Navigating Towards Fairness with Data Selection

• URL: http://arxiv.org/abs/2412.11072v1
• Date: Sun, 15 Dec 2024 06:11:05 GMT
• Title: Navigating Towards Fairness with Data Selection
• Authors: Yixuan Zhang, Zhidong Li, Yang Wang, Fang Chen, Xuhui Fan, Feng Zhou,
• Abstract summary: We introduce a data selection method designed to efficiently and flexibly mitigate label bias. Our approach utilizes a zero-shot predictor as a proxy model that simulates training on a clean holdout set. Our modality-agnostic method has proven efficient and effective in handling label bias and improving fairness across diverse datasets in experimental evaluations.
• Score: 27.731128352096555
• License:
• Abstract: Machine learning algorithms often struggle to eliminate inherent data biases, particularly those arising from unreliable labels, which poses a significant challenge in ensuring fairness. Existing fairness techniques that address label bias typically involve modifying models and intervening in the training process, but these lack flexibility for large-scale datasets. To address this limitation, we introduce a data selection method designed to efficiently and flexibly mitigate label bias, tailored to more practical needs. Our approach utilizes a zero-shot predictor as a proxy model that simulates training on a clean holdout set. This strategy, supported by peer predictions, ensures the fairness of the proxy model and eliminates the need for an additional holdout set, which is a common requirement in previous methods. Without altering the classifier's architecture, our modality-agnostic method effectively selects appropriate training data and has proven efficient and effective in handling label bias and improving fairness across diverse datasets in experimental evaluations.

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