Jackson Lewis warns NYC AI monitoring audit risk

Law firm Jackson Lewis released a podcast and video warning U.S. employers that AI-powered employee monitoring tools can create legal exposure — including triggering New York City’s bias-audit and transparency obligations under Local Law 144.

Published Aug. 18, 2026, the discussion walks through how systems used to measure productivity, inform promotion or evaluate performance can produce adverse impacts that invite regulatory and enforcement scrutiny. Jackson Lewis flagged Local Law 144’s requirements for Automated Employment Decision Tools (AEDTs), and cautioned that monitoring platforms — even those not traditionally thought of as hiring software — can fall within the law’s ambit if they affect employment outcomes.

Jackson Lewis partners told listeners that the risk is twofold: privacy obligations around surveillance and data collection, and discrimination or disparate-impact claims if monitoring outputs correlate with protected characteristics. The firm emphasised that transparency duties under New York City law require employers to disclose the use of AEDTs and, in many cases, to conduct pre-deployment bias audits and make certain information available to workers.

Practically, Jackson Lewis urged employers to take an inventory of monitoring technologies in use — from productivity-tracking algorithms to camera-based or keystroke monitoring systems — and to assess whether those tools feed into decisions about hiring, firing, promotion or discipline. The podcast noted that even tools deployed for operational efficiency can have downstream effects on raises, promotions and termination decisions that expose employers to Local Law 144’s audit and disclosure obligations.

The warning follows a broader regulatory trend. Municipal and state-level rules like New York City’s have pushed automated decision tools into the compliance spotlight, while civil-rights enforcers and plaintiffs’ lawyers are increasingly focused on how algorithmic systems produce disparate outcomes. Jackson Lewis positioned its guidance against that backdrop, urging companies to consider both the technical audit and the legal framing of monitoring programs.

The firm discussed mitigation steps — such as conducting fairness testing, documenting data provenance and building governance processes — but the podcast did not include a standardized audit template, cost estimates for compliance, or a checklist of objective thresholds that determine when a monitoring tool becomes an AEDT subject to Local Law 144. Jackson Lewis recommended working with counsel and vendors to tailor audits, but did not identify independent certifiers or set out vendor-contract language employers should demand.

Jackson Lewis also noted enforcement is not limited to local AI-audit rules: existing federal and state civil-rights statutes can apply where monitoring systems have disparate impacts on protected groups. That dual pathway — regulatory audit plus traditional anti-discrimination enforcement — was a recurring theme of the conversation and the reason the firm framed the issue as a cross-cutting legal risk rather than a narrow technical problem.

For HR leaders and in-house counsel, the message is pragmatic: procurement and vendor-management practices that once focused on uptime and integration now need to account for fairness testing, disclosure obligations and privacy controls. As employers scale surveillance and productivity tools, the interplay between municipal AI-audit laws and long-standing employment protections will increasingly shape vendor selection, onboarding and the documentation that supports personnel decisions.