Compliance Stuff

Employers are increasingly using artificial intelligence and other algorithmic tools to support workplace decisions, including recruiting, screening, interviewing, promotion, workforce planning, and performance management. These tools can improve efficiency and consistency, but they also introduce important compliance, reputational, and employee-relations considerations. Two concepts that often arise in AI governance are bias audits and validation testing. Although related, they serve different purposes. A bias audit generally evaluates whether the use of a tool is associated with materially different outcomes across protected or demographic groups. Depending on the jurisdiction and the tool at issue, a bias audit may be legally required before use. For example, New York City’s automated employment decision tool law requires certain employers and employment agencies to obtain a bias audit within one year before using covered tools and to provide related notices and disclosures . And o...

On May 11, 2026, the Connecticut General Assembly passed Senate Bill 5 , and Governor Lamont is expected to sign it into law. The law is a comprehensive online safety law with significant requirements relating to Automated Employment-related Decision Technology (AEDT) . These AEDT requirements combine concepts from the current AI regulations in California and the European Union , taking a disclosure-focused approach that encourages, but does not impose, substantive pre-use design or audit mandates . It also innovates by creating a program for third-party risk assessments as a means to vet and certify AI models, but falls short of making such evidence broadly admissible to defend against AI model-specific claims. Broad Definition of Covered Technologies: The law defines the covered technology broadly. An AEDT is any system that processes personal data and produces outputs ( e.g ., predictions, scores, rankings, classifications, or recommendations) that are a “substantial factor” in ma...

A recent Gallup poll shows that only  31% of employees  feel actively engaged at work, with the majority of workers voicing concerns about whether their supervisor cares about them as a person, their lack of opportunity to learn and grow, and not understanding their company’s mission or purpose. Could AI technology hold the key to revival? This Insight will outline some of the ways that AI could assist your organization in connecting with your workforce, point out the risks, and then provide a playbook for how AI can aid in your employee engagement and retention efforts. AI Offers a Predictive Analytics Solution By using AI to leverage data analytics, employers can proactively identify the factors that contribute to resignations and quiet-quitting and implement strategies to retain talent. Some commonly analyzed factors include: Pay increases:  Analytics can show whether employees who receive regular pay increases are less likely to resign compared to stagnant compensatio...

California regulators recently adopted regulations regarding automated-decision systems (ADS) in the workplace, aiming to protect against employment discrimination given the dramatic rise in artificial intelligence use in employment. On March 21, the California Civil Council of the Civil Rights Department (CRD) voted to approve the rules, which now must be cleared by the Office of Administrative Law (OAL) and published by the Secretary of State. If they pass these final hurdles, they will likely become effective on July 1. Read on for key takeaways from the updated regulations and three steps you should take to stay compliant. Brief Background Employers are increasingly using AI tools during the employee lifecycle. They bring obvious advantages, such as saving time, processing efficiencies, and providing insightful data on people analytics . On the flipside, they can lead to potential discriminatory practices without proper oversight and governance. California leads the way in proposed...