Bias in AI systems: integrating formal and socio-technical approaches.

ABSTRACT: Artificial Intelligence (AI) systems are increasingly embedded in high-stakes decision-making across domains such as healthcare, finance, criminal justice, and employment. Evidence has been accumulated showing that these systems can reproduce and amplify structural inequities, leading to ethical, social, and technical concerns. In this review, formal mathematical definitions of bias are integrated with socio-technical perspectives to examine its origins, manifestations, and impacts. Bias is categorized into four interrelated families: historical/representational, selection/measurement, algorithmic/optimization, and feedback/emergent, and its operation is illustrated through case studies in facial recognition, large language models, credit scoring, healthcare, employment, and criminal justi

SUBMITTER: Ahmad A 

PROVIDER: S-EPMC12823528 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature