Labor & Employment Law Daily Wrap Up, EXPERT INSIGHTS—FP snapshot on manufacturing industry: mitigating AI bias risk in hiring and workforce management, (Jul 9, 2026)

By Michael D. Carrouth and Chase Dorn of Fisher Phillips, LLC

AI has rapidly become embedded in the employment lifecycle…

Production demands are up and you need to fill several key positions. At the same time, you need to quickly evaluate annual performance reviews to finalize merit pay increases. AI seems to be just the tool to get all this done efficiently and effectively, right? The Fisher Phillips AI, Data, and Analytics Team recently outlined 10 practical steps employers can take to mitigate bias and manage legal risk when using AI tools in hiring, promotion, and employee management. This Manufacturing Snapshot will review these steps as they relate to your manufacturing operations.

Snapshot Look at AI in the Workplace

AI has rapidly become embedded in the employment lifecycle—from résumé screening and video interview platforms to performance analytics and automated workforce scheduling. While these tools promise significant efficiency gains, they carry real legal exposure.

Employers who deploy AI tools without proper vetting, ongoing monitoring, and human oversight risk disparate impact claims, regulatory scrutiny under a rapidly expanding patchwork of state and local laws, and potential liability under federal anti-discrimination statutes. For a deeper dive into the situation, you can read our comprehensive article here.

What Do Manufacturers Need to Know?

Here are three key takeaways specifically developed for manufacturers when it comes to the use of AI during hiring, promotion, and performance management.

ATS Screening on the Shop Floor

For manufacturers trying to fill production line roles—from machine operators and welders to quality control technicians and maintenance mechanics—AI-powered applicant tracking systems (ATS) have become a go-to tool for managing high-volume hiring. But manufacturers need to be especially vigilant that the data features these tools rely on to rank or screen candidates are genuinely tied to actual job performance. An ATS model trained predominantly on data from white-collar or service-sector workforces may inadvertently deprioritize qualified candidates for skilled trades positions, creating disparate impact exposure across protected classes.

Manufacturers should demand full transparency from AI vendors about exactly which data points drive screening decisions. Once you have that, validate those features against the real physical, technical, and safety requirements of each role in your facility. Avoid bloated job postings that mix “must-haves” with “nice-to-haves,” as this creates data noise that AI systems can misuse, potentially weighing irrelevant factors like education pedigree over hands-on trade credentials.

Staffing Partners and the Flex-Workforce Challenge

Manufacturers might heavily rely on staffing agencies and contract labor to flex their headcount, which is a common practice when ramping up for production surges, managing seasonal demand, or supporting just-in-time manufacturing models. But if you do so, you could face a particularly layered governance challenge around AI tools.

When a third-party staffing vendor deploys its own AI screening platform to supply workers to your facility, the legal question of who bears responsibility for AI-driven bias doesn’t disappear simply because the hiring decision originated upstream in the supply chain.

Manufacturers should ensure that any staffing partners or workforce management platforms they engage can demonstrate compliance with applicable automated decision-tool (ADT) regulations, provide audit access, and agree to strict data use and retention limits. This is particularly true as states like New York, California, Illinois, and Colorado continue to enact or expand AI employment laws.

Performance Analytics, Biometric Tools, and Human Oversight

The risk doesn’t stop at the hiring gate. Many manufacturers now deploy AI-driven performance analytics to monitor output metrics, quality defect rates, machine utilization, and attendance patterns. That data can be used to inform promotion, discipline, and even termination decisions on the production floor. But when these systems are not properly validated and continuously monitored, they can introduce or amplify bias in ways that are difficult to detect until significant legal exposure has already been created.

Additionally, manufacturers who use video-based tools for remote interviews or onboarding should be aware that platforms capturing facial movement, voice inflection, or other biometric-adjacent signals trigger specific state notice, consent, and audit requirements, including under Illinois’s AI Video Interview Act.

Want More?

We will continue monitoring workplace law developments as they apply to manufacturers, so make sure you are subscribed to Fisher Phillips’ Insight System to have the most up-to-date information sent directly to your inbox. If you have questions, contact your Fisher Phillips attorney, the authors of this Insight, or any attorney on our Manufacturing Industry Team.

Michael D. Carrouth and Chase Dorn

Fisher Phillips, LLC

URL: https://www.fisherphillips.com/

Attorneys: (Fisher Phillips).

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