adverse-impact

Bias-audit artifacts for automated employment decision tools: NYC LL144 selection/scoring rates and impact ratios, EEOC four-fifths adverse-impact flags, and a score-traceability schema.

Risk-tiered human oversight for AI hiring agents. Scores every agent action 0-100, routes it across four autonomy tiers, and evidences the human decision in a hash-chained append-only ledger. A four-fifths adverse-impact monitor runs on real recruitment data and auto-escalates flagged requisitions. Mapped to EU AI Act Art. 14/50 and NYC LL144.

California RIF Copilot is an Deterministic workforce restructuring platform that helps employers plan layoffs, analyze financial impact, flag WARN and adverse-impact risks, calculate final pay and severance, generate documents, and manage HR/legal approvals through one auditable California-focused system.

Open-source statistical engine for auditing AI employment decision systems: adverse impact analysis under the Uniform Guidelines (29 CFR 1607), name-swap bias testing, and model drift detection

Open, auditable implementations of the algorithms behind ATS resume screening and gamified hiring assessment. EEOC four-fifths analysis, NYC LL144 bias audits, counterfactual perturbation testing, and published psychometric task paradigms.

Gender pay gap regressions (R) on 71K Montgomery County, MD payroll rows, plus simulated RIF adverse impact and meal break exposure checks