Combining Counterfactuals With Shapley Values To Explain Image Models

Images require different explanation approaches than simpler data: While machine learning models trained on tabular data (spreadsheets with rows and columns) have established explanation methods, images present a unique challenge because individual pixels are meaningless on their own—the paper addresses this gap by using high-level interpretable features instead of raw pixels.

Counterfactuals provide intuitive explanations: The method generates counterfactual explanations (showing what would need to change in an image for the model to make a different decision), which helps users understand not just what the model decided, but why by showing alternative scenarios.

Shapley values offer mathematically principled fairness: By applying Shapley values from game theory, the approach provides explanations with "strong axiomatic guarantees"—meaning the method is built on mathematically proven principles that ensure fair and consistent attribution of importance to different image features.

Bridging the gap between accuracy and interpretability: This work addresses a critical challenge in responsible AI: making sophisticated image recognition models transparent and understandable to stakeholders, which is especially important for "sensitive applications" where decisions affect people's lives.

High-dimensional data explanation is now more tractable: The paper demonstrates a practical pipeline for explaining complex visual models, moving beyond the limitation that only simple, low-dimensional datasets could be easily interpreted.