REVIEW article
Front. Big Data
Sec. Machine Learning and Artificial Intelligence
Bias in AI Systems: Integrating Formal and Socio-Technical Approaches
Provisionally accepted
- New York University Abu Dhabi, Abu Dhabi, United Arab Emirates
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Artificial Intelligence (AI) systems have been 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 ine-quities, 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, manife-stations, and impacts. Bias is categorised 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 justice. Current mitigation strategies are critically evaluated, including dataset diversification, fairness-aware modelling, post-deployment auditing, regulatory frameworks, and participatory design. An integrated framework is proposed in which statistical diagnostics are coupled with governance mechanisms to enable bias mitigation across the entire AI lifecycle. By bridging technical precision with sociological insight, guidance is offered for the development of AI systems that are equitable, accountable, and responsive to the needs of diverse populations.
Keywords: Algorithmic bias, Fairness in machine learning, Ethical AI, Bias mitigation, socio-technical systems
Received: 15 Aug 2025; Accepted: 25 Nov 2025.
Copyright: © 2025 Ahmad, Vallès and Idaghdour. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Amar Ahmad
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