Keeping up with Mobley vs Workday August 31, 2025

For employers who have been utilizing AI for talent acquisition, whether through a Human Capital Management system or recruiter, it is imperative you keep up with the class-action lawsuit, Mobley vs Worday. The suit is on track to potentially hold Workday AND their customers liable for violating the Age Discrimination in Employment Act (ADEA) of 1967. This is the first suit of its kind and will no doubt instruct the landscape around the use of AI in employment.

Start here for an easy read: https://www.hrdive.com/news/judge-allows-workday-avoid-disclosing-full-customer-list/758878/

Further, effective October 1, 2025, the California Civil Rights department will begin to enforce an amendment to the Fair Employment and Housing Act (FEHA) addressing automated decision-systems (ADS). The onus will be on employers and any agents (i.e. a recruiter or applicant tracking system embedded in a Human Capital Management system) to ensure the ADS is not making hiring decisions by discriminating against protected classes.

More information can be found here: https://calcivilrights.ca.gov/2025/06/30/civil-rights-council-secures-approval-for-regulations-to-protect-against-employment-discrimination-related-to-artificial-intelligence/

Human Resources Professionals’ Checklist for Bias-Vetting of Automated Decision-Systems (hiring using AI) August 31, 2025

Consider using the following questions for guiding your conversations with Human Capital Management/HRIS vendors and recruiters when creating your practices around utilizing AI for talent acquisition. I reached out to Alaric Scott, Jr., founder of EquiHire, specifically because he is not legal counsel or a HCM vendor; but instead he is is a AI/ML developer who understands exactly what is “under the hood” in the creation of various platforms.

This checklist helps HR professionals vet Applicant Tracking Systems (ATS) and Human Capital Management (HCM) software for bias. It also applies to job postings, performance reviews, and resume screening.

1.  Core Vendor Questions

•       What data was the AI trained on? Is it diverse and representative across gender, race, age, and ability?

•       How does the system test for and mitigate bias on an ongoing basis?

•       Do you rely solely on historical hiring data (which can reinforce past discrimination)?

•       How often do you update training datasets and bias prevention methods?

•       Does the system use anomaly detection to flag unusual rejection/selection patterns?

2.  Accountability & Liability

•       Who is accountable if bias is discovered — the employer, the vendor, or both?

•       Does your contract include language on shared liability for discriminatory outcomes?

•       What recourse does the employer have if bias is detected after implementation?

3.  Performance Management & Promotion

•       Does the performance management system feed into the ATS or hiring process?

•       How are ratings like 'meets expectations' or 'exceeds expectations' used in AI models?

•       Do review summaries introduce bias in compensation or promotions?

4.  Job Posting & Resume Language

•       Does the system flag gender-coded, age-coded, or ability-coded terms?

•       Are there neutral alternatives suggested to widen the talent pool?

•       Can recruiters export a 'bias-clean' draft of job postings before publishing?

5.  How EquiHire Helps

EquiHire AI provides a Job Post Bias Checker (Beta) that highlights coded terms across gender, age, and ability categories, suggests neutral replacements, and generates a bias-clean draft of job descriptions. This tool complements vendor accountability by giving HR professionals a transparent, hands-on way to detect and reduce bias immediately.

Disclaimer: This checklist is for educational purposes only and does not constitute legal advice. Always review hiring practices with legal counsel.