Related papers: Classifier-to-Bias: Toward Unsupervised Automatic Bias Detection for Visual Classifiers

Classifier-to-Bias: Toward Unsupervised Automatic Bias Detection for Visual Classifiers

URL: http://arxiv.org/abs/2504.20902v1
Date: Tue, 29 Apr 2025 16:19:38 GMT
Title: Classifier-to-Bias: Toward Unsupervised Automatic Bias Detection for Visual Classifiers
Authors: Quentin Guimard, Moreno D'Incà, Massimiliano Mancini, Elisa Ricci,
Abstract summary: Existing approaches for bias identification rely on datasets containing labels for the task of interest.<n>We present-to-Bias (C2B), the first bias discovery framework that works without access to any labeled data.<n>C2B is training-free, does not require any annotations, has no constraints on the list of biases, and can be applied to any pre-trained model on any classification task.
Score: 25.909153114646692
License: http://creativecommons.org/licenses/by-nc-sa/4.0/
Abstract: A person downloading a pre-trained model from the web should be aware of its biases. Existing approaches for bias identification rely on datasets containing labels for the task of interest, something that a non-expert may not have access to, or may not have the necessary resources to collect: this greatly limits the number of tasks where model biases can be identified. In this work, we present Classifier-to-Bias (C2B), the first bias discovery framework that works without access to any labeled data: it only relies on a textual description of the classification task to identify biases in the target classification model. This description is fed to a large language model to generate bias proposals and corresponding captions depicting biases together with task-specific target labels. A retrieval model collects images for those captions, which are then used to assess the accuracy of the model w.r.t. the given biases. C2B is training-free, does not require any annotations, has no constraints on the list of biases, and can be applied to any pre-trained model on any classification task. Experiments on two publicly available datasets show that C2B discovers biases beyond those of the original datasets and outperforms a recent state-of-the-art bias detection baseline that relies on task-specific annotations, being a promising first step toward addressing task-agnostic unsupervised bias detection.

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