A Unifying Human-Centered AI Fairness Framework

• URL: http://arxiv.org/abs/2512.06944v1
• Date: Sun, 07 Dec 2025 17:52:38 GMT
• Title: A Unifying Human-Centered AI Fairness Framework
• Authors: Munshi Mahbubur Rahman, Shimei Pan, James R. Foulds,
• Abstract summary: We introduce a unifying human-centered fairness framework that covers eight distinct fairness metrics.<n>Rather than privileging a single fairness notion, the framework enables stakeholders to assign weights across multiple fairness objectives.<n>We show that adjusting weights reveals nuanced trade-offs between different fairness metrics.
• Score: 2.9385229328767988
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The increasing use of Artificial Intelligence (AI) in critical societal domains has amplified concerns about fairness, particularly regarding unequal treatment across sensitive attributes such as race, gender, and socioeconomic status. While there has been substantial work on ensuring AI fairness, navigating trade-offs between competing notions of fairness as well as predictive accuracy remains challenging, creating barriers to the practical deployment of fair AI systems. To address this, we introduce a unifying human-centered fairness framework that systematically covers eight distinct fairness metrics, formed by combining individual and group fairness, infra-marginal and intersectional assumptions, and outcome-based and equality-of-opportunity (EOO) perspectives. This structure allows stakeholders to align fairness interventions with their values and contextual considerations. The framework uses a consistent and easy-to-understand formulation for all metrics to reduce the learning curve for non-experts. Rather than privileging a single fairness notion, the framework enables stakeholders to assign weights across multiple fairness objectives, reflecting their priorities and facilitating multi-stakeholder compromises. We apply this approach to four real-world datasets: the UCI Adult census dataset for income prediction, the COMPAS dataset for criminal recidivism, the German Credit dataset for credit risk assessment, and the MEPS dataset for healthcare utilization. We show that adjusting weights reveals nuanced trade-offs between different fairness metrics. Finally, through case studies in judicial decision-making and healthcare, we demonstrate how the framework can inform practical and value-sensitive deployment of fair AI systems.

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