提出公平性增强的集成分类方法，提升供水网络漏损检测的公平性。
Fairness-Enhancing Ensemble Classification in Water Distribution Networks
- 定义供水网络中的受保护群体与群体公平性标准
- 发现传统漏损检测方法存在不公平性
- 设计可适配非可微集成模型的公平性修复方案
如未来犯罪预测软件等案例所示，人工智能在社会领域决策支持工具中的公平性已成为重要研究方向。本文探讨人工智能在供水网络（WDNs）等社会经济关键基础设施中的应用，此类场景下的公平性问题尚未受到足够关注。为此，我们扩展现有定义，提出适用于WDNs的受保护群体与群体公平性概念。实验表明，典型的漏损检测方法在此标准下存在不公平现象。为此，我们进一步提出一种公平性增强方案，该方法可应用于非可微的集成分类模型，有效提升整体公平性。
原文摘要 · Abstract (English)
As relevant examples such as the future criminal detection software [1] show, fairness of AI-based and social domain affecting decision support tools constitutes an important area of research. In this contribution, we investigate the applications of AI to socioeconomically relevant infrastructures such as those of water distribution networks (WDNs), where fairness issues have yet to gain a foothold. To establish the notion of fairness in this domain, we propose an appropriate definition of protected groups and group fairness in WDNs as an extension of existing definitions. We demonstrate that typical methods for the detection of leakages in WDNs are unfair in this sense. Further, we thus propose a remedy to increase the fairness which can be applied even to non-differentiable ensemble classification methods as used in this context.
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