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

## TL;DR

This paper reviews how AI systems can reproduce bias and proposes an integrated approach combining technical and social strategies to address it across the AI lifecycle.

## Contribution

The paper introduces an integrated framework combining formal bias definitions with socio-technical perspectives to mitigate bias throughout the AI lifecycle.

## Key findings

- Bias in AI systems can be categorized into four interrelated families: historical/representational, selection/measurement, algorithmic/optimization, and feedback/emergent.
- Current mitigation strategies include dataset diversification, fairness-aware modeling, and participatory design, but require integration with governance mechanisms.
- An integrated framework is proposed to enable bias mitigation across the entire AI lifecycle through statistical diagnostics and governance.

## 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 justice. Current mitigation strategies are critically evaluated, including dataset diversification, fairness-aware modeling, 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.