Mining bias-target Alignment from Voronoi Cells

• URL: http://arxiv.org/abs/2305.03691v1
• Date: Fri, 5 May 2023 17:09:01 GMT
• Title: Mining bias-target Alignment from Voronoi Cells
• Authors: R\'emi Nahon and Van-Tam Nguyen and Enzo Tartaglione
• Abstract summary: We propose a bias-agnostic approach to mitigate the impact of bias in deep neural networks. Unlike traditional debiasing approaches, we rely on a metric to quantify bias alignment/misalignment'' on target classes. Our results indicate that the proposed method achieves comparable performance to state-of-the-art supervised approaches.
• Score: 2.66418345185993
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Despite significant research efforts, deep neural networks are still vulnerable to biases: this raises concerns about their fairness and limits their generalization. In this paper, we propose a bias-agnostic approach to mitigate the impact of bias in deep neural networks. Unlike traditional debiasing approaches, we rely on a metric to quantify ``bias alignment/misalignment'' on target classes, and use this information to discourage the propagation of bias-target alignment information through the network. We conduct experiments on several commonly used datasets for debiasing and compare our method to supervised and bias-specific approaches. Our results indicate that the proposed method achieves comparable performance to state-of-the-art supervised approaches, although it is bias-agnostic, even in presence of multiple biases in the same sample.

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