Independent AEDT Bias Audit - NYC Local Law 144 (Disparate Impact / 4-Fifths Analysis)

This is time-sensitive: we need the completed audit by Saturday, June 21 (ET). The scope is intentionally small - we provide the full pseudonymized dataset and the methodology, so the work is primarily the short analysis and a short, signed report (not data wrangling). Please only apply if you can commit to the June 21 (ET) deadline.

How our platform works (so you can scope the methodology):

We run an online job-fair platform. An AI tool scores each candidate against each job (0–100) based on skills, experience, education, language, and location vs. the job requirements.

About the law (context)

NYC Local Law 144 requires that AI tools used to screen job candidates undergo an independent bias audit before use. The audit measures impact ratios (selection/scoring rates) across sex, race/ethnicity, and intersectional groups using the 4/5ths rule, and a summary is published.

If you've done EEOC adverse-impact analysis, this is the same statistical work applied to NYC's specific reporting format - prior LL144 experience is a plus but not required if your adverse-impact stats are strong.

https://www.nyc.gov/site/dca/about/automated-employment-decision-tools.page

https://www.osc.ny.gov/state-agencies/audits/2025/12/02/enforcement-local-law-144-automated-employment-decision-tools

What we provide

A pseudonymized dataset (no names/PII): candidate ref, sex, race/ethnicity (EEOC categories), match score - a few thousand rows.

Optionally, an open-source bias-audit engine you may use, or apply your own methodology.

What we need

Impact ratios by sex, race/ethnicity, and intersectional categories (4/5ths rule), aligned with DCWP/LL144 expectations.

Appropriate handling of small-sample subgroups; documented methodology, data source, and any excluded records.

A signed audit report naming you as the independent auditor of record, plus a publication-ready summary.

Ideal background: statistics / I-O psychology / EEO adverse-impact analysis / HR analytics. NYC LL144 familiarity a strong plus.

Example of the report that we are looking for

https://github.com/aclu-national/tracking-ll144-bias-audits

Dover: https://cdn.dover.io/compliance/Dover%20-%20Audit_Result.pdf

Bloomberg: https://assets.bbhub.io/company/sites/51/2023/07/20230703-BLP-Bias-Audit-for-AEDT.pdf

SmartAssistant: https://perma.cc/YZ8T-CK5M

Zoominfo: https://perma.cc/YHV4-XVFC