Prohibits the use of a wage-fixing algorithm in combination with personal or behavioral data to set or recommend wages or compensation; defines terms; establishes penalties for violations of such prohibition.
This bill would add a new section to the New York Labor Law prohibiting employers from using a “wage-fixing algorithm” together with personal data or behavioral data to set or recommend wages or compensation. The bill defines wage-fixing algorithms broadly to cover computational tools, machine learning, automated decision-making, and related data-processing methods used for wage-setting, while carving out tools that rely only on job requirements, job performance, qualifications, labor market conditions, or cost of living. It also defines personal data and behavioral data expansively, covering identifying information, financial and health information, online activity, protected-class characteristics, geolocation, biometric data, and inferred profiles.
The bill would create a private right of action and authorize enforcement by the Attorney General and the Labor Commissioner. Remedies include injunctive relief, actual damages or $3,000 per violation, treble damages for willful or egregious violations, disgorgement of profits, attorneys’ fees and costs, and other equitable relief. It also protects workers from retaliation and places the burden on employers to show that wage differences were based only on lawful factors. The act would take effect 30 days after becoming law.
If enacted, the bill would expand New York labor law by regulating employer use of algorithmic pay-setting tools and limiting the use of worker data in compensation decisions. It would affect employers, third-party vendors providing compensation software or analytics, and workers whose wages may be influenced by automated systems. The measure would also give state enforcement authorities and individual workers new tools to challenge unlawful wage-setting practices and seek monetary and injunctive relief.
Based on the bill text and available context, the measure appears to be framed as a worker-protection and transparency bill aimed at preventing discriminatory or opaque pay practices driven by automated systems. There is no recorded committee debate or vote history in the provided materials, so no formal legislative sentiment can be inferred beyond the bill’s protective purpose and enforcement-oriented design.
The main points of potential contention are the breadth of the algorithm definition, the wide scope of covered personal and behavioral data, and the burden-shifting provision requiring employers to prove wage differences were based only on lawful factors. Employers and technology vendors may view the bill as imposing compliance costs and limiting legitimate compensation analytics, while worker advocates are likely to support it as a safeguard against hidden bias, surveillance-based pay practices, and algorithmic discrimination. The exclusion for tools based on job requirements, performance, qualifications, labor market conditions, or cost of living may be an attempt to narrow that dispute, but the bill still leaves room for debate over what counts as permissible inputs and whether customer reviews should be excluded from job performance.