed by computer programs that reason about what knowledge they lack and where to get it. Mental qualities peculiar to human-like motivational structures , such as love and hate, will not be required for intelligent behavior, but we could probably program computers to exhibit them if we wanted to, because our common sense notions about them translate readily into certain program and data structures. Still other mental qualities, e.g. humor and appreciation of beauty, seem much harder to model.

John McCarthy, "History of Lisp," 12 February 1979; republished at www-formal.stanford.edu.

Descartes on Machines & thinking.

René Descartes, French philosopher and mathematician, was one of the earliest thinkers to address the question of whether machines could think. Descartes believed that the mind and the body were separate entities, and that the mind was capable of thinking, reasoning, and having experiences, while the body was merely a physical machine.

In "Meditations on First Philosophy", Descartes argued that the mind was a non-physical substance that was distinct from the physical body. He believed that the mind was capable of independent thought and consciousness, and that it was not possible for a machine, no matter how sophisticated, to have these same qualities.

Descartes' views on this issue were influential in shaping the philosophical debates about the nature of consciousness and the relationship between mental states and physical systems. Although his views have been widely criticized and challenged in the centuries since they were first articulated, they remain an important part of the philosophical discourse on the topic of artificial intelligence and the possibility of machines having mental states.

In summary, the difference between human belief and AI algorithms lies in their sources of knowledge, their cognitive processes, and the nature of their decision-making. While AI algorithms can provide a highly accurate and objective approach to decision-making, they lack the subjective and personal aspects of human beliefs.

In terms of academic research, there is a growing body of evidence that AI can acquire knowledge through machine learning and other methods. For example, deep learning algorithms have been shown to successfully identify objects in images and perform other tasks that require knowledge of the world. However, there is still much work to be done in order to determine the limits of AI's knowledge acquisition and whether it can truly know in the same way as humans.

Consciousness.

Can artificial intelligence be said to have consciousness ?

Obviously, in order to answer the question whether AI can be said to have consciousness we need to consider different perspectives on what constitutes consciousness.

Many writers have attempted to describe the different characteristics which could be said to constitute consciousness. A useful writer in this area is David Chalmers (1995) who made the distinction between the easy problems of consciousness (characteristics which can be described by neurology and cognitive science), and  “The hard problems” (more process based, or seemingly diaphanous characteristics);

the “easy problems” he describes include:

• The focus of attention.

• Assimilation of new knowledge into a knowledge system.

• The communication of mental states.

Chalmers' 'easy problems' are a useful list for ToK students as they provide a model which can be easily used to compare AI with humans. We can identify both humans and AI executing, solving and displaying these processes/problems. However, are they 'the same' in both humans and AI ? Obviously to answer such a question we would have to consider what we mean by 'the same', but we could also introduce the notion of qualitative differences at this point. Those arguing for a more distinct difference between humans and AI might argue that whilst we can apply the same labels to processes in both entities (humans and AI) there is a qualitative difference in the ways in which those processes are realised and experienced. This (conveniently) takes us onto the issue of consciousness.

What is Consciousness ?

In this blog I only seek to answer the question on the nature of consciousness in order to help us to answer the question of whether AI knows in a similar way to the way in which humans know. A comprehensive discussion on what consciousness is would require writing a very long, and complicated, book - which is not the purpose of this blog !

Some of the main philosophical theories of human consciousness include:

1. Dualism: This theory, first proposed by René Descartes, holds that consciousness is a non-physical substance that exists separate from the physical body. According to dualism, the mind and body interact, but they are distinct entities.

2. Physicalism: This theory, as put forward by J.J.C. Smart, D.M. Armstrong, and Paul Churchland, proposes that consciousness is a byproduct of physical processes in the brain, and that there is no need to postulate a separate, non-physical substance to explain it. Physicalism is sometimes referred to as materialism or reductionism.

3. Idealism has been developed by writers including George Berkeley, Immanuel Kant, and Georg Wilhelm Friedrich Hegel. This theory holds that consciousness is the primary reality, and that everything else, including the physical world, is derived from consciousness.

4. Emergentism takes a more systems based approach to the problem of consciousness. Developed by writers such as Samuel Alexander, C.D. Broad, and J.C. Smart. This theory suggests that consciousness emerges from complex interactions between physical processes, but that once it emerges, it cannot be reduced to these underlying processes.

It's worth noting that these theories have been developed and modified over time by many philosophers, and that the list of proponents for each theory is not exhaustive. 

How do these theories relate to Artificial Intelligence ?

Human & Machine Consciousness (David Gomez)

Dovid Gomez in his article Human & Machine Consciousness gives us some useful ways in which to approach the question of whether AI can achieve consciousness. The first distinction that Gomez makes is that clearly we can build AI which exhibits human conscious behaviours such as writing essays, driving cars and playing computer games. However, most commentators would agree that an external behaviour does not, in itself, constitute consciousness. For example, we often do things “without thinking”, ie we have not directed targeted consciousness towards our own behaviours. 

Further, we can even build AI which is modelled on human consciousness, that will display conscious-like behaviours, and may even display evidence of self awareness. However, this does not necessarily mean that the AI is experiencing consciousness,

To paraphrase Mr Gomez:

A model of a river looks like a river, tells us about the form of a river, even about the functions of a river, but it is not necessarily wet.

Here we continue to see some of the distinctions between how and why questions, and necessary and sufficient conditions. To understand the function (how) of a river it is not necessary to make a river, to simulate a river, nor to even be like a river. Could this be the same with consciousness?

Further, Mr Gomez argues that until we understand the relationship between human consciousness & the physical world we will not be able to simulate consciousness in artificial systems. This challenge presents itself even before we attempt to demarcate the necessary and sufficient conditions of consciousness, and the functions and causes of consciousness.

In many ways we could argue that Mr Gomez is drawing upon a more physicalist tradition in thinking about consciousness. He posits AI as outcomes which are, to a degree, dependent upon the physical processes and outcomes of human consciousness.

Free Will.

Consciousness, for most people, requires a degree of free will. Or at least free will constitutes a desirable component of consciousness. However, Nahmias et al in their paper Can AI have Free Will ? challenge this apparent link between consciousness and free will. Both consciousness and free will are context dependent, we could easily be in a state in which we are highly aware (conscious) of our lack of free will etc. As such, they argue, that free will is not a necessary, nor even sufficient, condition for AI to be said to be self-aware, or conscious. This argument is a strong counterargument against the “AI achieves Singularity and takes over the world” scenarios so prevalent in popular fiction.

In this (very brief) discussion of free will and AI we raise some of the issues arising from the Dualist and Idealist theories of consciousness. If consciousness is tenuously related to the physical structures within which it occurs, or from whence it is produced, then it could be expressed as being ‘beyond’ the limitations of those structures. This discussion brings into sharp focus the question of the purpose of consciousness, and free will, in an AI system.

