一句话：本文将软件公平性测试视为文化情境化实践，从指标、数据集和伦理三个维度批判其普遍主义假设。
软件公平性测试是评估AI系统的核心方法，但公平性的含义常被视为固定且普遍适用。这篇立场论文将公平性测试定位为文化情境化的实践，并从三个维度剖析该问题。第一，公平性指标编码特定文化价值观，边缘化其他价值观。第二，测试数据集主要基于西方情境设计，排除了口述传统、原住民语言和非数字社区的知识体系。第三，公平性测试引发伦理关切，包括依赖全球南方的低薪数据标注，以及大规模模型训练和部署的环境成本，这些对气候脆弱人群影响尤为严重。解决这些问题需要超越通用指标，转向尊重文化多元性的评估框架，并承认拒绝算法调解的权利。
Software fairness testing is a central method for evaluating AI systems, yet the meaning of fairness is often treated as fixed and universally applicable. This vision paper positions fairness testing as culturally situated and examines the problem across three dimensions. First, fairness metrics encode particular cultural values while marginalizing others. Second, test datasets are predominantly designed from Western contexts, excluding knowledge systems grounded in oral traditions, Indigenous languages, and non-digital communities. Third, fairness testing raises ethical concerns, including the reliance on low-paid data labeling in the Global South, and associated with this, the environmental costs of training and deploying large-scale models, which disproportionately affect climate-vulnerable populations. Addressing these issues requires rethinking fairness testing beyond universal metrics and moving toward evaluation frameworks that respect cultural plurality and acknowledge the right to refuse algorithmic mediation.
@inproceedings{paperbot12044,
title = {How Fair is Software Fairness Testing?},
author = {Ann Barcomb and Mariana Bento and Giuseppe Destefanis and Sherlock A. Licorish and Cleyton Vanut Cordeiro de Magalhães and Ronnie de Souza Santos and Mairieli Wessel},
booktitle = {ICSE},
year = {2026},
doi = {10.1145/3786581.3786926}
}