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Teaching AI Ethics

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Teaching AI Ethics

And yet, as much as I enjoy working with the technology, it has many flaws. I think it’s 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 will explore nine ethical considerations ranging from “beginner” to “intermediate” and “advanced” levels.

Education and Professional DevelopmentSocial Sciences
Author
Leon Furze
Publisher
LibreTexts
Source
LibreTexts
License
CC BY-NC
Language
EN
Category
Basic Sciences
Read online Download PDF (open licence)
Contents· 51 sections
  1. 01: Teaching AI Ethics- Original Post
  2. 1.01: Beginner
  3. 1.02: Intermediate
  4. 1.03: Advanced
  5. 1.04: Ethical Perspectives Approaches and Frameworks
  6. 1.05: Conclusion
  7. 02: Teaching AI Ethics- Bias and Discrimination
  8. 2.01: Algorithmic Bias
  9. 2.02: Discrimination by Default
  10. 2.03: Case Study- Predictive Policing in the US
  11. 2.04: Teaching AI Ethics
  12. 03: Teaching AI Ethics- Environment
  13. 3.01: Extractive AI
  14. 3.02: The Hidden Costs of the Cloud
  15. 3.03: Case Study- The Carbon Cost of Training Large Language Models
  16. 3.04: Teaching AI Ethics
  17. 04: Teaching AI Ethics- Truth and Academic Integrity
  18. 4.01: Synthetic Mirages
  19. 4.02: Academic Integrity in the Age of AI
  20. 4.03: AI and Truth
  21. 4.04: Case Study- Language Models and the Spread of Fake News
  22. 4.05: Teaching AI Ethics
  23. 05: Teaching AI Ethics- Copyright
  24. 5.01: Copyright and Image Generation
  25. 5.02: Whats the big deal
  26. 5.03: Writing Film and Music
  27. 5.04: Case Study- Zarya of the Dawn
  28. 5.05: Teaching AI Ethics
  29. 06: Teaching AI Ethics- Privacy
  30. 6.01: Where does all that Data come from
  31. 6.02: Protecting Personal Privacy
  32. 6.03: Case Study- AI Defamation
  33. 6.04: Teaching AI Ethics
  34. 07: Teaching AI Ethics- Datafication
  35. 7.01: Case Study- The Datafication of Education
  36. 7.02: Where does all the Data go
  37. 7.03: Teaching AI Ethics
  38. 08: Teaching AI Ethics- Affect Recognition
  39. 8.01: What is Affect Recognition
  40. 8.02: Case Study- Microsofts Decision to Remove Emotion Recognition Services
  41. 8.03: Affect in Education
  42. 8.04: Teaching AI Ethics
  43. 09: Teaching AI Ethics- Human Labour
  44. 9.01: Case Study- OpenAIs Data Labelling
  45. 9.02: Anatomy of an AI System
  46. 9.03: Teaching AI Ethics
  47. 10: Teaching AI Ethics- Power
  48. 10.01: Understanding Hegemony
  49. 10.02: Connecting the Dots- Bias Environment Human Labour and Datafication
  50. 10.03: What about Pauses and Open Letters
  51. 10.04: What can be done about it