Title: Bridging Explainability and Embeddings: BEE Aware of Spuriousness
Primary area: interpretability and explainable AI
Keywords: spurious correlation, interpretability, clip, foundation models

Abstract: Current methods for detecting spurious correlations rely on data splits or error patterns, leaving many harmful shortcuts invisible when counterexamples are absent. We introduce BEE (Bridging Explainability and Embeddings), a framework that shifts the focus from model predictions to the weight space and embedding geometry underlying decisions. By analyzing how fine-tuning perturbs pretrained representations, BEE uncovers spurious correlations that remain hidden from conventional evaluation pipelines. We use linear probing as a transparent diagnostic lens, revealing spurious features that not only persist after full fine-tuning but also transfer across diverse state-of-the-art models. Our experiments cover numerous datasets and domains: vision (Waterbirds, CelebA, ImageNet-1k), language (CivilComments, MIMIC-CXR medical notes), and multiple embedding families (CLIP, CLIP-DataComp.XL, mGTE, BLIP2, SigLIP2). 
BEE consistently exposes spurious correlations: from concepts that slash the ImageNet accuracy by up to 95\%, to clinical shortcuts in MIMIC-CXR notes that induce dangerous false negatives. Together, these results position BEE as a general and principled tool for diagnosing spurious correlations in weight space, enabling principled dataset auditing and more trustworthy foundation models. Our code is publicly available.

TL;DR: An embedding space method for identifying spuriously correlated concepts in a dataset, based on foundation models' fine-tuned weights.