As we head into the start of Term 1 it is already looking like Artificial Intelligence is going to be one of the most talked about issues in the classroom. Much of the narrative around models like OpenAI's ChatGPT has centred on students using it to cheat on assignments. But many schools are much more interested in the potential of these technologies to help, rather than hinder in education.

As much as there is enjoyment in working with the technology, it has many flaws. It is our responsibility to discuss the ethical considerations of AI with our students. AI ethics goes beyond the well-documented "algorithmic bias" that results in language models like ChatGPT producing racist and sexist output. In this article, I explore nine ethical considerations ranging from "beginner" to "intermediate" and "advanced" levels.

The levels reflect how easy it is to access information and resources on the particular ethical concern, and how likely the concepts are to already fit within your curriculum. As you move through the levels you and your students will be required to understand and apply increasingly complex concepts and terminology.

Beginner

Bias

Artificial Intelligence comes in many forms, but all require data. ChatGPT, for example, is a large language model that is trained on a huge dataset which includes the "common crawl": a text-based database of over 12 years' worth of web pages. These datasets give the models tremendous capabilities, but they are also inherently biased. Indiscriminately "scraping" the internet lets in the bad along with the good, meaning that the dataset can contain racist, sexist, ableist, and otherwise discriminatory language.

Unfortunately, AI large language models hold up a mirror to internet society, and the reflection is not pretty. Like other societies, the online community underrepresents marginalised groups, and overrepresents others. The prevalence of racism and bigotry on sources like Reddit and Twitter can bleed through the datasets and be reproduced in the output.

Bias can also come from the methods of training and reinforcement used when developing the AI systems. For example, police in the US have used systems for "predictive policing" which use algorithms to predict people likely to commit crimes. These algorithms disproportionately target poor, Black, and Latinx communities, reinforcing existing systemic biases.

Subject examples:

  • Humanities (Legal Studies): What legal precedents exist for protecting marginalised groups from discrimination? Is a biased algorithm legal?
  • English and Literature: How have certain groups been silenced or oppressed throughout history? What is the implication of this "gap" in the written record of the internet?
  • Mathematics: What is an algorithm? How do algorithms and probability link to policing and other societal functions?

Environmental Impact

The technology industry as a whole has an enormous impact on the environment. Most devices incorporate metals like lithium and rare earth minerals which are in short supply and costly to extract. The mining and refining of these products adds to the environmental impact, including soil erosion, water pollution, and greenhouse gas emissions.

AI computing is increasingly carried out in "the cloud". Cloud services sound ethereal and temporary, but the name hides the physical reality of the technology. Cloud computing relies on huge data centres and infrastructure, all of which consumes energy and produces waste.

Although many major companies such as Google, Microsoft, and Meta have pledged to make their data centres carbon neutral, in reality this often means engaging in carbon-trading or offsetting schemes rather than actually reducing the amount of waste or environmental damage. This "greenwashing" is heavily criticised.

Subject examples:

  • Humanities (Geography): What is the impact of the climate crisis on different global populations? Which parts of society are responsible for the most emissions due to AI technologies?
  • Science: Why does computing use so much energy and produce so much waste heat? What is the impact on the environment?
  • Design and Technology: How might we design more sustainable systems for the manufacturing of AI technologies?

Academic Integrity and "Truth"

Academic integrity has been by far the biggest potential issue of AI covered in the media. There have been widespread fears that students will use language models like ChatGPT to write essays, answer questions, and cheat on assignments.

It is still unclear to what extent using an AI constitutes "cheating". It is not, strictly speaking, plagiarism as the output of the model is not copied from another source. Rather, the output is an original creation which is generated "probabilistically". Knowing where to draw the line raises ethical questions about academic integrity and honesty.

As well as cheating, there are concerns that AI will be used to produce massive amounts of "fake news" or deliberately harmful media. This may be unintentional, as one of the biggest current flaws of most language models is that they can generate very convincing lies. Or, people may use these technologies maliciously to spread political misinformation.

Subject examples:

  • English: If an AI can write an essay, what is the point of writing essays? How do essays help us to build knowledge and not just demonstrate it?
  • Religion: What are the ethical and moral implications of academic integrity?
  • Humanities (Legal Studies): Is using an AI to write an essay cheating? Is using an AI a form of contract cheating?

