让普通人也能质疑算法是否公平，提供可操作的维权框架。
Am I Being Treated Fairly? A Conceptual Framework for Individuals to Ascertain Fairness
- 构建用户可感知的公平性评估框架，支持个体主动追问决策公正性。
- 整合可解释性、问责制与申诉机制，使用户能验证和挑战算法决策。
- 为个人维权提供工具，适合政策制定者与系统设计者参考。
当前的公平性度量与缓解技术主要服务于实践者，用于评估自动决策系统（ADM）的非歧视程度。但作为直接面对决策结果的个体，我们如何判断自己是否被公平对待？本文探讨如何赋予个体提出这一问题的能力。我们主张将公平性不仅视为系统的属性，更应成为个体获取相关决策信息并据此申诉、寻求有效救济的知情权。通过融合算法公平性、可解释人工智能、问责制与可争议性等领域的核心概念，本文提出一个概念框架，整合多种工具以赋能最终用户。该框架将关注点从面向从业者的纯技术方案，转向支持个体理解、挑战和验证决策公平性的机制，同时为组织与政策制定者提供可落地的用户中心型问责蓝图。
原文摘要 · Abstract (English)
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.
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