Fairness-enhancing classification methods for non-binary sensitive features—How to fairly detect leakages in water distribution systems

Abstract

Especially if artificial intelligence (AI)-supported decisions affect the society, the fairness of such AI-based methodologies constitutes an important area of research. In this contribution, we investigate the applications of AI to the socioeconomically relevant infrastructure of water distribution systems (WDSs). We propose an appropriate definition of protected groups in WDSs and generalized definitions of group fairness, applicable even to multiple non-binary sensitive features, that provably coincide with existing definitions for a single binary sensitive feature. We demonstrate that typical methods for the detection of leakages in WDSs are unfair in this sense. Further, we thus propose a general fairness-enhancing framework as an extension of the specific leakage detection pipeline, but also for an arbitrary learning scheme, to increase the fairness of the AI-based algorithm. Finally, we evaluate and compare several specific instantiations of this framework on a toy and on a realistic WDS to show their utility.

Keywords

• Fairness
• Machine learning
• Fair machine learning
• Disparate impact
• Equal opportunity
• Leakage detection