Intermediate

Copyright and Intellectual Property

Copyright issues have been particularly prevalent in AI image generation. AI image generators like Stable Diffusion, DALL-E 2, and Midjourney are trained on images "scraped" from the internet. This has resulted in artists' "styles" being used in AI image generation without their permission. Many artists believe that this infringes their intellectual property rights.

Large language models like ChatGPT also incorporate huge amounts of other writers' work. Prompting a language model to write something in the style of another author could be viewed the same way as an image generator adopting another artist's style.

There are also question marks over who owns the copyright to materials produced by AIs.

Subject examples:

  • Visual Arts: Is it art? If a user can generate an image in the style of another artist, does the digital output count as "real art"?
  • Performing Arts: Some AI models can produce music and lyrics. Is it possible to create a complete AI performing artist?
  • English: Does producing a piece of writing in the style of another author infringe their intellectual property rights?

Privacy and Security

Privacy is a major concern in the development and use of AI systems. There are increasing concerns about the collection and use of personal data, data breaches, and the lack of transparency in AI decision-making.

One of the most prominent examples is the use of facial recognition technology. This technology has been criticised for its potential to violate individuals' privacy and civil rights. Facial recognition systems have been known to have higher error rates for people with darker skin tones, and have been used to target and monitor marginalised communities.

Another example is targeted advertising. AI-powered algorithms analyse data on individuals' online activities to deliver targeted ads, raising concerns about data privacy and the use of personal data for commercial gain.

Subject examples:

  • Humanities (Legal Studies): What is the impact of AI on data protection laws such as GDPR?
  • Mathematics: How can we analyse the data sets used by AI systems for potential biases and privacy issues?
  • Health and Physical Education: What are the privacy concerns surrounding personal health data and its use in AI-powered healthcare?

Data Collection and "Datafication"

"Datafication" is a term used for turning all parts of our life into a data point to be fed into an AI algorithm. From location data to health data, shopping habits, likes, clicks, and views, almost every interaction with technology is fed into an algorithm somewhere.

As we become commodities, we open ourselves up to exploitation. One major ethical concern with "datafication" is that users become the products, and the free-labour of the users is used to generate capital for the platform owners.

"Big Data" also contributes to many of the issues already described, including bias and discrimination. Because not everyone in the world has access to these technologies, that worldview is by definition missing some very important data.

Subject examples:

  • Humanities (History): What is the historical and societal context of data collection and the impact of datafication on different communities?
  • Design and Technology (Digital Technology): How can we design ethical data collection practices and data privacy measures?
  • English: How might we critically analyse datafication and its implications on privacy?

Advanced

Affect Recognition

Affect recognition means interpreting a person's emotions through their facial expressions, body language, speech patterns and actions. It is a controversial practice that has been widely criticised for poor research methodologies and inconsistent results.

Despite these controversies, affect recognition is an industry worth billions of dollars and has already made its way into education. A system named 4 Little Trees, developed in Hong Kong, claimed to be able to monitor children's facial expressions and assign labels for emotions such as "happy", "sad", and "angry".

Affect recognition is problematic on a number of levels. As well as the question mark over its accuracy, many people question whether emotions should be "datafied" at all. There are privacy concerns, and affect recognition technology can perpetuate discrimination.

Human Labour

The ethical concern of AI and human labour is a two-sided coin. On the one hand there are always fears that machine automation will replace jobs. On the other is the fact that current AI systems actually rely on a tremendous amount of dangerous, low-paid human labour.

Hidden beneath the rhetoric of the jobs AI will destroy is an unseen narrative of the jobs it currently requires to function. A recent article by Time magazine explored the harsh conditions of the Kenyan workers employed by OpenAI to label inappropriate data for its language model. Working for less than $2 an hour, these labourers were partly responsible for training an AI algorithm to identify harsh language, graphic, sexual, and violent content.

Subject examples:

  • Humanities (History): What is the history of the "robots taking our jobs" argument?
  • Humanities (Economics): What is the potential impact of AI on employment and the labour market?
  • Science (Psychology): What is the psychological impact of low-paid labor on workers and the potential for trauma?

