Controlled Model Debiasing through Minimal and Interpretable Updates

• URL: http://arxiv.org/abs/2502.21284v1
• Date: Fri, 28 Feb 2025 18:03:55 GMT
• Title: Controlled Model Debiasing through Minimal and Interpretable Updates
• Authors: Federico Di Gennaro, Thibault Laugel, Vincent Grari, Marcin Detyniecki,
• Abstract summary: We introduce the notion of controlled model debiasing, a novel supervised learning task relying on two desideratas.<n>We introduce a novel algorithm for algorithmic fairness, COMMOD, that is both model-agnostic and does not require the sensitive attribute at test time.<n>Our approach combines a concept-based architecture and adversarial learning and we demonstrate through empirical results that it achieves comparable performance to state-of-the-art debiasing methods.
• Score: 6.089774484591287
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Traditional approaches to learning fair machine learning models often require rebuilding models from scratch, generally without accounting for potentially existing previous models. In a context where models need to be retrained frequently, this can lead to inconsistent model updates, as well as redundant and costly validation testing. To address this limitation, we introduce the notion of controlled model debiasing, a novel supervised learning task relying on two desiderata: that the differences between new fair model and the existing one should be (i) interpretable and (ii) minimal. After providing theoretical guarantees to this new problem, we introduce a novel algorithm for algorithmic fairness, COMMOD, that is both model-agnostic and does not require the sensitive attribute at test time. In addition, our algorithm is explicitly designed to enforce minimal and interpretable changes between biased and debiased predictions -a property that, while highly desirable in high-stakes applications, is rarely prioritized as an explicit objective in fairness literature. Our approach combines a concept-based architecture and adversarial learning and we demonstrate through empirical results that it achieves comparable performance to state-of-the-art debiasing methods while performing minimal and interpretable prediction changes.

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