Imagination.

Most people view imagination as being central to our human consciousness, and often posited as a necessary condition of our free will. Critics of the notion that AI can achieve consciousness often cite the difficulty of simulating imagination as evidence that AI cannot become conscious. Hilary McLellan, in her work AI & Imagination, takes a Systems Theory approach rather than a functionalist atomistic approach to the issue of imagination. In systems theory the emphasis is placed on the processes and interactions between the components of the system rather than on the components themselves. In these processes and interactions we see the product or output. The processes work in a synergistic manner in which the outcomes are greater than the sum of the products of the individual components. As such the product of the AI is greater than the sum of the outcomes of the individual units of coding.

Ms McLellan situates AI in the symbiotic relationship between culture and technology, culture changes technology and technology changes culture. In such an environment adaptation and evolution have low context relevancy (adaptation happens as context changes), however stability has very high context relevancy (the organism, or machine, can only remain stable if the context remains stable). This observation helps us to start to answer the question why AI would need imagination ?, and why might AI need imagination to attain consciousness ? 

In evolutionary psychology imagination is often seen to be a survival function allowing us to predict threats, prepare for, and take actions to avoid such threats. If we see imagination as a necessary condition for AI to be said to be conscious we will need to consider whether AI has the same needs for threat protection ?

AI, Context & Consciousness.

Both Heidegger and Dreyfuss discussed the importance of context when considering whether AI knows in a similar way to humans. Heidegger, in explaining the Daesin, described how we achieve many things because of the way in which the world works rather than because of our knowledge of how things explicitly work. For example, we can achieve flying from one country to another by getting on a plane, we don’t actually have to know how a jet engine works to achieve this goal.

All of our human behaviours are highly context specific, we are consciously aware, and subconsciously sense, the immense range of factors constituting context. Dreyfuss called this immensely complex context “background”. Both writers argued that it is very difficult, nigh impossible, to code for / artificially simulate context. Heidegger argued that this difficulty is compounded because we have very limited understanding of our human contexts, the influences on, and experiences of those contexts.

Dreyfuss further developed Heidegger’s Daesin in his discussion on the unconscious mental processes which give us a sense of context, and in turn allow us to be consciously aware of our position and intention in context. Contextual understanding and contextually appropriate conscious thought and behaviour requires the corralling of a seemingly infinite number of facts. We have to select this huge number of facts from an even greater number of facts, in order to make correct selections we need rules about contextual based selection. These rules are, in turn, underpinned with even more facts about the selection rules. As such we see a mutually inclusive relationship between facts, rules, selection and context. Both writers argued that such endless possibilities are nigh impossible to code for. 

Critics of Heidegger and Dreyfuss level two key critiques at this argument. Firstly, AI is only required to operate within a very narrow band / confines of contexts. AI doesn’t need to understand the wide range of human contexts that a human may encounter. AI which is developed to identify a particular type of fruit has a narrow band of contexts, with very limited variation. 

Creativity.

AI can simulate certain aspects of human creativity, but it cannot truly replicate the full extent of human creativity. Human creativity involves the ability to generate novel and original ideas, to make connections between seemingly disparate concepts, and to imagine new possibilities. While AI can generate novel outputs based on patterns it has learned from data, it still lacks the ability to truly understand the context and meaning behind its outputs, and to experience the emotions and intuition that drive human creativity.

Additionally, AI algorithms are limited by the data and parameters they are trained on, and they may generate outputs that are not truly creative or original. In this sense, AI can only simulate certain aspects of human creativity and cannot fully replicate the richness and complexity of human imagination.

There have been some fascinating attempts to develop AI that has archetypical creative behaviours. At a phenotypical level these involve designing the AI to write music or paint in the style of pre-existing human artists.

Daddy's Car is an AI devised and produced song written in the style of the Beatles.

Art created by the AI Dall E2. I asked the AI to create "an oil painting in the style of Lucian Freud of a man reading a book in a library"

However, the field of AI and creativity is an active area of research, and AI is being developed to perform increasingly complex tasks related to creativity, such as generating art and music, or creating new designs and products. As AI continues to evolve, it is possible that it may one day be able to simulate human creativity in more sophisticated ways. But for now, human creativity remains a unique and valuable aspect of human cognition that cannot be fully replicated by AI.

Conclusion

Overall, the question of whether AI can know in a similar way to humans is still a matter of ongoing debate and research. While AI has made great strides in recent years, there is still much that is unknown about its capacity for knowledge and understanding. As the field of AI continues to evolve and advance, it is likely that we will gain a better understanding of the limits of AI's knowledge and its potential for truly knowing in the same way as humans.

Most crucially for ToK Students, we could argue that as we develop AI's capacity and abilities our definitions, or labels, of what it is doing will develop. We will develop labels beyond "machine learning", "algorithm" etc to describe new and richer processes that AI is capable of. In turn we may also develop new labels for human ways of knowing. As such this very question ("Can AI know in the same way as humans") will develop and change in the future.

Daniel, Lisbon, February 2023

Other posts / videos on Technology:

We need to talk about Pune India.

What is Technology?

How does technology change our pursuit of knowledge?

Bibliography

• Brandhorst, Kurt. Descartes’ Meditations on First Philosophy. Bloomington, Ind., Indiana University Press, 2010.

• Chalmers, David. 1995. “Facing up to the Problem of Consciousness.” Journal of Consciousness Studies 2(3): 200-219.

• Dennett, Daniel Clement, and Paul Weiner. Consciousness Explained. Little, Brown and Company, 2007. 

• Gamez, David. “Machine Consciousness.” Human and Machine Consciousness, 1st ed., Open Book Publishers, 2018, pp. 135–48. JSTOR, http://www.jstor.org/stable/j.ctv8j3zv.14. Accessed 31 Jan. 2023.

• McCarthy, John (1979). Ascribing mental qualities to machines. In Martin Ringle (ed.), Philosophical Perspectives in Artificial Intelligence. Humanities Press.

• "The Little Thoughts of Thinking Machines", Psychology Today, December 1983, pp. 46–49. Reprinted in Formalizing Common Sense: Papers By John McCarthy, 1990, ISBN 0893915351"The Little Thoughts of Thinking Machines", Psychology Today, December 1983, pp. 46–49. Reprinted in Formalizing Common Sense: Papers By John McCarthy, 1990, ISBN 0893915351

• Mccarthy, John. “The Little Thoughts of Thinking Machines.” Psychology Today, 1983, pp. 46–49. 

