Module 2 Overview

Theme

Bias, fairness, and representational harm

Essential Question

How do datasets and objectives encode inequity?

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

• Instructor notes: facilitation plan, discussion prompts, and grading cues

• 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 bias, fairness, and representational harm: Audit a model scenario for bias pathways and mitigations.

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.