Title: Discussion 15 — Who's Accountable When AI Gets It Wrong?
Course: AIINFRA 100: AI Systems & Development Environments > Section: Week 15 — Reproducibility, Testing & Responsible AI — Final

💬 Discussion 15 — Who's Accountable When AI Gets It Wrong? (20 pts)

Pick a real-world AI use case you've read about — a hiring/résumé tool, a content-moderation system, a chatbot, or a recidivism-risk tool like COMPAS.

In your initial post, answer:

1. In your example, who should be accountable when the system produces a harmful or biased outcome — the organization that deployed it, the vendor that built it, the individual who approved its use, or some shared combination? Explain your reasoning.
2. Using this week's lecture, name one origin of bias (pre-existing, technical, or emerging) most likely at play in your example, and why.
3. Name one piece of the NIST AI RMF (Govern, Map, Measure, or Manage) that, if done well, might have caught the problem before it caused harm.

Respond to at least two classmates. Try to engage with a use case different from your own, and consider whether you'd assign accountability the same way they did.