Testing Fairness with Utility Tradeoffs: A Wasserstein Projection Approach

[2505.11678] Testing Fairness with Utility Tradeoffs: A Wasserstein Projection Approach Skip to main content Search Submit Donate Log in Computer Science > Computers and Society arXiv:2505.11678 (cs) [Submitted on 16 May 2025 ( v1 ), last revised 26 Aug 2026 (this version, v4)] Title: Testing Fairness with Utility Tradeoffs: A Wasserstein Projection Approach Authors: Yan Chen , Zheng Tan , Jose Blanchet , Hanzhang Qin View PDF HTML (experimental) Abstract: Ensuring fairness in data driven decision making has become a central concern across domains such as marketing, lending, and healthcare, but fairness constraints often come at the cost of utility. We propose a statistical hypothesis testing framework that jointly evaluates approximate fairness and utility, relaxing strict fairness requirements while ensuring that overall utility remains above a specified threshold. Our framework builds on the strong demographic parity (SDP) criterion and incorporates a utility measure motivated by the potential outcomes framework. The test statistic is constructed via Wasserstein projections, enabling auditors to assess whether observed fairness-utility tradeoffs are intrinsic to the algorithm or attributable to randomness in the data. We show that the test is computationally tractable, interpretable, broadly applicable across machine learning models, and extendable to more general settings. We apply our approach to multiple real-world datasets, offering new insights into the fairness-utility tradeoff through the perspective of statistical hypothesis testing. Subjects: Computers and Society (cs.CY) Cite as: arXiv:2505.11678 [cs.CY] (or arXiv:2505.11678v4 [cs.CY] for this version) https://doi.org/10.48550/arXiv.2505.11678 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Yan Chen [ view email ] [v1] Fri, 16 May 2025 20:29:06 UTC (1,659 KB) [v2] Tue, 23 Sep 2025 17:40:07 UTC (1,857 KB) [v3] Wed, 24 Sep 2025 12:20:53 UTC (1,857 KB) [v4] Wed, 26 Aug 2026 22:13:55 UTC (1,850 KB) Full-text links: Access Paper: View PDF HTML (experimental) TeX Source view license Additional Features Audio Summary Current browse context: cs.CY < prev | next > new | recent | 2025-05 Change to browse by: cs References & Citations SciX Google Scholar Semantic Scholar export BibTeX citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer? ) Connected Papers Toggle Connected Papers ( What is Connected Papers? ) Litmaps Toggle Litmaps ( What is Litmaps? ) scite.ai Toggle scite Smart Citations ( What are Smart Citations? ) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv ( What is alphaXiv? ) Links to Code Toggle CatalyzeX Code Finder for Papers ( What is CatalyzeX? ) DagsHub Toggle DagsHub ( What is DagsHub? ) GotitPub Toggle Gotit.pub ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle TXYZ.AI ( What is TXYZ.AI? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs . Which authors of this paper are endorsers? | Disable MathJax ( What is MathJax? ) We gratefully acknowledge support from our major funders , member institutions , and all contributors. About · Help · Contact · Subscribe · Copyright · Privacy · Accessibility · Operational Status (opens in new tab)

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