Whither Bias Goes, I Will Go: An Integrative, Systematic Review of Algorithmic Bias Mitigation

Bias in AI hiring tools is a multi-stage problem: The paper identifies four critical stages where bias can enter ML-based hiring systems—from data collection through deployment—meaning organizations cannot rely on fixing bias at just one point in the process.

"Fairness" means different things to different people: The review reveals that computer scientists, data scientists, and organizational researchers define and measure algorithmic bias differently, creating confusion about what "fair" hiring actually means and making it difficult to compare which solutions actually work.

Legal requirements exist but vary by region: The U.S. and European legal frameworks governing personnel selection differ significantly, suggesting that organizations using AI hiring tools globally need to understand multiple regulatory standards rather than applying a one-size-fits-all approach.

Many bias mitigation methods lack proven effectiveness: Despite numerous proposed solutions for reducing algorithmic bias, the systematic review identifies significant gaps in research demonstrating which methods actually work in practice, particularly for hiring and personnel assessment contexts.

Bias mitigation requires intentional action at every stage: The paper emphasizes that algorithmic bias won't disappear on its own—organizations must actively implement specific mitigation strategies during training data generation, model development, testing, and deployment to reduce the risk of perpetuating or exacerbating workplace inequality.