做视觉模型部署和公平性的团队，终于有了一个无需额外标注就能定位模型偏见的工具——直接在冻结模型上分析，省去重新训练的麻烦，值得一试。
本文提出一种无需偏见标签的后处理方法，用于识别冻结视觉模型中的虚假关联。该方法仅依赖标准类别标签，通过非负矩阵分解从中间激活中提取可解释概念向量，并利用误分类样本的梯度信号对候选概念进行排序。在Colored MNIST、Waterbirds和CelebA数据集上，该方法成功识别出已知虚假线索，且抑制排名靠前的概念可将最差组准确率提升最高17.9个百分点。该方法无需重新训练或参数更新，为部署后的模型提供了可解释的审计工具和去偏手段。代码已开源。
Bias Leaves a Gradient Trail: Label-Free Bias Identification via Gradient Probes on Concept Decompositions
Vision classifiers can exploit spurious correlations, achieving high in-distribution accuracy yet failing under distribution shift. Existing approaches to bias mitigation and analysis often depend on curated datasets, spurious-attribute or group labels, or retraining, which may be infeasible once a model is deployed or the relevant bias is unknown. We present a bias-label-free, post-hoc method for identifying spurious concepts in frozen vision models, relying only on standard class labels from a held-out audit dataset. For each target class, we collect patches from inputs predicted as that class and apply non-negative matrix factorization to intermediate activations to obtain a bank of interpretable concept vectors. Candidate concepts are then ranked with a bias estimator derived from their interaction with backpropagated gradients on misclassified examples: bias concepts tend to get activated when correcting false negatives and suppressed when correcting false positives. On Colored MNIST and Waterbirds the method recovers concepts aligned with the known spurious cue, and on CelebA it surfaces decision-relevant directions that only partially coincide with the annotated gender attribute; suppressing the top-ranked concepts at inference time improves worst-group accuracy by up to 17.9 percentage points on Waterbirds and 10.4 on CelebA without any retraining or parameter updates. Our method identifies decision-relevant spurious directions that need not coincide with annotated ones, providing both an interpretable auditing tool and an actionable debiasing handle for frozen vision models. Code is available at https://github.com/vitryt/label-free-bias-identification.