A theoretical model of AI bias mitigation: incentives, regulation and equilibrium

Abstract

Purpose This study aims to develop a conceptual economic model to analyse bias in artificial intelligence (AI), decomposing it into functionality bias (arising from data and algorithms) and usage bias (stemming from user incentives). It explores how these biases interact and emerge endogenously in economic systems, proposing a cost-constrained optimisation framework for mitigation. Design/methodology/approach A mathematical optimisation model is formulated to minimise total bias, accounting for data curation costs, regulatory penalties and audit expenses. The model derives equilibrium conditions under which bias mitigation is cost-effective and identifies thresholds beyond which interventions fail. Findings Reducing usage bias enhances the effectiveness of functionality bias mitigation, creating a cascading fairness effect. High regulatory costs without incentive alignment can discourage mitigation, emphasising the need for balanced interventions. The model highlights the cost-fairness trade-offs and suggests critical thresholds for policy action. Research limitations/implications The theoretical conceptual model is static; future work should explore dynamic extensions and conduct empirical validation to enhance its applicability. Nonetheless, it provides a quantitative foundation linking AI ethics with economic policymaking. Practical implications Firms and policymakers can use the conceptual model to evaluate cost-efficient strategies for mitigating bias, particularly through early-stage interventions and the design of usage incentives. Social implications The study emphasises the importance of fairness in AI as both a technical and socioeconomic objective, essential for achieving equitable outcomes in areas such as finance, employment and public services. Originality/value Unlike prior approaches that treat fairness as exogenous, this study endogenises bias within an economic decision framework, presenting a novel lens to address AI fairness as a constrained optimisation problem.

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