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.
Audit sample decisions and compare model results.
Use the fairness audit to compare selection rates and error rates across groups in sample data or your own CSV. The model-comparison tab shows recorded results from a 540-case-per-model benchmark. Red-team results are available in the campaign explorer.