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🏛️ Governance

Local Law 144: Bias Audits for Hiring Algorithms

Adopted in 2021, the law requires employers that use an automated tool to screen job candidates or promote staff to commission an annual independent bias audit, publish a summary of its selection and scoring rates by sex and race/ethnicity, and notify candidates ten business days beforehand. Enforcement began on 5 July 2023, six months after the law formally took effect. Researchers who checked 391 employers that autumn found 18 published audit reports. A December 2025 state comptroller audit called enforcement ineffective: two complaints in two years, and a city sweep of 32 employers that flagged one likely violation where state auditors found 17.

Live 📅 since 2021 🤖 Uses AI ✔ Editor-reviewed

✔ Reviewed by the 1001 Smart Cities editorial desk · 🔎 Last verified

📊 Impact

18 of 391 employers checked had posted a bias audit report and 13 a transparency notice (Wright et al., FAccT 2024; fieldwork October-November 2023)

🎓 Lesson

Requiring an audit is not the same as enforcing one. When employers decide for themselves whether their tool is in scope, choose and pay their own auditor, and the regulator waits for complaints, almost no audits appear.

Evidence🔬 Peer-reviewed
Scale🏙️ City-wide

Sources

↗ NYC Department of Consumer and Worker Protection — Automated Employment Decision ToolsThe regulator's own page: enforcement begins 5 July 2023; the tool must have had a bias audit within one year of use, information about the audit must be publicly available and a summary of results posted, and notice must be given 10 business days before an AEDT is used.
↗ NYC Rules — Automated Employment Decision Tools (DCWP rulemaking)The implementing rule: defines a covered tool as machine learning, statistical modelling, data analytics or artificial intelligence whose output is used to substantially assist or replace discretionary decision making, and sets out selection rate, scoring rate and impact ratio calculated by sex category, race/ethnicity category and intersectional categories.
↗ Wright et al., Null Compliance: NYC Local Law 144 and the Challenges of Algorithm Accountability (ACM FAccT 2024)Peer-reviewed source for the compliance figure: 155 student investigators recorded 391 employers between 24 October and 9 November 2023; 18 posted audit reports and 13 posted transparency notices. Also the source for the 'null compliance' problem, employers holding near-total discretion over whether their system is in scope. Published at FAccT '24, DOI 10.1145/3630106.3658998.
↗ Groves, Metcalf, Kennedy, Vecchione and Strait, Auditing Work: Exploring the New York City algorithmic bias audit regime (ACM FAccT 2024)The methodological counterweight, from interviews with auditors and practitioners: the law fails to define AEDTs and independent auditors clearly, rests on a flawed transparency-driven theory of change, and industry lobbying narrowed the definition of a covered tool. Also the source characterising LL 144 as the first algorithm auditing system for commercial machine-learning systems.
↗ Office of the New York State Comptroller — Enforcement of Local Law 144, Automated Employment Decision Tools (2 December 2025)Independent state audit covering July 2023 to June 2025: DCWP's system for enforcing the law is ineffective, only two AEDT-related complaints were received in the whole period, and the complaint intake process does not reliably route complaints to DCWP.
↗ New York State Comptroller press release on the DCWP audit (December 2025)Source for the specific enforcement numbers: DCWP reviewed 32 employers' and vendors' websites and identified one instance of likely non-compliance, while state auditors reviewing the same companies found 17; of 12 test calls to 311 only three reached DCWP.
📎 Cite this project

1001 Smart Cities (2026). “Local Law 144: Bias Audits for Hiring Algorithms” — New York City, United States. The Smart City Atlas. https://1001smartcities.org/projects/nyc-ai-hiring-audit-law/ (last verified 2026-07-26). Data: CC BY 4.0.

The underlying data is free to reuse with attribution — see the open data page.

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