Privacy & Information Security Law Blog

On April 6, 2023, the New York City Department of Consumer and Worker Protection ("DCWP") announced it adopted final rules to implement NYC’s Local Law 144 (“LL 144”) regarding automated employment decision tools (“AEDTs”). Enforcement of the law and the rules will begin on July 5, 2023.

Adoption of the final rules comes after the DCWP initially proposed and received a high volume of public comment on rules implementing LL 144, which imposes on employers and employment agencies certain requirements to provide notice and conduct a bias audit regarding AEDTs. The rules establish specifications for the bias audit to comply with requirements to report on impacts on race, ethnicity and sex to the Equal Employment Opportunity Commission.

Key highlights of changes in the adopted rules include:

• Expanding the scope of covered AEDTs by removing from the definition of “machine learning, statistical modeling, data analytics, or artificial intelligence” the requirement that, to be in-scope, they must involve inputs and parameters that are refined through cross-validation or by using training and testing data;
• Clarifying examples of where a bias audit may be required (e.g., where an employer wants to use an AEDT to screen resumes and schedule interviews for a job posting, an early point in the application process, even though the employer is not using the AEDT to make the final hiring decision);
• Clarifying that an employer or employment agency may rely on a bias audit conducted using the historical data of other employers or employment agencies only if the employer or employment agency provided historical data from its own use of the AEDT to the independent auditor conducting the bias audit or if the employer or employment agency has never used the AEDT; and
• Adding requirements that published results of a bias audit must include the number of applicants or candidates for all categories, the number of individuals the AEDT assessed that are not included in the calculations because they fall within an unknown category (e.g., an unknown race or ethnicity), and the selection or scoring rate of a category as applicable.