arXiv cs.LG论文
生成模型中的公平性失败是一个评估问题
本文提出，生成模型中的公平性失败本质上源于评估问题：现有公平性研究结果难以跨论文比较，也难以指导部署决策。作者诊断了当前实践中的常见失败模式，并主张从临时偏见检查转向标准化、生成模型专属的评估。他们提出“公平性卡片”作为最小报告工具，明确评估选择（提示族、反事实协议、指标和拒绝处理），以提高可复现性、可比性和问责性。
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Abstract:Despite groundbreaking advancements in generative models during the last decade, concerns about their lack of fairness, reinforcing societal inequalities and harming marginalized groups, remain under-addressed and difficult to act upon. This position paper argues that fairness failures in generative models, albeit driven by multiple factors, are ultimately stemming from an evaluation problem: fairness findings are rarely comparable across papers or actionable for deployment decisions. This paper diagnoses recurring empirical and conceptual failure modes in current practice and motivates a shift from ad-hoc bias checks to standardized, generative-specific evaluation. We propose Fairness Cards as a minimal reporting artifact that makes evaluation choices explicit (prompt families, counterfactual protocols, metrics, and refusal handling) enabling reproducibility, comparability, and accountability. We conclude with additional recommendations towards a paradigm shift in evaluation standards. Our project page can be found at this https URL .