• Nahmias, Eddy, et al. “When Do Robots Have Free Will?: Exploring the Relationships between (Attributions of) Consciousness and Free Will.” Free Will, Causality, and Neuroscience, edited by Bernard Feltz et al., vol. 338, Brill, 2020, pp. 57–80. JSTOR, http://www.jstor.org/stable/10.1163/j.ctvrxk31x.8. Accessed 31 Jan. 2023.

• Preston, Beth. “Heidegger and Artificial Intelligence.” Philosophy and Phenomenological Research, vol. 53, no. 1, 1993, pp. 43–69. JSTOR, https://doi.org/10.2307/2108053. Accessed 31 Jan. 2023.

How does technology change our pursuit of knowledge ?

The question how does technology change, help or hinder our pursuit of knowledge will be approached by arguments that technology helps in the pursuit of knowledge, then arguments that technology hinders the pursuit of knowledge, and finally (and probably most importantly) the wider implications of the arguments & question.

The exploration of many of the KQs in ToK Optional Theme Knowledge and Technology could start by looking at different ways to define technology, I have tried to do this in my earlier blog post “What is Technology ?” - if you are yet to read that you may want to jump back to check that out.

A note on complexity and simplification.

Anyone undertaking even cursory reading into the technology and knowledge space will quickly be confronted with the word complexity. Virtually all writers agree that a contemporary understanding of the role of technology is becoming ever more complicated, and technology itself is only increasing that complexity. Whilst I do not wish to understate both complexity, and its’ importance, I don’t see it as being a useful starting point for ToK teachers in our work with DP students - it may be our eventual destination, but I think it important to explore some of the underlying processes and phenomena first in order to give a better sense of that complexity. Obviously such parsing can lead to a (justifiable) charge of reductionism, but there is an educational reason for such reductionism.

Technology helps in the pursuit of knowledge.

Technology as a tool.

If we start from the premise that technology is created to solve problems / meet needs then, quite obviously, technology helps in the pursuit of knowledge by giving us new ways to acquire knowledge, reveals new knowledge to us, and allows us to synthesis pre-existing knowledge into new knowledge. This is the fairly conventional, near orthodox, perspective on the role of technology in the construction of knowledge. It’s widely found in the ToK literature, and as such I will not spend too much time on this argument here.

The argument that physical artefacts created to solve problems increase our ability to pursue, acquire, construct (and discover) new knowledge appears to be fairly indisputable. It is, as I will show, highly disputable, but we need to go through the tech as a good tool argument first.

Technological innovation in terms of physical artefacts have always given us access to previously hidden / inaccessible knowledge. Appropriate examples are near inexhaustible, a few examples include: the map allowed for better navigation, the compass allowed for even better navigation, powered flight allowed for faster travel, and the placement of satellites in space, GPS brings all of these technological innovations to give us the knowledge of exactly where we are on the globe at any moment in time. The same arguments can be built for any number of physical technology eg microscopes, printing presses, the crossbow, x-rays etc 

The “Tech as tool” argument becomes a little more interesting when we consider the technology as the knowledge which has led to the physical artefacts. For example the knowledge of geometry and algebra which enabled the map to be made, the knowledge of maps and physics which led to the compass, the knowledge of gaseous exchange and displacement which led to the rocket which deployed the satellite etc. This is interesting for two main reasons:

Tech as tool knowledge: causation issues.

Prima facie the cause of technology appears obvious - we create it to fulfil a need or solve a problem, this is often called the Functional explanation. We could ask why we decided to create a specific technology to fulfil a particular need rather than another form of technology, the answer to which is usefulness. This argument is well advanced by James Woodward in his paper A Functional Account of Causation; or, A Defense of the Legitimacy of Causal Thinking by Reference to the Only Standard That Matters—Usefulness  (Woodward, 2004). As such we have a way in which Technology helps the pursuit of knowledge, namely in its usefulness to that pursuit of knowledge. Through processes of selection and application the technology is found to be the most useful way in which to pursue (as an umbrella term) knowledge.

The concerns with, and challenges to, the functional explanation arise from the causal relationship between the knowledge, need and technology. Again, a prima facie explanation posits the need as existing first, and thus causing the technology. However the relationship between the knowledge being pursued and the need is not clear - with many technologies we were aware of the knowledge before we had the technology to confirm it (eg we knew the moon existed before we could get there etc) - so not all needs can are uniform, neither are they uniform in their operation.

Further, much technological innovation reveals new knowledge when we apply it (for example we weren’t aware of spike proteins on viruses until we had electro-scanning microscopes) as such the need didn’t exist for the technology to be created. Therefore need (nor function, nor usefulness) do not seem to be necessary, nor even sufficient, conditions for the creation of new technology. If we accept this premise we could build an argument that technology neither helps nor hinders in the pursuit of knowledge, but rather has a more haphazard, unpredictable, directionless effect.

The causes of technological innovation are an issue for us if we hope to answer the help/hinder question - we will need to return to them in more detail when we look at the hinder side of this question.

“Hold up Daniel, you’re taking a very serial, near uni-directional view, of the relationship between technology and the pursuit of knowledge. It’s more nuanced and bi-directional than that.” I hear you cry, and you would be correct. So, let’s look in more detail at the processes of knowledge transfer which could give rise to new technologies. 

Tech as tool for knowledge transfer.

Knowledge transfer is the process by which knowledge is transferred from one sphere to another, in ToK we could think of it in terms of transfer across concepts, theories, applications, disciplines, themes or Areas of Knowledge. It is not difficult to construct the fairly conventional argument that technological innovation in one area causes knowledge transfer to another area, which leads to subsequent technological innovation in that area. We often see examples drawn from the US space programme citing such processes. With application to the question regarding the role of technology in our pursuit of knowledge we can consider whether such technology transfer is necessarily causal in the creation of the new technology, whether the need for the new knowledge existed before the transfer (“The Russians took a pencil” story could be much cited here), and whether the transfer actually diminishes the pursuit of other forms of new knowledge in the second area - an issue to which we will return later in this piece.

Examples of such technology transfer that Diploma students may come across in their studies could include the development of fMRI in Natural Sciences led to new knowledge in Bio-Psychology in the Human Sciences, the development of new computer visualisation capability led to new techniques for designing objects and buildings, the development of social psychology in Human Sciences led to new models of epidemiology in The Natural Sciences.

Tech as Knowledge - Helps the Pursuit of Knowledge.

If we approach technology as a knowledge framework, or knowledge which gives rise to artefacts (tools) we start to develop some new ways in which we can argue that technology ‘helps’ the pursuit of knowledge.

A useful starting point for this approach is in the values, methods and purpose of the European Enlightenment (1600s+). At the core of the Enlightenment is a rationalist approach to thinking, and knowledge production / acquisition. That which is thought to be rational was often labelled as ‘useful’ knowledge  - useful in that it allowed for greater control of (“instrumentality”) of the environment. 

