# Syllabus: AINS6005 AI Ethics, Law & Policy

## Catalog Description

Examines ethical, legal, privacy, fairness, transparency, and governance obligations for AI.

## Course Structure

Each week includes readings, a lecture/slide sequence, an executable lab, and an applied deliverable. Students maintain a reproducible project record and submit work through the LMS or GitHub workflow selected by the instructor.

## Weekly Schedule

| Week | Topic | Essential Question | Deliverable |
|------|-------|--------------------|-------------|
| 1 | Ethical theories for AI decisions | Which ethical lenses reveal different AI risks? | Lab notebook + assignment brief |
| 2 | Bias, fairness, and representational harm | How do datasets and objectives encode inequity? | Lab notebook + assignment brief |
| 3 | Privacy, consent, and data rights | What permissions are required to use data responsibly? | Lab notebook + assignment brief |
| 4 | Transparency, explainability, and accountability | Who needs to understand what, and when? | Lab notebook + assignment brief |
| 5 | AI law and emerging regulation | How do legal duties constrain system design? | Lab notebook + assignment brief |
| 6 | Governance programs and controls | What organizational controls make responsible AI repeatable? | Lab notebook + assignment brief |
| 7 | Incident response and redress | How should institutions respond when AI causes harm? | Lab notebook + assignment brief |
| 8 | Responsible AI policy portfolio | What evidence shows a system is governable? | Lab notebook + assignment brief |

## Assessment

| Component | Weight |
|-----------|--------|
| Weekly labs and notebooks | 30% |
| Applied assignments | 35% |
| Participation and technical critique | 15% |
| Final synthesis portfolio | 20% |

## Graduate Expectations

Submissions must show technical reasoning, evidence awareness, clear limitations, and responsible use of AI assistance. Code and analysis should be reproducible enough for instructor review.