Null Compliance: NYC Local Law 144 and the Challenges of Algorithm Accountability
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Abstract (arXiv)
In July 2023, New York City became the first jurisdiction globally to mandate bias audits for commercial algorithmic systems, specifically for automated employment decisions systems (AEDTs) used in hiring and promotion. Local Law 144 (LL 144) requires AEDTs to be independently audited annually for race and gender bias, and the audit report must be publicly posted. Additionally, employers are obligated to post a transparency notice with the job listing. In this study, 155 student investigators recorded 391 employers' compliance with LL 144 and the user experience for prospective job applicants. Among these employers, 18 posted audit reports and 13 posted transparency notices. These rates could potentially be explained by a significant limitation in the accountability mechanisms enacted by LL 144. Since the law grants employers substantial discretion over whether their system is in scope of the law, a null result cannot be said to indicate non-compliance, a condition we call ``null compliance." Employer discretion may also explain our finding that nearly all audits reported an impact factor over 0.8, a rule of thumb often used in employment discrimination cases. We also find that the benefit of LL 144 to ordinary job seekers is limited due to shortcomings in accessibility and usability. Our findings offer important lessons for policy-makers as they consider regulating algorithmic systems, particularly the degree of discretion to grant to regulated parties and the limitations of relying on transparency and end-user accountability.
Key facts. Cited by 9 papers in the map (data through 7 Oct 2026). First posted on arXiv 3 Jun 2024.
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Citing papers per year: 2025: 4; 2026: 5.
9 papers.
- 2025Correlated Errors in Large Language Modelscited by 42
- 2026Formal Methods Meet LLMs: Auditing, Monitoring, and Intervention for Compliance of Advanced AI Systemscited by 3
- 2025Exposing the Illusion of Fairness: Auditing Vulnerabilities to Distributional Manipulation Attackscited by 2
- 2026Algorithmic Monocultures in Hiringcited by 2
- 2026Legal Infrastructure for Transformative AI Governancecited by 1
- 2025Fairness-in-the-Workflow: How Machine Learning Practitioners at Big Tech Companies Approach Fairness in Recommender Systemscited by 0
- 2025The Fair Game: Auditing & Debiasing AI Algorithms Over Timecited by 0
- 2026Compliant But Unsatisfactory: The Gap Between Auditing Standards and Practices for Probabilistic Genotyping Softwarecited by 0
- 2026How Supply Chain Dependencies Complicate Bias Measurement and Accountability Attribution in AI Hiring Applicationscited by 0
- 2026Algorithmic Monocultures in Hiring26 May 2026
- 2026Formal Methods Meet LLMs: Auditing, Monitoring, and Intervention for Compliance of Advanced AI Systems15 May 2026
- 2026How Supply Chain Dependencies Complicate Bias Measurement and Accountability Attribution in AI Hiring Applications24 Apr 2026
- 2026Compliant But Unsatisfactory: The Gap Between Auditing Standards and Practices for Probabilistic Genotyping Software13 Apr 2026
- 2026Legal Infrastructure for Transformative AI Governance1 Feb 2026
- 2025The Fair Game: Auditing & Debiasing AI Algorithms Over Time8 Aug 2025
- 2025Exposing the Illusion of Fairness: Auditing Vulnerabilities to Distributional Manipulation Attacks28 Jul 2025
- 2025Correlated Errors in Large Language Models9 Jun 2025
- 2025Fairness-in-the-Workflow: How Machine Learning Practitioners at Big Tech Companies Approach Fairness in Recommender Systems26 May 2025