Underpinning the enlightenment is a significant increase in both the overall ‘amount’ of knowledge, and the ‘accessibility’ that people had to that knowledge. We see this at both the level of artefact (eg printing press and book) and at the institutional level (eg universities and public libraries). As such, we can argue that the knowledge framework of the enlightenment, as a technology for organising the world, significantly improved our pursuit of knowledge in terms of the aggregate production of knowledge and access to that knowledge. 

However, this argument can be further developed in terms of the role of ‘useful knowledge’ in control (which further helps the pursuit of knowledge). In his article  “The Intellectual Origins of Modern Economic Growth.” Mokyr, Joel argues that technology is the application of useful knowledge to control the unexpected (e.g. weather, natural disasters, and social behaviours caused by faith etc). 

As such, Mokyr argues that ‘useful knowledge’ both produces technology and is sustained by technology, and could be seen as a form of technology in itself (similar to Heidegger’s ‘essence’). The processes by which useful knowledge affects this are (i) producing cultural cohesion through technological dispersion of knowledge, and (ii) allowing specialisation, professionalisation and expertisation. Both factors significantly increases the material wealth of a society, which further enables technological innovation. Here we see a worked through model of the “Some Knowledge is Tech” approach.

However, in evaluation Charles Gillespie in his book Science and Polity in France at the end of the Old Regime argues that the link between knowledge and technology is at best tenuous, and often knowledge lags behind technological innovation. This argument is taken further by Nathan Rosenberg and Derek Price argue that technology causes knowledge. In order to make this argument we need to separate out formalised, validated, knowledge (such as academic and theoretical knowledge) from applied, pragmatic knowledge. In doing so, we can argue that practical innovation of technology gives rise to new knowledge, and as such further helps our pursuit of knowledge

Rationalism & Specialism.

The premise of Nicholas Maxwell’s book Science, Reason, Knowledge and Wisdom: A Critique of Specialism.” provides us with a strong framework for the claim that Technology is a form of knowledge that helps with the pursuit of further knowledge. Maxwell argues that the function of universalism (ie the reason why we have knowledge) is to answer 4 questions: 

1. What kind of world is this ?

2. How do we fit into the world, & how did we come to be ?

3. What is of most value in life, and how is it to be achieved ?

4. How can we help to develop a better human world ?

Maxwell argues that rationalism is a specific form of knowledge which enables specialisation in order to answer those 4 universal questions. These 4 questions are broken down into myriad smaller sub-questions helping us to form smaller solutions to the big questions. Specialisation of knowledge is the academic disciplines which give us subjects, leading to   larger groupings as Areas of Knowledge.

Maxwell’s work (built on Popper, amongst others) provides us with a framework for both the Tech as Tool and the Most Knowledge is Tech approach in answering the question how does technology change our pursuit of knowledge ? In précis he is arguing that technology helps us in our pursuit of knowledge as it is the applied form of specialised rational thinking serving to answer the big questions of universalism. In this claim the 4 universal questions constitute the fundamental cause of technology, subject disciplines are the knowledge solutions of those questions, and reason and rationality are the enabling means of the subject disciplines.

For Maxwell ‘intellectual inquiry’ (ie our pursuit of knowledge) is a tool, as such it constitutes technology in itself. He states:

“Intellectual Inquiry is our servant, not our master. It is not in itself any kind of authority or oracle”. 

Nicholas Maxwell

Tech as Tool - Hinders the Pursuit of knowledge.

The “Tech as Tool'' approach focuses on the physical artefacts of technology as constituting the technology itself. At first glance it would seem very hard to construct an argument that an artefact that gives us greater capacity to manipulate our environment would, also in turn, somehow hinder our pursuit of knowledge. It seems to be obvious that greater control of our environment would enable improved discovery / construction / acquisition of knowledge. 

However the argument that Technology (as a tool) hinders our pursuit of knowledge can be developed on the basis of:

• Alienation

• Selectivity, Amplification and The long tail.

• Homogenisation of knowledge.

Students may be very tempted to focus on the effects of technology on humans (such as reduced attention span, narrowing of interests, mental health etc). If these effects are commented upon it is important that they are explicitly linked to the “pursuit of knowledge”. The knowledge question is clearly focussed on the pursuit of knowledge, as such human effects of technology will need to make the association between psychological effects and the pursuit of knowledge. Nicholas Carr’s 2020 book The Shallows may be of use for students who wish to make this link.

There is obviously significant overlap with the Tech as knowledge approach here, which we will further develop in the next section.

Alienation:

The estrangement of individuals from themselves and others; a feeling of normlessness and powerlessness caused by separation and isolation from an individual’s sense of self, society, and work.

Open Education Sociology Dictionary

Many Human Scientists and Philosophers have written about ‘alienation’ as a product of human interaction with technology, especially relating to production in the workforce. Of particular note here is Robert Blauner’s 1964 account of working on the Ford production line (Peterson 1965).  

It could be argued that this process of separation of the individual from their themselves, their wider knowledge community hinders the pursuit of knowledge. The process of alienation (caused by technology) arguably reduces the knowledge that may be acquired / constructed in collaboration with others as we are put into ‘silos’ by technology. Further, it could be argued that the feelings of powerlessness and estrangement caused by the technology reduce the individual’s self efficacy for the pursuit of knowledge. In this scenario individuals feel less confident, and intrinsically less interested, in new knowledge, or the creation of new knowledge.

Selectivity, Amplification and “the Long Tail”.

It is easier for us to understand the processes of selectivity, amplification, and ‘the long tail’ in modern digital technologies than in antecedent technologies, but the same processes apply to all technologies. Let’s take each process in turn, and apply it to the pursuit of knowledge.

Selectivity - technology generally selects both the knower and the knowledge to which they are exposed (eg the knower has to have access to the internet, algorithms will then select knowledge to which the knower is exposed). This process of selectivity hinders our pursuit of knowledge because it reduces the range of diverse knowledge to which we may be exposed, it reinforces the power of the select knowledge to which we are exposed. This is obvious with the modern internet, but the same processes are in play with the establishment of public libraries, the printing press, factories, even the crop rotation system and early Mesopotamia farming processes. In every implementation of technology we increase our control of the environment, consequently reducing our exposure to alternative environmental pressures. As such we reduce our exposure to alternative knowledge sources - these knowledge sources could have been previously unwanted, unpredictable, or thought to be unhelpful.

Amplification - Once selectivity of knowledge occurs the subsequent amplification of the knowledge that we have been exposed to occurs. We generally don’t experience our knowledge world in deficit or lacking, we experience it as ful. As such, if we have been exposed to only a limited range of available knowledge we will ‘amplify’ that to which we have been exposed to become representative of ‘all knowledge’. This process is evident in contemporary social media, but the processes also apply to all antecedent technology (Marx described it as the knowledge function of capitalism – calling it “Commodity Fetishism”).

