Explanation-Driven Counterfactual Testing for Faithfulness in Vision-Language Model Explanations
This addresses technical and governance risks for users and regulators by providing automated testing for faithfulness in VLM explanations, though it is incremental as it builds on existing counterfactual and explanation methods.
The paper tackled the problem of unfaithful explanations in Vision-Language Models by introducing Explanation-Driven Counterfactual Testing (EDCT), which automatically verifies faithfulness and uncovered substantial gaps across 120 OK-VQA examples and multiple VLMs.
Vision-Language Models (VLMs) often produce fluent Natural Language Explanations (NLEs) that sound convincing but may not reflect the causal factors driving predictions. This mismatch of plausibility and faithfulness poses technical and governance risks. We introduce Explanation-Driven Counterfactual Testing (EDCT), a fully automated verification procedure for a target VLM that treats the model's own explanation as a falsifiable hypothesis. Given an image-question pair, EDCT: (1) obtains the model's answer and NLE, (2) parses the NLE into testable visual concepts, (3) generates targeted counterfactual edits via generative inpainting, and (4) computes a Counterfactual Consistency Score (CCS) using LLM-assisted analysis of changes in both answers and explanations. Across 120 curated OK-VQA examples and multiple VLMs, EDCT uncovers substantial faithfulness gaps and provides regulator-aligned audit artifacts indicating when cited concepts fail causal tests.