Mitigating Label Bias via Decoupled Confident Learning

• URL: http://arxiv.org/abs/2307.08945v2
• Date: Fri, 29 Sep 2023 16:06:05 GMT
• Title: Mitigating Label Bias via Decoupled Confident Learning
• Authors: Yunyi Li, Maria De-Arteaga, Maytal Saar-Tsechansky
• Abstract summary: Growing concerns regarding algorithmic fairness have led to a surge in methodologies to mitigate algorithmic bias. bias in labels is pervasive across important domains, including healthcare, hiring, and content moderation. We propose a pruning method -- Decoupled Confident Learning (DeCoLe) -- specifically designed to mitigate label bias.
• Score: 14.001915875687862
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Growing concerns regarding algorithmic fairness have led to a surge in methodologies to mitigate algorithmic bias. However, such methodologies largely assume that observed labels in training data are correct. This is problematic because bias in labels is pervasive across important domains, including healthcare, hiring, and content moderation. In particular, human-generated labels are prone to encoding societal biases. While the presence of labeling bias has been discussed conceptually, there is a lack of methodologies to address this problem. We propose a pruning method -- Decoupled Confident Learning (DeCoLe) -- specifically designed to mitigate label bias. After illustrating its performance on a synthetic dataset, we apply DeCoLe in the context of hate speech detection, where label bias has been recognized as an important challenge, and show that it successfully identifies biased labels and outperforms competing approaches.

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