Much recent political research (AoK Human Sciences) has been done on the role of selectivity and amplification in the rise of populist political movements in Europe and North America. Valentino et al (2013) discuss how selectivity in both pursuit of, and exposure to, knowledge on the internet both causes, and is amplified by, anxiety. Technology causes anxiety (alienation effects), and in turn this causes The Knower to find knowledge which corroborates their viewpoint in order to reduce their anxiety. Arguably, this hinders our pursuit of knowledge as further reducing the range of diversity of knowledge to which we are exposed, and augmenting the knowledge perspectives already held. This leads to a process of homogenisation of knowledge.

The Long Tail

The Long Tail is the theory that the internet offers us a far wider range of knowledge sources (it’s usually applied to business, so those knowledge sources are usually ‘products’ in the literature), and therefore we are able to specialise in niches. The theory would, therefore, probably support the claim that technology helps our pursuit of knowledge. 

However, many recent studies have shown that the ‘long tail’ is not actually happening. What researchers are seeing (eg Netessine, Serguei. “Why Tom Cruise Is Still Bankable: Debunking the Long Tail.”) is that faced with a wide choice of knowledge sources we are more likely to fall back on what we know to be safe. This leads to further amplification of well known knowledge sources, and a reduction of exposure to lesser known alternative knowledge sources. This further supports the argument that technology hinders our pursuit of knowledge.

Tech as Knowledge - Hinders the pursuit of knowledge.

Beyond the arguments of the physical attributes hindering our pursuit of knowledge we can look at the KQ from the perspective that understanding technology is a form of knowledge that in turn inhibits the further pursuit of knowledge.

In this we will draw upon two main areas of philosophy:

(i) Aristotle’s 4 causes of technology

(ii) Martin Heidegger’s Question about Technology.

However, of course, ToK is not a Philosophy course, so we’ll just use these two philosphy sources as ‘underlying’ frameworks in order to unpack the knowledge question.

The starting point with this approach is that technology is a form of knowledge, and some (or all) knowledge is a form of technology. This argument is developed in the first blog & video in this series What is Technology? linked here.

How the causes of technology change / hinder our pursuit of knowledge.

Purpose.

We often find that technology is not used for the original purpose for which it was created. For example steam trains were invented to transport coal from the mine to the seaport, were adapted to carry humans, the world wide web was created to locate tanks on a battlefield, the necktie was created to fasten the upper seam of a shirt together etc.

The question arising from this re-purposing of technology is whether the need existed before the repurposing, and the new technology merely fulfills the need. Or, conversley did the new technology ‘create’ a new need which it then fulfilled ? This question is not central to our KQ (How does Tech change our pursuit of knowledge?).

The central concern arising here is whether the new technology (or knowledge) in the process of repurposing has limited our pursuit of knowledge in the area to which the technology has been repurposed ? Or has the repurposed technology led to new questions which further change our pursuit of knowledge ? For example, if the world wide web had not been created for military purposes, and then repurposed as the internet would we have found alternative technologies for sharing and creating knowledge amongst large groups of people ? Would these new technologies have avoided some of the negative consequences of mass group based interaction that we see with the internet ? And, most importantly -  did the invention of the internet constrain the pursuit of alternative technologies ? 

Potentiality.

The conventional tech as tool approach puts forward a fairly straightforward view that a need exists, knowledge is garnered to create a solution to that need, and that solution is new technology. This new technology in turn gives us access to new knowledge. As such, the argument goes that technology helps our pursuit of knowledge.

However, one of the problems with this argument is that it implies a very narrow set of human needs because the potential of the raw materials is vast whilst the uses that we put them to are relatively narrow. Further, there are a vast range of problems (needs) that we are yet to find solutions for. Some of these problems (needs) are for more pressing than the needs that we have fulfilled through technology. As such, the relationship between needs and technology is far from direct, and unequivocally unilinear.

Obviously we need to build into our argument the complicating external factors of access to technological materials, access to re-combination knowledge and technologies, powers of access to realisation and implementation, culture etc. All of these factors could, arguably, hinder the pursuit of knowledge.

The further, and bigger, knowledge question is the cause of the identification of needs. Are those things that we have identified as needs actually the needs which need fulfilling ? Do those needs necessarily help our pursuit of knowledge ? or is it possible that that which we identify as a need to augment our pursuit of knowledge ends up hindering our pursuit of knowledge ? The logical consequence of this argument is that it has to accept totalitarianism (which results in repressing alternative viewpoints, burning books, censoring the internet etc) is a political technology which fulfills a need which helps our pursuit of knowledge. Obviously I have picked an extreme example, but it is the outliers / exceptions that can illustrate the problems in the original argument (Popper’s Black Swans).

Technology changes our perspective on the world.

If we take the conventional tech as tool argument then we can argue that technology helps us to better understand the world around us. In its role as ‘revealing’ the world it clearly helps us in our pursuit of knowledge. This argument is fairly easy to make with many pre-industrial technologies, particularly those that generally worked in balance with the natural world (eg windmills, waterwheels, hunter-gatherer societies etc). However, it could be argued that technology which enables intensive exploitation of the natural world changes our perspective, and therefore our knowledge, of the world.

The argument here is that modern technologies enable such a high degree of manipulation and exploitation of the natural world that we stop seeing the natural world as a place that we fit into, but a place that has to fit to our needs. As such modern technology elevates us to a position of supreme  mastery over the natural world. In turn this means that rather than pursuing knowledge of the world per se modern technology enables us to pursue knowledge of the world for purely extractive and exploitative purposes. As such this hinders our pursuit of knowledge as it limits the perspectives and potentiality that we see in the world.

Closing Thoughts.

As with all good ToK the key terms in the PT need to be unpacked before venturing an exploration of this question. How we define “change / help / hinder” will have a bearing on how we consider the effects of technology identified (somewhat like Aristotle’s causes !). Crucially how we explain / define ‘the pursuit of knowledge’ will influence both our definition of technology, and our judgment of the role that that which is defined has. Interestingly, the ToK Study Guide (first assessment 2020) does not define “the pursuit of knowledge”.  

I would argue that the pursuit of knowledge is always contextualised, and that context will have a significant effect on the ways in which technology changes our pursuit of knowledge. This will vary by situation, place, time, knower and the knower’s purpose. In some contexts the pursuit of knowledge could be helped by technology, in other contexts it could be hindered by technology. This change could vary for the same knower, using the same technology for the same purpose in different contexts.

