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