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FairCognizer: A Model for Accurate Predictions with Inherent Fairness Evaluation (Extended Abstract)

Adda Akram Bendoukha, Nesrine Kaaniche, Aymen Boudguiga, Renaud Sirdey

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Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence. Sister Conferences Best Papers. Pages 10869-10874.

https://doi.org/10.24963/ijcai.2025/1207

Keywords

  • Sister Conferences Best Papers: AI Ethics, Trust, Fairness

Abstract

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.

This 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.

To 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.

Our 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%.

Adda Akram Bendoukha, Nesrine Kaaniche, Aymen Boudguiga, and Renaud Sirdey. 2025. FairCognizer: A Model for Accurate Predictions with Inherent Fairness Evaluation (Extended Abstract). In Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence, IJCAI-25. IJCAI, 10869–10874. https://doi.org/10.24963/ijcai.2025/1207
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