Local Law 144

What Local Law 144 Means for Hiring Technology

Local Law 144 is not just a disclosure rule, it changes whether an automated employment decision tool can be used at all without process controls. The law ties compliance to the tool’s function, the place of use, and whether the output supports an employment decision, so organisations have to treat deployment context as part of the control surface.

That matters because the same model can be low risk in one workflow and regulated in another. A scoring, ranking, screening, or recommendation tool may become covered once it affects hiring decisions in New York City, which means governance must follow the use case rather than the vendor label.

What Counts as a Covered Automated Employment Decision Tool

For practitioners, the important point is that coverage turns on operational use, not marketing language. A tool may be built by a vendor as a general analytics product, yet still fall under the law if it helps make or materially shape an employment decision such as candidate selection, ranking, or screening.

The practical question is whether the system is being used to support a decision about employment. That is why inventorying models, templates, scoring services, and downstream decision workflows is essential, especially where HR teams, recruiters, and procurement owners may describe the same tool differently.

The law also creates a location-based trigger. Organisations therefore need to know where the tool is used, where the decision-maker is located, and whether the employment process touches New York City enough to activate the rule.

Bias Audits, Notices, and Independent Review

Local Law 144 requires an independent bias audit for covered use cases, which makes fairness testing part of the compliance model rather than an optional assurance exercise. NHI Management Group’s Explainable and Fair AI Guide is a useful companion for understanding why bias metrics, drift, and interpretation matter in operational settings.

The audit requirement is paired with notice obligations, so transparency is not satisfied by internal testing alone. Organisations need to align candidate-facing notices, vendor evidence, and audit documentation so that the review is not just technically sound, but also defensible when challenged by regulators or applicants.

Because the audit is independent, teams should not assume the vendor’s own testing is enough. The control expectation is that a separate reviewer can evaluate the tool’s outcomes against the required fairness test and the relevant employment workflow.

How Organisations Reduce Exposure and Prove Compliance

Compliance usually starts with mapping every hiring-related tool to the jurisdictions, decision points, and users involved. That inventory should include AI screening products, ranking engines, interview assist tools, and any service that materially influences a hiring decision, even if it is embedded in a broader platform.

Legal and HR teams should also keep a clear record of when a tool is first used, who approves its use, which roles are covered, and what evidence supports the bias audit and notice process. NHI Management Group’s NYC Automated Employment Decision Tools (AEDT) Law Guide explains the scope, audit expectations, and compliance details in one place.

For organisations tracking wider policy change, the US State AI Laws Tracker helps place Local Law 144 alongside other state and city AI rules that can affect hiring governance.

What Happens When Compliance Is Weak

Local Law 144 creates a failure mode where an organisation may believe it has a general AI governance process, but still lack the specific audit, notice, and scope controls the law requires. That gap is especially common when recruitment technology is procured centrally and deployed locally without a jurisdiction check.

Independent audit findings can also expose deeper process problems, such as unclear ownership of model approval, missing vendor evidence, or poor documentation of how the tool affects employment outcomes. NHI Management Group’s Global AI Regulation Map helps situate this law within the broader regulatory trend toward auditable AI oversight.

When organisations cannot show which tools are covered, where they are used, and how the audit evidence was produced, they risk both compliance failure and loss of trust in the hiring process.

Related resources from NHI Mgmt Group

• What is the difference between New York City Local Law 144 and the newer state-level AEDT bias audit approaches?
• What is the difference between New York City Local Law 144 and the New York State AEDT bills?
• What is the difference between an automated employment decision tool and a bias audit under Local Law 144?
• What is the difference between the EU AI Act and NYC Local Law 144 for HR teams?