Module 1: Ethical theories for AI decisions

Module 1: Ethical theories for AI decisions#

Theme#

Ethical theories for AI decisions

Essential Question#

Which ethical lenses reveal different AI risks?

Module Components#

  • Book prose: conceptual framing, domain scenario, methods, and failure modes

  • Assignment: evidence-backed production of a specific artifact

  • Slides: presentation sequence for seminar or lecture delivery

  • Narration: spoken version of the slide flow

  • Rubric: criteria for evaluating the module artifact

  • Notebook: executable lab aligned with the module theme using synthetic governance case facts including affected groups, data rights, transparency controls, and harm scenarios

Module Artifact#

responsible AI review memo with risk register, policy analysis, and redress plan focused on ethical theories for ai decisions: Analyze a case with consequentialist, deontological, and justice frames.

Professional Setting#

Students work as if advising an AI governance board reviewing a proposed high-impact AI deployment. Their work must be intelligible to governance chair, legal counsel, affected-user advocate, product owner, and compliance officer.

Use This Module in Order#

  1. Read the learning chapter.

  2. Review the slide deck with the matching narration.

  3. In Populi, open the private student-repository link for this course and enter modules/module-1.

  4. Clone the repository once or open its Codespace/Colab copy; run lab.ipynb and complete exercise.ipynb there.

  5. Self-check with the rubric, commit and push the work, then submit exactly what Populi requests.