Am I Being Treated Fairly? A Conceptual Framework for Individuals to Ascertain Fairness

2025-04-03

AI Summary

This study addresses the core challenge that individuals face in automated decision-making (ADM) systems: difficulty identifying discriminatory outcomes, obtaining meaningful explanations, and mounting effective appeals. Methodologically, it introduces the novel conceptual framework of *fairness attestation*, which—uniquely—defines fairness as an individual’s *cognitive right*, shifting the focus from system-level attributes to user-centered practices of comprehension, contestation, and verification. The approach integrates explainable AI (XAI), algorithmic fairness metrics, complaint redress design, and user empowerment tools, emphasizing interdisciplinary collaboration over isolated technical fixes. Key contributions include: (1) establishing a user-centered fairness paradigm grounded in epistemic agency; (2) proposing an actionable, process-oriented fairness attestation framework; and (3) delivering a practical blueprint for organizations to implement accountability mechanisms and align technical specifications with regulatory and policy requirements.

Technology Category

• Philosophy and Ethics of AI: Accountability, Interpretability & Explainability
• Humans and AI: Explainable AI (XAI) for Human Understanding
• Machine Learning: Ethics, Bias, and Fairness

Application Category

• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Economics, Online Markets and Human Computation: Fairness and ethical considerations in crowd work and in human-in-the-loop AI systems
• Responsible Web: Algorithmic accountability and transparency on the web

Abstract

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.

Problem

• Enable individuals to assess fairness of ADM decisions affecting them
• Shift focus from technical solutions to user-centered fairness mechanisms
• Bridge gap between algorithmic fairness and practical accountability

Innovation

• User-centric fairness framework for ADM systems
• Integrates explainable AI and contestability tools
• Empowers individuals to challenge unfair decisions