Title: Discussion 7 — One Policy or Many? Tuning Hazard Categories per Deployment
Course: AIINFRA 302: AI Security, Guardrails & Governance > Section: Week 07 — Content Safety and Moderation with Llama Guard 4 and Rebuff

💬 Discussion 7 — One Policy or Many? Tuning Hazard Categories per Deployment (20 pts)

Imagine a medical-advice chatbot. Under the MLCommons taxonomy, giving detailed medical guidance would normally trip S6 (Specialized Advice) — content a general-purpose consumer chatbot should probably block or soften. But a legitimate clinical-support tool may need to allow exactly that kind of content to be useful at all. Post a response (150+ words) addressing:

- How should a team decide, category by category, which of the 14 MLCommons hazard categories to enable, soften, or fully permit for a specific deployment, rather than shipping one universal policy across every product surface?
- Who should own that decision inside an organization (engineering, legal/compliance, clinical/subject-matter experts, some combination), and why?
- What's one safeguard you'd want in place specifically because you loosened a category like S6 for this use case (e.g., audit logging, human-in-the-loop review, restricted user population)?

Then respond to at least two classmates — ideally engaging with a category or deployment scenario different from the one you wrote about.