Finally, we come back to the original conundrum running through this series on technology - that is the causes of technology. Most students responding to this KQ may take the premise that technology fulfils needs, the needs constitute the causes of the technology, and therefore the technology helps our pursuit of knowledge. However, the fulfilment of those needs may produce a further set of needs which in turn require further fulfilment. Technology must be seen as an integral and productive part of the knowledge being pursued. The relationship between needs and technology is mutual and reciprocal, as such we could conclude that rather than helping or hindering our pursuit of knowledge technology both changes, and is in itself, our pursuit of knowledge.

Daniel, Lisbon, Jan 2023.

Other Blogs & Videos in this series:

We need to talk about Pune, India.

What do we mean by Technology?

References and Bibliography.

• Bell, Kenton. “alienation definition.” Open Education Sociology Dictionary, https://sociologydictionary.org/alienation/. Accessed 24 January 2023.

• Bimber, B. “The Internet and Political Transformation: Populism.” Polity, no. 31, 1998, pp. 133-160. https://www.jstor.org/stable/25655419.

• Carr, Nicholas G. The Shallows: What the Internet Is Doing to Our Brains. W.W. Norton & Company, 2020. 

• Gillespie, Charles C. Science and Polity in France at the end of the Old Regime. Princeton, NJ. Princeton University Press, 1980.

• Maxwell, Nicholas. “Science, Reason, Knowledge and Wisdom: A Critique of Specialism.” Karl Popper, Science and Enlightenment, UCL Press, 2017, pp. 233–90. JSTOR, https://doi.org/10.2307/j.ctt1vxm8p6.14. Accessed 23 Jan. 2023.

• Mokyr, Joel. “The Intellectual Origins of Modern Economic Growth.” The Journal of Economic History, vol. 65, no. 2, 2005, pp. 285–351. JSTOR, http://www.jstor.org/stable/3875064. Accessed 20 Jan. 2023.

• Netessine, Serguei. “Why Tom Cruise Is Still Bankable: Debunking the Long Tail.” Knowledge at Wharton, 15 December 2017, https://knowledge.wharton.upenn.edu/article/tom-cruise-threatened/. Accessed 24 January 2023.

• Peterson, Richard A. The Sociological Quarterly, vol. 6, no. 1, 1965, pp. 83–85. JSTOR, http://www.jstor.org/stable/4105309. Accessed 24 Jan. 2023.

• Prince, Derek J. de Solla. “Notes towards a Philosophy of the Science / Technology Interaction.” In The Nature of Knowledge: Are Models of Scientific Change Relevant ? edited by Rachel Laudon. Dordrecht: Kluwer. 1984

• Rosenberg, Nathan. “Adam Smith on the Division of Labour: Two views or One ?” Economica 32, no. 126 (1965); 127-39

• Valentino, Nicholas A., et al. “Selective Exposure in the Internet Age: The Interaction between Anxiety and Information Utility.” Political Psychology, vol. 30, no. 4, 2009, pp. 591–613. JSTOR, http://www.jstor.org/stable/25655419. Accessed 24 Jan. 2023.

• Woodward, James. “A Functional Account of Causation; or, A Defense of the Legitimacy of Causal Thinking by Reference to the Only Standard That Matters—Usefulness (as Opposed to Metaphysics or Agreement with Intuitive Judgment).” Philosophy of Science, vol. 81, no. 5, 2014, pp. 691–713. JSTOR, https://doi.org/10.1086/678313. Accessed 11 Jan. 2023.

What is Technology?

Many of the explorations of knowledge questions in the Knowledge & Technology unit of ToK start with the question: "well, what do we mean by technology ?". So, I thought it would be useful to put together a blog which summarises 4 main approaches to how we can think of technology in its relationship with knowledge.

These approaches are very much umbrella approaches - they are rough ideal types to help us to explore that relationship between tech and knowledge, remember the focus is on knowledge, not tech.

The "tech is tool" approach.

The argument here is quite simply that technology is a tool that we use to solve human problems. This is obvious when we look at modern technologies such as the internet, cars, the printing press etc. It then also becomes apparent when we consider technologies from pre-industrial era such as smelting metals, wattle and daub etc.

This approach quickly takes us into non-physical technologies such as mathematics is a technology which allowed us to solve the problem of navigation through map-making, art is a technology which allows us to solve problems of expression and social cohesion etc. Arguably, language is the ultimate technology which allows for all other technological (& therefore knowledge) innovation. This approach has been well explained in the books by Yuval Noah Harari (particularly Sapiens: A brief history of Humankind).

Among the many writers who have taken the "tech is tool" approach are Plato and Rousseau who both argued that technology had a rather negative effect on knowledge and humanity. In Phaedrus Plato argued that that the use of writing had a negative impact on people's ability to remember and think critically. Jean-Jacques Rousseau, wrote about the dangers of technological progress in his work "Discourse on Inequality." He argued that the development of technology had led to the development of private property, which had in turn led to social inequality.

On the other hand, Francis Bacon and Karl Marx, are writers who, in taking the "tech is tool" approach see technology as a positive benefit to the pursuit of knowledge, and the development of humanity. Bacon saw science and technology as being a single unified entity. He argued that science was the best way to uncover universal ordered truths from the disordered chaos of nature. Marx saw technology as a means by which proletarian labour (& bourgeois extraction of it) is quantified, and therefore is a necessary stage for the realisation of socialism. As such, Marx was positive about the influence of technology on the pursuit of knowledge.

I think that this approach is implied, and assumed, in the knowledge questions included in the ToK Study Guide for Knowledge and Technology. This approach may be all that is required of the ToK learner. However,

However, there are some concerns with this approach, concerns which are both general for us as learners, and specific to ToK:

1. Did these problems, which technology apparently solves, come before the technology or did technology create these problems ? (the problem here is one of causation - what is the cause of an object ?)

2. If the problems are antecedent to the technology, and technology is the solution to them, then are technology and knowledge actually separate entities ?

3. If technology and knowledge are intertwined then is there any non-technological knowledge ?

4. Wider ontological problems arising from the above - if knowledge is a requisite for existence, then is technology also a requisite for our existence ? Are we defined by solving problems ? Is consciousness essentially a task focussed process (Heidegger).

Concerns #1 & #2 conveniently segue into our second approach.

The "Some knowledge is tech" approach.

This approach argues that the knowledge which gives rise to the technology developed to solve the problems that we face is in itself technology. Knowledge such as language (incl. digital coding languages), religion, scientific theories, artistic arrangement etc all give rise to specific technologies which help us to solve a set of problems.

In this approach we start to understand technology as a set of practices rather than merely as a set of objects. Both the object (artifact) and the practices (processes) are seen as being technology. The object itself might be termed "instrumentality" as it was produced to (instrumentally) change the environment - ie to solve a problem. The practices which brought the artifact into being might be termed "productivity" as they gave us an object which, at some point, gave us increased control of our environment for a required purpose. The effect of this categorisation on the acquisition and production of knowledge will be explored in greater detail in subsequent blogs.

