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Fairness-enhancing classification methods for non-binary sensitive features-How to fairly detect leakages in water
Janine Strotherm1, Inaam Ashraf1, Barbara Hammer1
1Center for Cognitive Interaction Technology, Universität Bielefeld, Bielefeld, North Rhine-Westphalia, Germany.
Peerj. Computer Science
|December 9, 2024
Summary
Artificial intelligence (AI) in water distribution systems can be unfair. This study introduces new fairness definitions and a framework to ensure equitable AI decision-making in critical infrastructure like water systems.
Area of Science:
- Computer Science, Artificial Intelligence, Machine Learning
- Environmental Engineering, Water Resource Management
- Societal Impact of Technology, Ethics in AI
Background:
- AI-driven decisions increasingly impact societal infrastructure, raising concerns about fairness.
- Water Distribution Systems (WDSs) are critical infrastructure where AI applications are growing.
- Existing AI fairness metrics often fall short when dealing with complex, real-world scenarios.
Purpose of the Study:
- To investigate AI fairness in the context of Water Distribution Systems (WDSs).
- To propose novel, generalized definitions of group fairness applicable to WDSs and non-binary sensitive attributes.
- To develop and evaluate a framework for enhancing AI fairness in WDSs, particularly for leakage detection.
Main Methods:
- Definition of protected groups and generalized group fairness metrics for WDSs.
- Analysis of typical AI-based leakage detection methods in WDSs for fairness.
- Development of a general fairness-enhancing framework adaptable to various AI learning schemes.
- Empirical evaluation of the proposed framework on both synthetic and realistic WDS datasets.
Main Results:
- Typical AI methods for WDS leakage detection exhibit unfairness.
- The proposed generalized fairness definitions are shown to be robust and align with existing metrics for simpler cases.
- The fairness-enhancing framework demonstrably improves the equity of AI algorithms in WDS applications.
- Evaluations on toy and realistic WDS models confirm the practical utility of the proposed framework.
Conclusions:
- Ensuring fairness in AI applied to critical infrastructure like WDSs is crucial for societal equity.
- The developed generalized fairness definitions and the proposed framework offer a significant advancement in equitable AI for WDSs.
- This research provides a pathway to more just and reliable AI-powered management of essential water resources.
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