ITGS and Theory of Knowledge (TOK)

The IB Theory of Knowledge (TOK) course is central to the IB diploma programme and aims to help students examine knowledge issues, Ways of knowing (sense perception, language, reason, emotion, intuition, memory, imagination, faith) and different Areas of knowledge (mathematics, natural sciences, human sciences, history, the arts, ethics, religious knowledge systems, indigenous knowledge systems).
TOK clearly relates directly to many of the social impacts and ethical issues students will encounter in the ITGS course. The resources below should help teachers draw on these links during their classes. If you are looking for TOK resources for a particular ITGS topic, you can use the search page to select TOK and the topic of your choice.

Textbook exercise

Exercise 1.8

ITGS Guide: "On what basis can we trust "knowledge" acquired from a range of sources?"
ITGS Links: 1.1 Reliability and Integrity, 1.12 Digital Citizenship, 3.5 Internet

The BBC article Are we trapped in our own web bubbles? and Eli Pariser's TED talk 'Beware online filter bubbles' are two resources that discuss how personalised search results could limit our access to new information.

Search engines play a major role in providing access to knowledge and information. The order of the links appearing in search results therefore has a significant impact on the types of information that will be accessed by the majority of people (witness how many people only ever use the first page - or even half page - of search results). Additionally, some search engines and social media sites have started to use personalised search results, which can prioritise results that are similar to pages we have previously viewed - thus forming a so-called 'search bubble' or 'filter bubble' that might limit our exposure to new views.

Despite this, there is still some debate over just how significant the filter bubble effect is. A 2015 study of Facebook data suggested the effect was minimal or non-existent - but the study itself was quickly criticised. Filter bubbles returned to the media spotlight after political events including the election of Donald Trump and the UK Brexit vote. The Guardian attempted to examine the effect in 2018, while the University of Illinois has an interesting page examining the effect and presenting an experiment you can try for yourself.

This can be a useful starting point for exercise 1.8, and also links closely to the IB Theory of Knowledge (TOK) course.

Updated: 2018-08-17
ITGS TOK lesson

Lesson resources: ITGS and TOK

The BBC article How fake images change our memory and behaviour ties in closely with ITGS and the Theory of Knowledge course. The article explains experiments in which false memories have been implanted in subjects' minds by showing them manipulated photographs of events that never happened. In one instance, 50% of subjects 'remembered' childhood events that never happened after being shown manipulated photos. A very interesting and new angle on manipulated images.
Updated: 2014-10-03
Internet censorship

Exercise 14.1: Internet filtering, censorship, and surveillance

Internet censorship is a huge topic, and one that truly highlights the global nature of the ITGS course. It is also closely related to the IB TOK course.

As an introduction to this topic, asking students to discuss or research a little about censorship in their own countries (and their opinions of this) is often very englightening. The news articles below have been divided into general categories simply to facilitate navigation.

General articles about Internet censorship

Reporters without Borders and the Open Network Initiative (ONI) both maintain up to date information about global Internet surveillance and censorship. In addition, the following articles are useful for stimulating conversation about types of appropriate and inappropriate content, and whether government control of the internet is appropriate.

Internet censorship in Europe

Internet censorship in Asia

Internet censorship in Australia

Filtering by search engines and online services

Increasingly search engines, social networks, and other web sites may also be asked to block access to certain content - either locally or globally. This is particularly significant because millions of users rely on these services to access information: the absence of a piece of content may well be taken as an indication that the content simply does not exist. The news articles below provide examples of this type of filtering:

The digital citizenship page covers some of the potential legal impacts of online behaviour.

Updated: 2018-05-21
Artificial intelligence examples

Exercise 16.13: AI artist examples

These links may be helpful as examples of the types of 'Artificial Artists' that are currently available.

