# AINS6005: AI Ethics, Law & Policy

**Aurnova MSAI track:** Core  
**Credits:** 3  
**Format:** 8-week online graduate course

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

This course follows the Aurnova/Castalia course-site pattern used by AINS6003: each module includes book prose, an assignment notebook, slide notebook, narration, instructor notes, and an executable lab.

## Course Outcomes

By the end of the course, students will be able to:

- explain the major concepts and tradeoffs in AI Ethics, Law & Policy;
- build or evaluate applied AI artifacts aligned with the course domain;
- document assumptions, evidence, limitations, and operational risks;
- connect technical work to governance, stakeholder needs, and deployment readiness.

## Module Map

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