研究不完整性别信息下的公平性，突破传统偏见缓解的局限。
AI Fairness Beyond Complete Demographics: Current Achievements and Future Directions
- 提出不完整人口属性下公平性的新分类体系
- 总结现有方法应对真实场景数据缺失问题
- 适合关注伦理与实际落地的AI研究者参考
人工智能（AI）中的公平性问题日益受到关注，因其在基于AI的决策系统中可能导致歧视性结果。尽管已有多种方法用于缓解偏见，但大多数依赖完整的性别、种族等人口统计信息，而这一假设在法律限制和避免强化歧视的风险下往往不切实际。本文综述了在人口属性不完整情况下的AI公平性研究，填补了传统方法与现实挑战之间的空白。我们提出了该情境下公平性概念的新分类体系，阐明了各类概念间的关联与差异。同时，系统总结了现有促进不完整人口信息下公平性的技术，并指出尚未解决的关键研究问题，以推动该领域的进一步发展。
原文摘要 · Abstract (English)
Fairness in artificial intelligence (AI) has become a growing concern due to discriminatory outcomes in AI-based decision-making systems. While various methods have been proposed to mitigate bias, most rely on complete demographic information, an assumption often impractical due to legal constraints and the risk of reinforcing discrimination. This survey examines fairness in AI when demographics are incomplete, addressing the gap between traditional approaches and real-world challenges. We introduce a novel taxonomy of fairness notions in this setting, clarifying their relationships and distinctions. Additionally, we summarize existing techniques that promote fairness beyond complete demographics and highlight open research questions to encourage further progress in the field.
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