  • AARON - Description of AARON, an AI artist, and some sample pictures.
  • Ray Kurzweil's Cybernetic Poet - An artificial poet.
  • FractMus - a program to generate musically mathematically (Windows).
  • TuneSmithy - Another Fractal music program (Windows).
  • Emily Howell A computer program developed by David Cope, which 'learns' how to compose music by being 'encouraged' and 'discouraged' by the user.

The Best Examples Of "Unusual" Art is a blog post that contains some great examples of human created art for this discussion. Can Robots be Creative? is a good video for a lesson starter.

Updated: 2017-10-23
Bias in Wikipedia

TOK, Wikipedia, and ITGS

Wikipedia is often criticised for being "unreliable", but few criticisms go beyond "anybody can edit it". The resources below examine the demographics of Wikipedia's contributors and editors, and provide some insightful statistics that can be a great source of discussion in both TOK and ITGS lessons.

Wikipedia's editors are basically all dudes examines gender bias in Wikipedia while Wikipedia's own page on systematic bias is full of useful information.

This can lead to some great TOK knowledge questions, including:

  • How can we identify systematic bias?
  • Can we ever truly overcome systematic bias in sources?
  • If 'average' is used in the mathematical sense, how representative would an 'average' contributor be? Is an 'average' of knowledge desirable?
  • Is there some information which cannot be simply classified as 'correct' or 'incorrect'?
  • Is there a place for such information in an encyclopedia that aims to be "to be the sum of human knowledge" (which leads us back to a classic open-ended TOK question: "What is knowledge?")

Updated: 2014-11-17
Facebook logo

Policies: Policing social media

Policing a global web service such as Facebook or Twitter is clearly a difficult task, and there are many social impacts and ethical issues to consider. Most obviously, different countries, regions, and users have wildly different standards regarding what is acceptable and unacceptable. Content also spreads extremely quickly online, while new situations constantly arise, requiring companies to make quick policy decisions. Below are examples of situations where material has been removed (and sometimes reinstated) by social media sites. These issues are also a great opportunity to link ITGS and TOK, with many knowledge issues surrounding censorship and filtering.

In May 2017 a Facebook document was leaked which revealed their internal rulebook on sex, terrorism and violence. Finally, ITGS students might be surprised to learn who makes the decisions about removing content - The dark side of Facebook explains this.

Updated: 2017-07-12
Self driving cars

Driverless cars and ethics

Driverless or self-driving vehicles are often promoted as being safer than human drivers. However, there may be situations in which an accident is unavoidable. In these situations, how should a driverless vehicle be programmed to behave? Which course of action should it take if all have negative outcomes? And, of course, who takes responsibility for any damage that is caused?

This is a topic which links to ITGS and TOK. The ethical dilemma of self-driving cars (video) is a good introduction. Why Self-Driving Cars Must Be Programmed to Kill and Ethics of Self-Driving Cars are great articles that examine the topic in more detail.

Uber driverless car accident

In March 2018 an accident occured which was reportedly the first death caused by a driverless vehicle. The Uber self-driving car hit and killed Elaine Herzberg, 49, in Arizona. The human monitor in the car also failed to spot the pedestrian until seconds before the collision. Uber stopped all self driving experiments in the aftermath of the crash.

Velodyne, the company that produces the sensors for the cars, reported that the sensors were working correctly - suggesting a software issue may have been the cause. It was later reported that the car's sensors detected Herzberg, but chose not to swerve as it was uncertain about the nature of the obstacle.

Updated: 2018-05-21
Should Robots look like humans

Video: Should Robots Look Like Humans?

This short video examines a new android called Sophia, and discusses the reasons for designing human-like robots (and the reasons not, too).

Updated: 2018-01-26
AI racial bias

AI facial analysis demonstrates bias

This article discusses how artificial intelligence systems can demonstrate both gender and racial 'bias'. Of course, this bias stems from unrepresentative training data - such systems are better at recognising white males because they are given more images of white males as training data. The article and video could lead to some interesting TOK discussions such as 'Can machines be bias?' and 'can we ever escape bias?".

Updated: 2019-05-10