This approach also opens the door to a consideration of the social environment within which needs arise, and knowledge develops in order to meet those needs. Of course, this brings a sharp focus on what we define as 'needs', and who has the attendant power to solve that which they define as 'needs' (a quick sub-question: a lot of technology serves 'improvement' - is improvement fulfilment of a 'need' ?). And again, we have significant problems of causation here - what is the order of causation ? Is causation a necessary, or merely, sufficient requirement for the acquisition and production of knowledge ? etc

Overall, this approach also poses a number of challenges for our theory of knowledge:

1. Is the technology causal to the knowledge or vice versa ? (think about examples - this is more problematic than it first appears).

2. Both knowledge and technology can be thought of as evolutionary (and sometimes revolutionary) - does knowledge cause technology to evolve, or vice versa ? , and if so, how ?

3. Do we produce some knowledge which is not to solve problems ? , and if so what, and why ?

4. A range of ontological questions arising from #3: are we solely a problem solving being ? what about non-problem solving behaviours ? (do they even exist in this definition?). Is consciousness contingent on

Challenge #3 conveniently segues into our next approach.

The "all knowledge is tech approach".

If we accept that technology is a tool to solve problems, and that we accept that that which is known about the world is acquired, pursued and produced to solve problems then we arrive at the position that all knowledge is technology. Conversely, all technology is knowledge (however, this is a little more obvious, and a little less overwhelming). This approach gives rise to some very significant challenges:

1. Is there any knowledge which is not technology ? We can unpack this question by positioning problems as time, person and situation specific. ie we know things but may not be using them to solve a problem at that moment in time. Someone, somewhere else, may have used that knowledge to solve a problem, and once created this knowledge has been passed to me. This gives rise to a second problem:

2. Why do we have knowledge which does not solve problems for us ? If we accept that all knowledge can be categorised as technology, and that technology solves problems for us, then why do we know things which don't solve problems for us ?

3. Our now familiar ontological questions are now even stronger - if all knowledge is technology, and knowledge is a necessary requirement for our existence then this approach inevitably leads to the position that to being human is being technology, or put another way that a human being is technology itself.

And so we, conveniently, segue into our final approach.,

The "we are the tech - unified being approach".

OK, so now we need to work a little out of the realms of conventional ToK, but only to give us better ways to explore some of the ToK KQs posed in the optional theme Knowledge and Technology. Some writers have argued that our very existence, - our very human 'being', is one and the same as technology. Put simply we are technology. This approach, as the culmination of the 3 earlier arguments, aggregates those arguments to posit our 'being'ness as constituting a problem solving set of processes. This is often characterised as consciousness - the idea that consciousness is a referenced intention in the world.

This approach really helps us to start to answer questions about the role of technology in changing our pursuit of knowledge. Rather than tech merely improving, or impeding, our pursuit of knowledge technology reveals the world, and therefore is our very consciousness, our very awareness of the world - it neither improves nor impedes, but in its role as revelation is consciousness itself. This will really help us when we get to questions concerning artificial intelligence, and the biological integration of technology.

However, like the other 3 approaches, this approach poses some significant challenges for our understanding of the role of technology in the pursuit of knowledge:

1. Ethical issues - If tech & being human are one & the same thing, but there is unequal access to tech then is there also unequal access to the experience of being human ?

2. Continuum issues- where does the individuality of the knower begin, and the external universality of tech end ?

3. Categorisation & Organisation issues - why do we bother to have a separate category of knowledge called 'technology' at all ?

Hold Up!! - some of that has nothing to do with ToK!

"Some of those points above appear to be way beyond the scope of ToK". When we start to consider ontological questions such as the nature of existence, the requisite conditions for existence, and the nature of consciousness it appears that we are going well beyond the requirements of the ToK course. However, I believe that we can only tackle some of the technology KQs by considering some of the questions which might (conventionally) be asked by people who we label as existentialists, phenomenologists and ontologists. This will become far clearer when we get to the post on Artificial Intelligence.

Hold Up!! - again!

"Your 4 approaches are all based on one premise. They're all based on, and developments of Approach #1 - that technology is a tool to solve a problem".

Yes - this is a legitimate challenge to the framework outlined here. An equally valid approach would be to start from an entirely different premise, maybe that technology is not caused by problem solving, that technology is caused by, and defined by something entirely different. However, that's a big undertaking - maybe one that I will need to explore in another blog.

Closing thoughts.

We can use these definitions to help us to start to explore some of the knowledge questions in ToK Optional Theme Knowledge & Technology. We will look at 3 broad areas:

1. How does technology change our pursuit of knowledge?

2. Is Artificial Intelligence changing our understanding of knowledge?

3. Ethics and technology.

Those blogs are coming up in the next few weeks - I hope you come back to read them then!

Daniel, Lisbon, Jan 2023

Of further interest on Knowledge & Technology is:

We need to talk about Pune, India.

Women in STEM lesson (for teaching perspectives)

Did Photography change painting?

Bibliography and References.

• Bimber, Bruce, 1990, “Karl Marx and the Three Faces of Technological Determinism”, Social Studies of Science, 20(2): 333–351. doi:10.1177/030631290020002006

• Franssen, Maarten, and Gert-Jan Lokhorst. “Philosophy of Technology (Stanford Encyclopedia of Philosophy).” Stanford.edu, 2009, plato.stanford.edu/entries/technology/.

• Plato. Plato's Phaedrus. Cambridge :University Press, 1952

• Rousseau, Jean-Jacques. Discourse on Inequality. 1755. Aziloth Books, 2013.

• Weeks, Sophie. 2008. “The Role of Mechanics in Francis Bacon’s Great Instauration”, in Zittel, C., Engel, G., Nanni, R. & Karafyllis, N.C. (eds.), Philosophies of Technology: Francis Bacon and his Contemporaries. Brill. pp. 133-195.

• Yuval Noah Harari. Sapiens: A Brief History of Humankind. 2011. Random House Uk, 2019. https://www.bbc.com/future/article/20190207-technology-in-deep-time-how-it-evolves-alongside-us

Value Judgments in AoK History

Is it unfair to judge people & actions of the past by the standards of today?

I put together this lesson both because making value judgments in AoK History is a 'hot topic' in the current political zeitgeist sweeping academia and campuses, but also (and mainly) because some of my ToK students have been discussing it in their History class. I wanted to show how we could approach answering the question using Knowledge Arguments (or "perspectives").

This is the second lesson in the unit on AoK History. The first lesson can be found here.

