Explainability

For many use-cases, it is often important to explain the prediction of a black-box model by identifying the most influential training data samples. We propose AIDE, Antithetical, Intent-based, and Diverse Example-Based Explanations, an approach for providing antithetical (i.

We propose a method for global explainability of black box models using counterfactual explanations. A counterfactual explanation locally explains an outcome by providing the minimal changes necessary to reverse the outcome, e.

We propose novel fairness definitions concerning algorithmic recourse. For an individual that receives an undesirable outcome (e.g., loan application rejected), recourse is a way to reverse the outcome (e.g., increase down payment).