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Bryq Compliance according to NYC Local Law 144 (Adverse Impact / AI Bias Law)
Bryq Compliance according to NYC Local Law 144 (Adverse Impact / AI Bias Law)

This article outlines the understanding & interpretation of Bryq's obligations to the NYC Bias Audit Law

Markellos Diorinos avatar
Written by Markellos Diorinos
Updated over a year ago

Background

The New York City Bias Audit Law, enacted in 2021 and set to be enforced from July 5, 2023, has significant implications for AI and automation in talent management. It mandates that employers and employment agencies utilizing automated employment decision tools (AEDTs) to assess candidates for employment or employees for promotion in New York City must obtain an annual independent and impartial bias audit.

This law was introduced by the New York City Council in 2020 and was initially scheduled to take effect on January 1, 2023. However, due to an extensive consultation process on the proposed rules by the Department of Consumer Protection (DCWP), the enforcement date was first postponed to April 15, 2023, and ultimately pushed back to July 5, 2023, when the DCWP's adopted rules will be enforced. In this blog post, we will outline four key steps to help organizations prepare for compliance with the law.

Is Bryq classified as an AEDT and does this new law apply?

Local Law 144 applies to computational processes derived from machine learning, statistical modeling, data analytics, or artificial intelligence. These processes produce simplified outputs like scores, classifications, or recommendations that substantially aid or replace discretionary decision-making in employment decisions.

According to the rules, substantial assistance or replacement of decision-making occurs when:

i) the simplified output is relied upon exclusively without considering other factors,

ii) the simplified output is weighted more heavily than any other factor within a set of criteria, or

iii) the simplified output overrides conclusions drawn from other factors, including human decision-making.

Machine learning, statistical modeling, data analytics, and artificial intelligence encompass mathematical, computer-based techniques that generate predictions (e.g., candidate assessment or likelihood of success) or classifications (e.g., grouping individuals based on skills or aptitude). The computer identifies inputs, assigns relative importance to those inputs, and adjusts model parameters to improve prediction or classification accuracy.

Even if these tools are not the primary or sole criteria, they can still have significant unintended consequences if biased. Therefore, obtaining a bias audit is advisable, even if the criteria are not entirely met.

πŸ’‘ As a result, the law applies to you if you use Bryq's AI-powered automated candidate ranking functionality within your hiring process.

Is Bryq collaborating with an independent auditor to produce the bias audit report?

Yes, we have contracted Holistic AI, a leading AI audit and risk management firm. A Summary Results report produced on May, 15th of 2023 is available to customers upon request.

Are there any exemptions to the NYC Bias Audit Law?

The NYC Bias Audit law applies to all employers within New York City and does not provide any exemptions based on the size of the organization or its revenue. Therefore, all employers, regardless of their size or type, are required to comply with the law and conduct a bias audit of their workplace practices. This includes conducting a comprehensive analysis of hiring, promotion, and retention policies and procedures to identify and address any potential biases that may exist. Failure to comply with the law may result in penalties and fines imposed by the New York City Commission on Human Rights. For more information and guidance on how to comply with the law, please refer to our NYC Bias audit solution page.

πŸ“Œ Have a look at our article about the Guidelines for Customers on New York City Local Law 144 of 2021 to guide you through your obligations regarding the Use of Automated Employment Decision Tools.

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