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Teaching AI ethics: 9 essential articles and resources

Explore ethical concerns in AI with Leon Furze

Leon Furze, Consultant, author and PhD candidate /
10 April 2024

1 min read

Artificial Intelligence (AI) presents many complex ethical concerns which are well worth discussing with our students. Education consultant Leon Furze has you covered with these articles for AI education, from beginner to advanced.


The three articles at this level cover the most commonly discussed ethical concerns with AI: Bias, environmental concerns and “truth”. In these articles, you’ll find case studies and discussion points for each area.

Teaching AI Ethics: Bias and Discrimination

AI can undoubtedly be a valuable tool in education but it’s important for educators to understand the ethical concern.
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Teaching AI Ethics: Environment

Explore the impact of AI technologies on the environment and what AI developers are doing – or not doing – to mitigate those risks.
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Teaching AI Ethics: Truth and Academic Integrity

Explore both the AI tendency to fabricate information and the various ways which humans might misuse the technology.
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At this level, the concepts become more complex and the ethical issues harder to find information on, or more difficult to untangle. This level includes copyright, privacy and datafication, and offers ways for educators to explore how AI developers have collected intellectual property and data to train models in ways which don’t always protect users’ rights or privacy.

Teaching AI Ethics: Copyright

Copyright is a hugely contentious aspect of the current wave of AI, particularly in the field of AI image generation. As AI continues to advance, questions are cropping up about who owns the copyright to those works.
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Teaching AI Ethics: Privacy

The use of personal data in AI training data and the potential for data breaches and cyber attacks also pose significant privacy risks to individuals and organisations.
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Teaching AI Ethics: Datafication

Technology advances enable the collection, storage and analysis of vast amounts of data from nearly every aspect of our lives. While this process has led to numerous benefits such as improved efficiency of services, it also raises significant ethical concerns.
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These final three articles deal with the most complex ethical concerns: affect recognition, human labour and power. These articles explore how AI is already being used to recognise and evaluate human emotions and actions and how the processes of AI can reinforce societal prejudices and hegemonies. These posts offer discussion points and lesson activities to address these complex issues with students.

Teaching AI Ethics: Affect Recognition

One particularly controversial implementation of AI is affect or emotion recognition, which claims to interpret human emotions and mental states by analysing facial expressions, body language and speech patterns.
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Teaching AI Ethics: Human Labour

Explore the exploitation of human labour in AI development, including low paid workers used for categorising and labelling data and the impact of the AI infrastructure on human workers.
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Teaching AI Ethics: Power

Explore how the ethical concerns I’ve discussed throughout this series coalesce to reinforce and perpetuate societal power structures and how AI might contribute to an uneven distribution of wealth, freedom and power.
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Looking to upskill in AI?

Learn more about AI at one of our upcoming professional learning events with Leon Furze and Independent Schools Victoria.

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