发表机构
Johannes Gutenberg University（约翰内斯·古腾堡大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文构建了弥合算法形式公平与用户感知公平的跨学科框架，通过文献综合、研讨会及访谈，为算法公平评估与系统设计提供方法支撑。
AI 中文摘要
尽管公平已成为算法系统研究的核心关切，但该领域仍主要受计算机科学主导，导致对形式公平指标与偏差缓解策略的高度重视。然而，这种关注可能掩盖了一个根本挑战：公平不仅是技术属性，还是由认知启发法、心智模型、规范预期及社会技术因素塑造的主观、依赖情境的人类判断。关键在于，用户对公平的感知可能与算法形式上满足的公平标准存在显著差异；某系统可能符合预设的技术公平要求，但仍会受到受决策影响的利益相关者的不公正感知。在此类情况下，该系统在根本维度上失效：它将无法获得信任、接受或合法性。本文以用户中心设计视角，提出一个处于发展阶段的概念框架，将计算机科学的形式算法公平方法，与关于感知公平、信任及技术接受的规范与社会科学方法相弥合，并将二者嵌入塑造人类判断的社会技术条件中。该项目通过（1）理论文献综合、（2）跨学科研讨会及（3）利益相关者访谈，旨在为将计算公平审计与用户中心评估相结合的评估方法提供信息，并指导公平感知、以人类为中心的算法系统的设计，以支持受影响者做出知情、校准良好的公平判断。
英文摘要
While fairness has become a central concern in research on algorithmic systems, the field remains predominantly shaped by Computer Science, resulting in a strong emphasis on formal fairness metrics and bias mitigation strategies. Nevertheless, this focus may obscure a fundamental challenge: fairness is not merely a technical property, but a subjective, context-sensitive human judgment shaped by cognitive heuristics, mental models, normative expectations, and sociotechnical factors. Crucially, users' perceptions of fairness may diverge substantially from the fairness criteria an algorithm formally satisfies; a system may meet predefined technical fairness requirements yet still be perceived as unjust by decision-affected stakeholders. In such cases, the system fails on a fundamental dimension: it will not be trusted, accepted, or considered legitimate. Taking a user-centered design perspective, this paper presents a work-in-progress conceptual framework that bridges Computer Science approaches to formal algorithmic fairness with normative and Social Science fairness approaches regarding perceived fairness, trust, and technology acceptance, embedding both within the sociotechnical conditions that shape human judgment. Through (1) theoretical literature synthesis, (2) interdisciplinary workshops, and (3) stakeholder interviews, the project aims to inform evaluation approaches that integrate computational fairness audits with user-centered assessments and guide the design of fairness-aware, human-centered algorithmic systems that support informed, well-calibrated fairness judgments by those affected.
Comments7 pages, European Conference on Algorithmic Fairness
