We monitor adverse impact across five pairs: sex (female / male), race (non-white / white), age (over-40 / under-40), education (non-graduate / graduate), and disability disclosure. Categories are voluntary, declared by the candidate post-decision (so the disclosure cannot influence the decision), and used only for aggregate adverse-impact monitoring.

• ●Voluntary self-identification. Candidates can decline every question.
• ●Post-decision only. Disclosure is captured after the candidate has been advanced or rejected.
• ●Aggregate only. Per-role and per-stage aggregates. Never used as an input to scoring.
• ●4/5ths rule is the regulatory floor (EEOC). We report against tighter thresholds where the role volume supports it.

Independent bias audits are required under NYC Local Law 144 for any automated employment decision tool used by NYC employers. We commission an annual audit by Holistic AI, an independent third party, and publish the summary in full. The 2026 audit is referenced above.

The audit covers selection rate ratios across protected categories, item-level differential item functioning (DIF) on assessment items, calibration of scores against post-hire outcomes, and inspection of the question bank for content bias. The auditor has access to anonymised production data, not customer-identifying data.

• ●Annual cadence, summary published on this page.
• ●NYC LL144 banner on every NYC-listed role, linking to the latest summary.
• ●Audit scope, methodology, and limitations disclosed in full.