Module 2 Narration

Module 2 Narration#

Opening#

Open with the professional setting: an AI governance board reviewing a proposed high-impact AI deployment. Ask students what decision is being made, who is affected, and what evidence would be persuasive to a skeptical reviewer.

Middle#

Move through the module in four passes:

  1. Define Bias, fairness, and representational harm in the context of AI Ethics, Law & Policy.

  2. Walk through the lab as a proxy-data exercise, emphasizing what it can and cannot show.

  3. Compare a baseline with an AI-enabled or more sophisticated alternative.

  4. Translate the result into stakeholder language: recommendation, risk, mitigation, and next evidence.

Closing#

Close by returning to the 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.. Students should leave knowing exactly what artifact they are producing and how it will be judged.