Explainable AI

Current research on explainability and interpretability of machine learning algorithms

This site is a community effort by the Explainable AI members. Please join the group and reach out to the administrators if you have any questions.

Made with Jekyll and Hyde. Idea and base code for the website adapted from ml4code.

Explaining Explanations to Society

Leilani H. Gilpin, Cecilia Testart, Nathaniel Fruchter, Julius Adebayo. 2019

[ArXiv]    

There is a disconnect between explanatory artificial intelligence (XAI) methods and the types of explanations that are useful for and demanded by society (policy makers, government officials, etc.) Questions that experts in artificial intelligence (AI) ask opaque systems provide inside explanations, focused on debugging, reliability, and validation. These are different from those that society will ask of these systems to build trust and confidence in their decisions. Although explanatory AI systems can answer many questions that experts desire, they often don’t explain why they made decisions in a way that is precise (true to the model) and understandable to humans. These outside explanations can be used to build trust, comply with regulatory and policy changes, and act as external validation. In this paper, we focus on XAI methods for deep neural networks (DNNs) because of DNNs’ use in decision-making and inherent opacity. We explore the types of questions that explanatory DNN systems can answer and discuss challenges in building explanatory systems that provide outside explanations for societal requirements and benefit.