BiasEdit: A Training-Free Bias-Detect-and-Edit Framework for Learning Fair Visual Classifiers

Abstract

Visual data from the Web power image classifiers, which underpin web services, including recommendation and moderation. However, Web data contain spurious correlations and social biases, and neural networks tend to learn biases in data. This can reinforce unfairness in web services and the web data, leading to a vicious cycle. In image classification, networks learn bias attributes for a specific class when most images contain the same attribute for a given class. Hence, training a fair and debiased classifier from a biased dataset demands handling imbalance between a majority of images with bias attributes (bias-aligned samples) and a minority without (bias-conflict samples). In this work, we introduce BiasEdit, a modular framework that automatically detects bias attributes from the original dataset and edits them to construct a debiased dataset. Specifically, BiasEdit first detects unknown bias attributes via statistical dependence and mutual information analysis of visual–linguistic representations, and then explicitly edits those attributes using text-guided image editing to generate realistic bias-conflict samples. Unlike prior works that assume known bias attributes or rely on synthetic mixing, our method operates without manual annotations and leverages off-the-shelf vision–language and editing models. BiasEdit addresses a fundamental challenge in Web-sourced visual AI, mitigating dataset-induced bias and achieving state-of-the-art debiasing performance even when training data are fully biased.