Fairness and bias in AI: a sociotechnical perspective

Abstract

Purpose This paper aims to advance a comprehensive sociotechnical framework for addressing fairness and bias in artificial intelligence (AI) systems, recognizing that purely technical solutions are insufficient to ensure equitable AI deployment across sectors such as hiring, lending and criminal justice. Design/methodology/approach This study critically evaluates existing technical solutions for mitigating bias, highlighting their limitations in addressing real-world sociocultural contexts. A sociotechnical framework that combines algorithmic techniques, human oversight, regulatory frameworks and stakeholder engagement is proposed. Findings This study presents a multi-component framework that integrates technical debiasing methods, stakeholder engagement, human oversight, regulatory compliance and continuous evaluation. The framework demonstrates that combining technical expertise, social science insights and diverse stakeholder perspectives leads to more effective bias mitigation and fairer AI systems. Research limitations/implications Although the framework provides a theoretical foundation, its practical implementation across different contexts and organizations requires further empirical validation. Future research should focus on measuring the effectiveness of this framework in real-world applications. Originality/value This paper advances the field by proposing a comprehensive sociotechnical framework that bridges the gap between technical and social approaches to AI fairness, providing practical guidelines for organizations while acknowledging the complexity of implementing fair AI systems.

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