Counterfactual In this chapter and section, we will be exploring AI explanations in a unique way. First, several features may be immutable and therefore inapplicable for recourse, e.g., number of past delinquencies. In addition, we will demonstrate several typical use cases of counterfactual explanations in popular research areas. Cloud Computing 79. 1: 2021: Example Perplexity. Explainable AI Counterfactual explanations, which I introduced in one of my previous posts 1, offer a simple and intuitive way to explain black-box models without opening them.Still, as of today there exists only one open-source library that provides a unifying approach to generate and benchmark counterfactual explanations for models built and trained in Python (Pawelczyk et … Authors: Susanne Dandl & Christoph Molnar. Such explanations are certainly useful to a person facing the decision, but they are also useful to system builders and evaluators in debugging the algorithm. By linking the information entered, we provide opportunities to make unexpected discoveries and … In this tutorial, we will present an overview of model interpretability and explainability in AI, key regulations/laws, and techniques/tools for providing explainability as part of AI/ML systems. We will give a tutorial Bias Issues and Solutions in Recsys on WWW 2021. Explainable AI (XAI) — A guide to 7 Packages in Python to … Counterfactuals about what could have happened are increasingly used in an array of Artificial Intelligence (AI) applications, and especially in explainable AI (XAI). DiCE: Diverse Counterfactual Explanations for Machine Learning ...
