{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,24]],"date-time":"2025-09-24T00:15:00Z","timestamp":1758672900639,"version":"3.44.0"},"publisher-location":"California","reference-count":0,"publisher":"International Joint Conferences on Artificial Intelligence Organization","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,9]]},"abstract":"<jats:p>Algorithmic fairness is a critical challenge in building trustworthy Machine Learning (ML)  models. ML classifiers strive to make predictions that closely match real-world observations (ground truth). However, if the ground truth data itself reflects biases against certain sub-populations, a dilemma arises: prioritize fairness and potentially reduce accuracy, or emphasize accuracy at the expense of fairness.\n\nThis work proposes a novel training framework that goes beyond achieving high accuracy. Our framework trains a classifier to not only deliver optimal predictions but also to identify potential fairness risks associated with each prediction. \n\nTo do so, we specify a dual-labeling strategy where the second label contains a per-prediction fairness evaluation, referred to as an unfairness risk evaluation. In addition, we identify a subset of samples as highly vulnerable to group-unfair classifiers.\n\nOur experiments demonstrate that our classifiers attain optimal accuracy levels on both the Adult-Census-Income and Compas-Recidivism datasets. Moreover, they identify unfair predictions with nearly 75% accuracy at the cost of expanding the size of the classifier by 45%.<\/jats:p>","DOI":"10.24963\/ijcai.2025\/1207","type":"proceedings-article","created":{"date-parts":[[2025,9,19]],"date-time":"2025-09-19T08:10:40Z","timestamp":1758269440000},"page":"10869-10874","source":"Crossref","is-referenced-by-count":0,"title":["FairCognizer: A Model for Accurate Predictions with Inherent Fairness Evaluation (Extended Abstract)"],"prefix":"10.24963","author":[{"given":"Adda Akram","family":"Bendoukha","sequence":"first","affiliation":[{"name":"Samovar, Telecom SudParis, Institut Polytechnique de Paris"}]},{"given":"Nesrine","family":"Kaaniche","sequence":"additional","affiliation":[{"name":"Samovar, Telecom SudParis, Institut Polytechnique de Paris"}]},{"given":"Aymen","family":"Boudguiga","sequence":"additional","affiliation":[{"name":"Universit\u00e9 Paris Saclay, CEA-List"}]},{"given":"Renaud","family":"Sirdey","sequence":"additional","affiliation":[{"name":"Universit\u00e9 Paris Saclay, CEA-List"}]}],"member":"10584","event":{"number":"34","sponsor":["International Joint Conferences on Artificial Intelligence Organization (IJCAI)"],"acronym":"IJCAI-2025","name":"Thirty-Fourth International Joint Conference on Artificial Intelligence {IJCAI-25}","start":{"date-parts":[[2025,8,16]]},"theme":"Artificial Intelligence","location":"Montreal, Canada","end":{"date-parts":[[2025,8,22]]}},"container-title":["Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence"],"original-title":[],"deposited":{"date-parts":[[2025,9,23]],"date-time":"2025-09-23T11:36:27Z","timestamp":1758627387000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ijcai.org\/proceedings\/2025\/1207"}},"subtitle":[],"proceedings-subject":"Artificial Intelligence Research Articles","short-title":[],"issued":{"date-parts":[[2025,9]]},"references-count":0,"URL":"https:\/\/doi.org\/10.24963\/ijcai.2025\/1207","relation":{},"subject":[],"published":{"date-parts":[[2025,9]]}}}