Hiring, lending, and support triage make the policy discussion concrete.
Fairness / red-team probes / launch gates
AI Safety Audit Tool
The platform's release-integrity baseline now connects fairness metrics and scenario benchmarks to an implemented PyRIT adversarial gate: PASS, WARN, or BLOCK with redacted evidence, a control, and an owner.
- Cases
- 540/model
- Scenarios
- 3 domains
- Models
- 2 compared
- Decision
- Launch gate
- Decision
- Tie fairness gaps and red-team failures to PASS, WARN, or BLOCK launch verdicts.
- Why
- Policy language gave launch owners no threshold, evidence record, or accountable follow-up.
- Result
- A 540-case-per-model benchmark across three risk domains with baseline-versus-candidate comparison.
Safety needs to show up as a launch artifact.
Responsible AI often stops at policy language. The harder part is giving a launch review one place to inspect scenario data, fairness gaps, red-team probes, and a clear go/no-go verdict—with an owner, retained evidence, exception expiry, and remediation path.
Protected-group metrics appear as gaps the launch team can debate and defend.
The implemented harness captures three vulnerable-baseline successes and produces the correct hard BLOCK.
PASS, WARN, and BLOCK include reasons that can survive a launch meeting.
The benchmark has one job: show when to block a launch.
Gate verdict
Launch decision with reasons, not a vague risk label.
Segment gaps
Demographic parity, equal opportunity, and FPR gap visibility.
Baseline vs candidate
Regressions become visible before launch.
Audit artifact
The output can be shared in a launch review.
Fairness calculations and provider evidence stay separate.
The browser recalculates disparity metrics from deterministic sample rows or a validated local CSV. Run npm run ai-safety:bench -- --limit 540 in the source repository to reproduce the provider evidence summarized in the second tab, including the full source artifact hash. Red-team evidence remains in its dedicated campaign explorer.