Predictability and Comprehensibility in Post-Hoc XAI Methods: A User-Centered Analysis
This work addresses the problem of user comprehension and predictability in XAI for practitioners, though it is incremental as it builds on existing methods with user-centered analysis.
The study evaluated how well users comprehend and predict model behavior using post-hoc explainability methods like LIME and SHAP, finding that SHAP's comprehensibility decreases near decision boundaries, while counterfactual explanations and misclassifications improve user understanding.
Post-hoc explainability methods aim to clarify predictions of black-box machine learning models. However, it is still largely unclear how well users comprehend the provided explanations and whether these increase the users ability to predict the model behavior. We approach this question by conducting a user study to evaluate comprehensibility and predictability in two widely used tools: LIME and SHAP. Moreover, we investigate the effect of counterfactual explanations and misclassifications on users ability to understand and predict the model behavior. We find that the comprehensibility of SHAP is significantly reduced when explanations are provided for samples near a model's decision boundary. Furthermore, we find that counterfactual explanations and misclassifications can significantly increase the users understanding of how a machine learning model is making decisions. Based on our findings, we also derive design recommendations for future post-hoc explainability methods with increased comprehensibility and predictability.