ASSEMBLY SCIENCE, INNOVATION AND TECHNOLOGY COMMITTEE

STATEMENT TO

ASSEMBLY COMMITTEE SUBSTITUTE FOR

ASSEMBLY, No. 3855

STATE OF NEW JERSEY

DATED:  MAY 16, 2024

      The Assembly Science, Innovation and Technology Committee reports favorably an Assembly Committee Substitute for Assembly Bill No. 3855.

      As substituted and reported, this bill provides standards for the use of an independent bias audit if an employer elects to use an automated employment decision tool (AEDT) for an employment decision. 

      The bill defines AEDT to mean a machine-based system that can, for a set of human-defined objectives provided by an employer or an individual acting on behalf of an employer, make predictions, recommendations, or decisions influencing recruitment, workforce, or employment decisions.

      The bill defines “bias audit” to mean an impartial evaluation conducted by an independent auditor, including but not limited to:

      a.   rigorous assessment of an AEDT to determine its impact on persons of any category, including race, color, national origin, ethnicity, sex, gender identity, sexual orientation, age, religion, marital or familial status, disability, and deriving income from any public assistance program;

      b.   identification and documentation of any biases, risks, or potential discriminatory outcomes that arise from the AEDT’s design, implementation, or use; and

      c.   clear actionable recommendations to avoid, manage, or mitigate, identified biases and risks, and to ensure the sage, secure and trustworthy use of the AEDT in employment decisions.

      Under the bill, an employer or employment agency is prohibited from using or continuing to use and AEDT if more than one year has passed since the most recent bias audit of the AEDT.

      The bill includes minimum requirements for bias audits.

      The bill also requires bias audits to use training data of the AEDT except in certain circumstances and provides requirements for when an individual employer or employment agency may use training data of other employers or employment agencies for the bias audit.