{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,22]],"date-time":"2025-10-22T23:38:38Z","timestamp":1761176318035,"version":"build-2065373602"},"reference-count":0,"publisher":"IOS Press","isbn-type":[{"value":"9781643686318","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,10,21]],"date-time":"2025-10-21T00:00:00Z","timestamp":1761004800000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,10,21]]},"abstract":"<jats:p>Within a legal framework, fairness in datasets and models is typically assessed by dividing observations into predefined groups and then computing fairness measures (e.g., Disparate Impact or Equality of Odds with respect to gender). However, when sensitive attributes such as skin color are continuous, dividing into default groups may overlook or obscure the discrimination experienced by certain minority subpopulations. To address this limitation, we propose a fairness-based grouping approach for continuous (possibly multidimensional) sensitive attributes. By grouping data according to observed levels of discrimination, our method identifies the partition that maximizes a novel criterion based on inter-group variance in discrimination, thereby isolating the most critical subgroups. We validate the proposed approach using multiple synthetic datasets and demonstrate its robustness under changing population distributions\u2014revealing how discrimination is manifested within the space of sensitive attributes. Furthermore, we examine a specialized setting of monotonic fairness for the case of skin color. Our empirical results on both CelebA and FFHQ, leveraging the skin tone as predicted by an industrial proprietary algorithm, show that the proposed segmentation uncovers more nuanced patterns of discrimination than previously reported, and that these findings remain stable across datasets for a given model. Finally, we leverage our grouping model for debiasing purpose, aiming at predicting fair scores with group-by-group post-processing. The results demonstrate that our approach improves fairness while having minimal impact on accuracy, thus confirming our partition method and opening the door for industrial deployment.<\/jats:p>","DOI":"10.3233\/faia251472","type":"book-chapter","created":{"date-parts":[[2025,10,22]],"date-time":"2025-10-22T10:03:40Z","timestamp":1761127420000},"source":"Crossref","is-referenced-by-count":0,"title":["Fairness-Aware Grouping for Continuous Sensitive Variables: Application for Debiasing Face Analysis with Respect to Skin Tone"],"prefix":"10.3233","author":[{"given":"Veronika","family":"Shilova","sequence":"first","affiliation":[{"name":"Artefact Research Center, Paris, France"},{"name":"Institut de Math\u00e9matiques de Toulouse (UMR 5219), CNRS, Universit\u00e9 de Toulouse, F-31062 Toulouse, France"}]},{"given":"Emmanuel","family":"Malherbe","sequence":"additional","affiliation":[{"name":"Artefact Research Center, Paris, France"}]},{"given":"Giovanni","family":"Palma","sequence":"additional","affiliation":[{"name":"L\u2019Or\u00e9al Research and Innovation, Paris, France"}]},{"given":"Laurent","family":"Risser","sequence":"additional","affiliation":[{"name":"Institut de Math\u00e9matiques de Toulouse (UMR 5219), CNRS, Universit\u00e9 de Toulouse, F-31062 Toulouse, France"}]},{"given":"Jean-Michel","family":"Loubes","sequence":"additional","affiliation":[{"name":"Institut de Math\u00e9matiques de Toulouse (UMR 5219), CNRS, Universit\u00e9 de Toulouse, F-31062 Toulouse, France"},{"name":"INRIA, ANITI, France"}]}],"member":"7437","container-title":["Frontiers in Artificial Intelligence and Applications","ECAI 2025"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/FAIA251472","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,22]],"date-time":"2025-10-22T10:03:40Z","timestamp":1761127420000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/FAIA251472"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10,21]]},"ISBN":["9781643686318"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/faia251472","relation":{},"ISSN":["0922-6389","1879-8314"],"issn-type":[{"value":"0922-6389","type":"print"},{"value":"1879-8314","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,10,21]]}}}