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.