Am I Being Treated Fairly? A Conceptual Framework for Individuals to Ascertain Fairness
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Abstract (arXiv)
Current fairness metrics and mitigation techniques provide tools for practitioners to asses how non-discriminatory Automatic Decision Making (ADM) systems are. What if I, as an individual facing a decision taken by an ADM system, would like to know: Am I being treated fairly? We explore how to create the affordance for users to be able to ask this question of ADM. In this paper, we argue for the reification of fairness not only as a property of ADM, but also as an epistemic right of an individual to acquire information about the decisions that affect them and use that information to contest and seek effective redress against those decisions, in case they are proven to be discriminatory. We examine key concepts from existing research not only in algorithmic fairness but also in explainable artificial intelligence, accountability, and contestability. Integrating notions from these domains, we propose a conceptual framework to ascertain fairness by combining different tools that empower the end-users of ADM systems. Our framework shifts the focus from technical solutions aimed at practitioners to mechanisms that enable individuals to understand, challenge, and verify the fairness of decisions, and also serves as a blueprint for organizations and policymakers, bridging the gap between technical requirements and practical, user-centered accountability.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 24 of its 63 references that are in the map. First posted on arXiv 3 Apr 2025.
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References · 24
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- 2021A Clarification of the Nuances in the Fairness Metrics Landscapecited by 98
12 more
- 2020On the Fairness of Causal Algorithmic Recoursecited by 69
- 2023Connecting the Dots in Trustworthy Artificial Intelligence: From AI Principles, Ethics, and Key Requirements to Responsible AI Systems and Regulationcited by 60
- 2020Evaluating and Mitigating Bias in Image Classifiers: A Causal Perspective Using Counterfactualscited by 56
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- 2019Equalizing Recourse across Groupscited by 51
- 2020Machine learning fairness notions: Bridging the gap with real-world applicationscited by 19
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- 2021Low-Cost Algorithmic Recourse for Users With Uncertain Cost Functionscited by 10
- 2020Revealing Neural Network Bias to Non-Experts Through Interactive Counterfactual Examplescited by 4
- 2021Fair and Responsible AI: A Focus on the Ability to Contestcited by 1
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