ECCCos from the Black Box

Faithful Model Explanations through Energy-Constrained Conformal Counterfactuals

Counterfactual explanations offer an intuitive and straightforward way to explain opaque machine learning (ML) models. They work under the premise of perturbing inputs to achieve a desired change in the predicted output.

If you have not heard about counterfactual explanations before, feel free to also check out my introductory posts: 1) Individual Recourse for Black Box Models and 2) A new tool for explainable AI.