This lesson focuses on two key ToK Skills:

1. Using knowledge arguments to explore a knowledge claim.

2. Identify the possible implications of a knowledge argument in terms of a knowledge claim.

ToK Skills Diagram Click here.

Lesson Structure.

Upfront teaching (Using the Presentation linked).

I've gone for some 'upfront' teaching at this point for 2 reasons:

1. My DP1 ToK class have requested more upfront direct instruction - I guess everyone has their fill of constructionism at some point !

2. I need to start bringing together some of their (burgeoning) ToK knowledge to show how it can be structured into an answer.

The presentation starts with a Mentimeter Poll - I find this is a great way to get engagement, and to give some RLS to form the basis of the ToK discussions.

Student active learning on historical judgments

Student Group Work.

Students form groups around the perspectives on the 'fairness' of historical judgements that they agree with.

Each group is to answer 3 questions:

1. Why did you choose this perspective rather than the other 2 perspectives ?

2. Explain one implication for Historical Knowledge of your perspective (using historical RLS).

3. Explain one implication for historical knowledge of one of the other perspectives (using historical RLS).

The challenge of the group work will probably be to keep the students focussed on the knowledge issues rather than the historical examples. The wording of the KQ from the ToK Study guide uses the word "unfair" which could be easily interpreted as a call to emotion. However practising the skill of extrapolating knowledge points from RLS will be of great benefit when the students do the ToK Exhibition and Essay.

Implications of knowledge arguments.

The group work also requires the students to consider the implications of knowledge arguments. This is a skill which I have found student's are particularly challenged by. Again, we're trying to build this for later assessments, as it is key to accessing the higher mark bands in the ToK Essay.

Closing Thoughts.

This lesson requires the students to exercise their "Historical Imagination" (to a degree), and then to transpose that into the ToK environment. We try to find a shortcut to that process by providing them with the ToK arguments at the beginning of the lesson. This is one of the ways of trying to build higher level skills in a restricted period of time. Obviously, this is only done because the students will have spent sustained time in more constructivist learning engagements earlier in the course.

There's more on ways to develop ToK understanding in the limited time available in the post linked here.

If you have any suggestions for teaching AoK History please do not hesitate to get in touch Daniel@TokToday.com. Wishing you a great day,

Daniel, Lisbon, Jan 2023

Bibliography

• Little, Vivienne. “What Is Historical Imagination?” Teaching History, no. 36, 1983, pp. 27–32. JSTOR, http://www.jstor.org/stable/43254801. Accessed 16 Jan. 2023.

We need to talk about.. Pune, India.

To understand this blog you're going to need to watch the (2 min) video below first.

“What's the video about ? Pune, India ?"

I hope that you realise that the video is not directly about Pune, India. Pune is used as an example of things that we know which may not have any apparent, direct, tangible, function for us. I could have picked my knowledge of the words to an Abba song, the history of the early Christians in England, or the chemical formula for photosynthesis. I know all of these things, but none of them have an apparent function to my every day life.

"So, you know stuff you don't use. What's that got to do with the ToK of Technology?"

The answer to the question "why do we know things that we don't use ?" is a way of approaching the ToK of Technology. Most conventional writing on the relationship between knowledge and technology takes the approach that technology is a tool which helps us to manipulate the world, and therefore helps us to know more about the world. I'm not sure that I agree with this.

"I'm still not clear what Pune has got to do with the ToK of Technology".

My knowledge of Pune may have been acquired due to technology, I am interested in the causes of that technology. Does that technology have a direct functional relationship to my needs ? Is it helping or hindering my pursuit of knowledge in the world ? How is it shaping my perspectives of the world ? Most importantly, what has caused the technology that allowed me to know about Pune ?

In the video I make the point that the conventional answers to these questions tend to focus on HOW we use technology, or HOW technology works. I'm more interested in the causes of technology, questions pertaining to WHY technology exists rather than just how it works.

Of far more interest to this approach is the premise that rather than just knowing about Pune through technology, Pune is technology itself. This approach starts to question the conventional separation between knowledge and technology.

"but, surely how technology changes our knowledge is more important to the Theory of Knowledge ?"

Many people would agree that a descriptive account of how technology operates gives you great insight into the effects of that technology. I contend that trying to understand the causes of technology will give us a greater understanding of its effects on knowledge than merely describing its operation. In this I am taking a rather structural, functionalist, approach.

"OK, how do I find out more ?"

In the coming weeks TokToday will publish 3 blogs on the relationship between knowledge and technology, unpacking some of the Knowledge Questions from ToK optional unit Knowledge & Technology. These blogs will focus more on the causal perspectives of technology rather than the descriptive perspectives.

These blogs are underpinned by my thinking on creating The ToK Mindset (linked here), ToK Skills and how to teach critical thinking.

I hope that you find the upcoming series useful, and if you have any questions or suggestions please don't hesitate to get in touch: Daniel@TokToday.com

have a great day!
Daniel, Lisbon, Jan 2023

Historical "Truth" - AoK History

Today's lesson explores the issues surrounding the reliability and validity of knowledge in AoK History. The lesson allows students to draw upon a wide range of ToK Concepts, and is purposefully open ended. The lesson allows students to explore the idea of a "historical Truth" (and hopefully to problematise such a term). As such it should be equally accessible for students who don't study History as it is for those who do. A

Students can draw upon the range of ToK concepts, maybe with particular reference to Objectivity, Perspective, Interpretation, Evidence and..., TRUTH

The lesson.

President George W. Bush at Emma E. Booker Elementary School in Sarasota County, Florida, September 11th 2001 as he was informed of the 9-11 attacks on The World Trade Centre.

What's wrong with this photo?

A few interesting sources:

Wired

Snopes.com

hoaxes.org

Student collaborative thinking:

Working in Groups brainstorm some answers to the following questions. Add your answers to the Presentation for feedback to the whole group:

Can you establish the Historical Truth of this photo ? (All groups)

If a range of sources disagree how do we establish objective facts ? (Grp 1)

What counts as a fact in history? (grp 2)

Is a history which is internally consistent necessarily true ? (grp 2)

Could more than one version of the past, even contradictory ones, be internally consistent ? (Grp 3)

Is a fact always the truth ? (& vice versa: is the truth always factually accurate ?) (Grp 4)

Is it important to establish historical objectivity, and if so why ? (Grp 5)

Is it unfair to judge people and actions in the past by the standards of today? (grp 6)

Should terms such as “atrocity” or “hero” be used when writing about history, or should value judgments be avoided? (Grp 7)

Feedback Presentation

Other TokToday content that ToK Teachers may find useful:

A whole unit of Natural Science Lessons (Free).

AoK Human Science Lesson on Cargo Cults of Melanesia (also use for Indigenous Knowledge) Free.

Developing the ToK Mindset.