Скачать с ютуб Explainable AI (XAI) Course #4: Counterfactual Explanations - Explaining and Debugging в хорошем качестве

Explainable AI (XAI) Course #4: Counterfactual Explanations - Explaining and Debugging

How to explain a machine learning model such that the explanation is truthful to the model and yet interpretable to people? This question is key to ML explanations research because explanation techniques face an inherent tradeoff between fidelity and interpretability: a high-fidelity explanation for an ML model tends to be complex and hard to interpret, while an interpretable explanation is often inconsistent with the ML model. In this talk, I will present counterfactual (CF) explanations that bridge this tradeoff. Rather than approximate an ML model or rank features by their predictive importance, a CF explanation “interrogates” a model to find required changes that would flip the model’s decision and presents those examples to a user. Such examples offer a true reflection of how the model would change its prediction, thus helping decision-subject decide what they should do next to obtain a desired outcome and helping model designers debug their model. Using benchmark datasets on loan approval, I will compare counterfactual explanations to popular alternatives like LIME and SHAP. I will also present a case study on generating CF examples for image classifiers that can be used for evaluating fairness and even improving the generalizability of a model. Speaker: Amit Sharma.