Counterfactual Explanations vs. Attribution based Explanations

This post is co-authored by Aalok Shanbhag and Ankur Taly As “black box” machine learning models spread to high stakes domains (e.g., lending, hiring, and healthcare), there is a growing need for explaining their predictions from end-user, regulatory, operations, and societal perspectives. Consequently, practical and scalable explainability approaches are being developed at a rapid pace.  … Continue reading “Counterfactual Explanations vs. Attribution based Explanations”