Labor and Employment

Employers are increasingly deploying artificial intelligence (AI) and data-driven tools in performance management in an effort to promote consistency and reduce human bias. Yet these systems inherit the limitations of the data that fuels them, and workplace performance data is rarely neutral. Importantly, AI models are only as reliable as the information on which they are trained, and when that information reflects historical inequities or includes data correlated with protected characteristics, AI-driven performance metrics may perpetuate patterns that have long disadvantaged certain groups of employees.

Performance data also frequently lacks critical context. Metrics such as output volume, response times, or client feedback often fail to account for legitimate sources of variation, including disability accommodations, intermittent or protected leave, caregiving responsibilities, or differences in job assignments. AI systems generally struggle to recognize these nuances, even though performance evaluations commonly inform high-stakes decisions involving promotions, compensation, and terminations. When adverse employment actions are grounded in incomplete or misleading data, employers may find it difficult to defend those decisions, particularly where they have a disparate impact on members of protected classes.

The takeaway for employers is not to abandon AI, but to deploy it thoughtfully and lawfully. Employers should routinely audit performance data for bias, understand how AI tools weigh and interpret inputs, and train managers to assess AI-generated insights critically rather than accept them at face value. And always remember: human oversight remains essential.