Teaching AI Ethics

by Leon Furze

Read here Read at LibreTextsPDF

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.

Description from LibreTexts.

Subjects
Social Sciences, Education and Professional Development
Licence
CC BY-NC 4.0, as LibreTexts states it. Non-commercial use only.
Who can read
Teens profiles and older, judged from its title and subjects

Contents

LibreTexts writes equations in TeX, and here they show as TeX; the PDF has them typeset.

  1. Front Matter
  2. TitlePage
  3. InfoPage
  4. Table of Contents
  5. Licensing
  6. 1: Teaching AI Ethics- Original Post
  7. 1.1: Beginner
  8. 1.2: Intermediate
  9. 1.3: Advanced
  10. 1.4: Ethical Perspectives, Approaches, and Frameworks
  11. 1.5: Conclusion
  12. 2: Teaching AI Ethics- Bias and Discrimination
  13. 2.1: Algorithmic Bias
  14. 2.2: Discrimination by Default
  15. 2.3: Case Study- Predictive Policing in the US
  16. 2.4: Teaching AI Ethics
  17. 3: Teaching AI Ethics- Environment
  18. 3.1: Extractive AI
  19. 3.2: The Hidden Costs of the Cloud
  20. 3.3: Case Study- The Carbon Cost of Training Large Language Models
  21. 3.4: Teaching AI Ethics
  22. 4: Teaching AI Ethics- Truth and Academic Integrity
  23. 4.1: Synthetic Mirages
  24. 4.2: Academic Integrity in the Age of AI
  25. 4.3: AI and Truth
  26. 4.4: Case Study- Language Models and the Spread of Fake News
  27. 4.5: Teaching AI Ethics
  28. 5: Teaching AI Ethics- Copyright
  29. 5.1: Copyright and Image Generation
  30. 5.2: What’s the big deal?
  31. 5.3: Writing, Film, and Music
  32. 5.4: Case Study- Zarya of the Dawn
  33. 5.5: Teaching AI Ethics
  34. 6: Teaching AI Ethics- Privacy
  35. 6.1: Where does all that Data come from?
  36. 6.2: Protecting Personal Privacy
  37. 6.3: Case Study- AI Defamation
  38. 6.4: Teaching AI Ethics
  39. 7: Teaching AI Ethics- Datafication
  40. 7.1: Case Study- The Datafication of Education
  41. 7.2: Where does all the Data go?
  42. 7.3: Teaching AI Ethics
  43. 8: Teaching AI Ethics- Affect Recognition
  44. 8.1: What is Affect Recognition?
  45. 8.2: Case Study- Microsoft’s Decision to Remove Emotion Recognition Services
  46. 8.3: Affect in Education
  47. 8.4: Teaching AI Ethics
  48. 9: Teaching AI Ethics- Human Labour
  49. 9.1: Case Study- OpenAI’s Data Labelling
  50. 9.2: Anatomy of an AI System
  51. 9.3: Teaching AI Ethics
  52. 10: Teaching AI Ethics- Power
  53. 10.1: Understanding Hegemony
  54. 10.2: Connecting the Dots- Bias, Environment, Human Labour and Datafication
  55. 10.3: What about Pauses and Open Letters?
  56. 10.4: What can be done about it?
  57. Back Matter
  58. Detailed Licensing