A Debate about When Algorithms Inherit Bias: Auditing AI Systems for Fairness

This research examines the systemic problem of algorithmic bias in critical fields like employment, finance, and criminal justice. It argues that AI tools are not neutral observers but sociotechnical artifacts that frequently inherit and amplify human prejudices through biased training data. Beyond outlining the legal and ethical risks of these failures, the research provides evidence-based strategies for organizations to improve fairness. These solutions include pre-deployment impact assessments, continuous monitoring, and the use of diverse, cross-functional teams to oversee system development. Ultimately, the research emphasizes that sustained governance and transparency are essential to prevent AI from entrenching structural inequalities.

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