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.

本研究解决了传统偏差识别方法依赖于有标签数据限制的的问题，提出了第一个无须标注数据的偏差发现框架C2B。该框架通过文本描述生成偏差提议，并利用检索模型评估目标分类模型的偏差准确性。实验表明，C2B能够发现超出原始数据集的偏差，并在性能上超过了基于任务特定标注的最新偏差检测基线，具有广泛的应用潜力。

分类器到偏差：面向视觉分类器的无监督自动偏差检测