New York City’s Local Law 144 of 2021 requires employers and employment agencies that use certain hiring algorithms to commission an annual bias audit. Enforcement began on 5 July 2023, making it one of the earliest US measures to place concrete, testable obligations on AI used in employment. This article explains what the law covers and what the audit involves. It is informational and not legal advice.
What the law covers
Local Law 144 applies to automated employment decision tools (AEDTs) — broadly, computational tools that substantially assist or replace discretionary decision-making in hiring or promotion. Where an AEDT is used to screen candidates or employees for a position in New York City, the law imposes audit, notice and publication duties. The official guidance is published by the city’s Department of Consumer and Worker Protection (DCWP).
What the bias audit requires
At the centre of the law is the audit itself. Key features, as described in the city’s rules, include:
- The audit must be independent and impartial, conducted by an auditor that is not involved in using or developing the tool.
- It must be carried out no more than one year before the tool is used.
- It tests the AEDT for disparate impact — assessing how selection or scoring outcomes differ across categories such as sex, and race or ethnicity, and intersections of those categories.
- It typically reports metrics such as selection rates and impact ratios across groups, drawing on the tool’s historical data or test data.
The audit is descriptive: it surfaces and quantifies differences in outcomes rather than certifying a tool as fair or unfair. The result is a defined set of figures that must then be disclosed.
Notice and publication duties
Beyond the audit, the law imposes transparency obligations:
- Employers must publish a summary of the most recent bias-audit results, and the tool’s distribution date, in a clear and conspicuous place on their website.
- Candidates and employees who live in New York City must be notified at least ten business days before an AEDT is used, including notice of the job qualifications and characteristics the tool will assess.
These notice duties connect Local Law 144 to broader debates about disclosing the use of automated systems, a theme we track under generative-AI disclosure even though AEDTs are not necessarily generative.
Enforcement and penalties
The law is enforced by the DCWP. Reported penalty structures include a civil penalty for a first violation and escalating penalties for subsequent violations, with each day of non-compliant use potentially treated as a separate violation. The enforcement posture has been the subject of public scrutiny, including review of how actively the requirements are being enforced.
How it fits the wider landscape
Local Law 144 is narrow by design: it targets a specific use of AI — employment screening in a single city — rather than AI broadly. That makes it a useful contrast with comprehensive frameworks. Where the EU AI Act classifies employment AI as high-risk within a sweeping regime, Local Law 144 takes a single, audit-and-disclose mechanism and applies it precisely. Organisations operationalising bias testing often reference voluntary tools such as the NIST AI RMF and management standards like ISO/IEC 42001 to structure the surrounding governance, although neither defines the city’s specific audit requirements.
What “independent” and “impartial” mean here
The independence requirement is central to the law’s credibility, and the city’s rules address what disqualifies an auditor. Broadly, an auditor should not have been involved in using, developing or distributing the tool, and should not have a financial interest that would compromise objectivity. The practical effect is that the bias audit cannot simply be a vendor’s self-assessment; it must be carried out by a party with sufficient distance from the tool. This independence is part of what distinguishes a Local Law 144 audit from internal fairness testing that organisations may already perform.
The role of test data and small samples
An operational challenge the rules confront is what to do when an employer lacks sufficient historical data about a particular tool’s outcomes. The rules permit the use of test data in defined circumstances, and they address how to handle categories with too few data points to produce a meaningful figure. These provisions matter because the headline metrics — selection rates and impact ratios across groups — depend on having enough data to compute them reliably. The framework therefore acknowledges that an audit’s quality is bounded by the data available, a recurring theme in algorithmic-fairness measurement.
Why it matters
As one of the first laws to require a concrete, recurring, publicly disclosed algorithmic audit, Local Law 144 became a reference point in discussions about how to make AI accountability measurable. Its emphasis on independent testing, quantified disparate-impact metrics and candidate notice illustrates a disclosure-and-audit model distinct from outright prohibition. It also prompted debate about the limits of the model: critics asked whether disclosure of impact ratios alone changes employer behaviour, while supporters pointed to the value of forcing measurement and transparency where previously there was none. Readers new to terms such as disparate impact, impact ratio or selection rate may find plain-language explanations in our dictionary.
In summary
NYC Local Law 144 requires annual independent bias audits of automated employment decision tools, public disclosure of summary results, and advance notice to candidates. It is a targeted, audit-based approach to AI accountability in hiring. This article describes the requirements as published by the city; it is not legal advice, and employers should consult qualified advisers and the official DCWP guidance.