Power and Hegemony

Because the data AI models are built on is "frozen in time", it represents a static worldview which encodes existing power and hierarchies in society. The reinforcement of the hegemony can further oppress and marginalise already disadvantaged people.

Think of AI as a self-perpetuating cycle. The datasets encode a certain power structure into the model, often the dominance of a heterosexual, white, Western, male perspective. This is then reflected in the output, which may be used to train future models.

AI also reinforces global hegemonies both in political and corporate terms. Countries and organisations need access to wealth, energy, and resources to successfully train and scale up AI models. This means that powerful AI is increasingly concentrated in the hands of those who already have the most.

Subject examples:

  • Humanities (Geography): Explore the global distribution of wealth, energy, and resources in relation to AI development.
  • Mathematics: Explore the statistical analysis of AI-generated data, including the detection and measurement of biases.
  • English and Literature: How can a Marxist critical perspective illuminate some of the problems of AI and power?

Ethical Perspectives, Approaches, and Frameworks

Ethical Perspectives

There are many ethical perspectives which could be applied to AI. Terry Bynum's principles of virtue ethics and human flourishing, translated from Aristotle into modern times, offer a useful starting point:

  1. Being happy and healthy is important for doing the right thing
  2. People need other people to be happy and healthy
  3. To be happy and healthy, people need to do what they are good at
  4. To know what is right, people need to learn and think for themselves
  5. Making good choices is key to doing what is right

Ethical Guidelines

Here are several examples of guiding principles used in AI ethics:

EU High-Level Expert Group on Artificial Intelligence:

  1. Human agency and oversight
  2. Technical robustness and safety
  3. Privacy and data governance
  4. Transparency
  5. Diversity, non-discrimination and fairness
  6. Social and environmental wellbeing
  7. Accountability

Google:

  1. Be socially beneficial
  2. Avoid creating or reinforcing unfair bias
  3. Be built and tested for safety
  4. Be accountable to people
  5. Incorporate privacy design principles
  6. Uphold high standards of scientific excellence
  7. Be made available for uses that accord with these principles

Australian AI Ethics Principles:

  1. Human-centred values
  2. Fairness
  3. Privacy protection and security
  4. Reliability and safety
  5. Transparency and explainability
  6. Contestability
  7. Accountability

"Ethics Washing"

Just because organisations develop AI Ethics or Responsible AI principles does not mean they follow them. Just like the "greenwashing" of environmental concerns, many AI companies have been accused of paying lip-service to ethical concerns. AI Ethics principles are often non-binding and cannot be enforced by law.

Conclusion

We are at a watershed moment in terms of Artificial Intelligence. Beyond the narrative of cheating and dramatic claims of the end of industrial-era education, AI offers real opportunities to personalise learning and help with differentiation, diversity, and inclusion. But there is a shadow side to AI which cannot be ignored.

Teaching AI Ethics: Suggested Activity

Activity Title: "Ethical Decision Making in AI"

  • Learning Intention 1: Students will understand the importance of ethical decision making in the development and implementation of AI technology.
  • Learning Intention 2: Students will be able to identify and analyse potential ethical issues in a given AI scenario.

Activity Description:

  1. Introduce the concept of ethical decision making in AI and its importance.
  2. Divide the class into small groups and provide each group with a different AI scenario (e.g. facial recognition in law enforcement, self-driving cars, AI-powered virtual assistants).
  3. Each group brainstorms potential ethical issues that may arise from the implementation of the AI technology in their scenario.
  4. Each group presents their findings to the class and engages in a class discussion.
  5. As a class, generate a list of best practices for ethical decision making in AI development and implementation.
  6. As an extension, students can research real-life cases where ethical issues have arisen in AI.

Resources and Further Reading

Books

  • Kate Crawford, Atlas of AI
  • Virginia Eubanks, Automating Inequality
  • Shoshana Zuboff, The Age of Surveillance Capitalism
  • James Barrat, Our Final Invention
  • Nick Srnicek, Platform Capitalism
  • Mark Andrejevic, Automated Media