Risks With AI and Employment in a Fragmented Regulatory Landscape

According to a Berkshire firm blog, the rapid growth of AI and employment — including recruiting, assessments, performance management, and monitoring — is prompting organizations to rethink compliance throughout the entire employment cycle. The discussion highlights an increasingly complex environment where federal civil rights laws still govern enforcement, while state and local regulations introduce new requirements for transparency, audits, and reporting. This provides context for understanding where compliance risks originate and how employers can develop defensible processes before adopting or expanding AI-driven HR tools.

Due diligence remains essential as litigation activity rises. Employers must ensure vendors offer significant transparency, provide access to underlying methodologies, and support independent validation. Testing tools with internal data before deployment is crucial to verify if a system performs as claimed.

The authors also highlight that bias audits are now expected in multiple jurisdictions, and internal teams need to coordinate to detect when data shifts, job changes, or algorithm updates require reassessment. A formal governance framework involving legal, HR, data science, and I/O psychology can be a practical way to oversee the lifecycle. Validation should be tailored to each organization’s specific context, and the blog notes that no legitimate Equal Employment Opportunity Commission (EEOC) certification or Office of Federal Contract Compliance Programs (OFCCP) approval exists for AI tools.

Compliance teams should view systems involving AI and employment as evolving, high-stakes tools that demand ongoing monitoring, context-specific validation, and strict governance. The key message for employers is clear: thorough documentation, transparent vendor relationships, and regular audits are the strongest defenses in a changing regulatory landscape.