FairnessBiasAccountabilitystat.ML
Fair Representation Learning for Continuous Sensitive Attributes using Expectation of Integral Probability Metrics
Insung Kong, Kunwoong Kim, Yongdai Kim
May 9, 2025
IEEE Transactions on Pattern Analysis and Machine Intelligence
10 citations
2 influential
Key Takeaways
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Existing fairness tools don't handle continuous characteristics well: Current fair AI algorithms work for categorical attributes (like gender or race) but struggle with continuous ones (like age or income), leaving a significant gap in how we can make AI systems fair across different types of sensitive information.
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A new measurement tool for fairness with continuous attributes: The paper introduces EIPM (Expectation of Integral Probability Metrics), a new way to measure whether an AI system's internal representations are fair when dealing with continuous sensitive attributes, and shows this measurement can be reliably calculated even with limited real-world data.
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Fair representations lead to fair predictions: The research demonstrates that if an AI system's internal representation space has low EIPM values, then any decision-making model built on top of it will be fair—regardless of how that model is designed, suggesting a foundational approach to fairness.
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Practical algorithm for real-world deployment: The authors propose FREM (Fair Representation using EIPM with MMD), a concrete algorithm that outperforms existing methods, offering practitioners a usable tool to build fairer AI systems when dealing with continuous sensitive attributes like age or income.
Abstract
AI fairness, also known as algorithmic fairness, aims to ensure that algorithms operate without bias or discrimination towards any individual or group. Among various AI algorithms, the Fair Representation Learning (FRL) approach has gained significant interest in recent years. However, existing FRL algorithms have a limitation: they are primarily designed for categorical sensitive attributes and thus cannot be applied to continuous sensitive attributes, such as age or income. In this paper, we propose an FRL algorithm for continuous sensitive attributes. First, we introduce a measure called the Expectation of Integral Probability Metrics (EIPM) to assess the fairness level of representation space for continuous sensitive attributes. We demonstrate that if the distribution of the representation has a low EIPM value, then any prediction head constructed on the top of the representation become fair, regardless of the selection of the prediction head. Furthermore, EIPM possesses a distinguished advantage in that it can be accurately estimated using our proposed estimator with finite samples. Based on these properties, we propose a new FRL algorithm called Fair Representation using EIPM with MMD (FREM). Experimental evidences show that FREM outperforms other baseline methods.
42 pages, 30 figures. IEEE Transactions on Pattern Analysis and Machine Intelligence (2025)
Paper Info
arXiv ID2505.06435
PublishedMay 9, 2025
Citations
10
Influential
2
Relevance92%
VenueIEEE Transactions on Pattern Analysis and Machine Intelligence
TypeJournalArticle
Categorystat.ML
Responsible AI Topics
FairnessBiasAccountability
Links
Citation data from Semantic Scholar