Title: Discussion 12 — Choosing a Fairness Definition
Course: AIINFRA 302: AI Security, Guardrails & Governance > Section: Week 12 — Responsible AI, Bias, Fairness, and Model Cards

💬 Discussion 12 — Choosing a Fairness Definition (20 pts)

In this week's lecture and lab, you saw that demographic parity and equalized odds can conflict for the same model on the same dataset — improving one can worsen the other. That means someone has to decide which definition of fairness the system will optimize for. Post a response addressing:

- In a real organization, who should make that call — the ML engineers who understand the tradeoff mathematically, a legal/compliance team, an ethics board, affected community members, or some combination? Justify your answer.
- On what basis should the decision be made — legal requirements (e.g., disparate impact thresholds), the specific harms of false positives vs. false negatives in your use case, stakeholder input, or something else?
- Ground your answer in a concrete scenario (hiring, lending, healthcare triage, content moderation, or one from your own lab results) and be specific about what "fair" should mean there.

Respond to at least two classmates — ideally engaging with a scenario different from your own, and pushing back respectfully if you'd have made a different call in their situation.