There has recently been a surge of work in explanatory artificial intelligence (XAI). This research area tackles the important problem that complex machines and algorithms often cannot provide insights into their behavior and thought processes. XAI allows users and parts of the internal system to be more transparent, providing explanations of their decisions in some level of detail. These explanations are important to ensure algorithmic fairness, identify potential bias problems in the training data, and to ensure that the algorithms perform as expected. However, explanations produced by these systems is neither standardized nor systematically assessed. In an effort to create best practices and identify open challenges, we provide our definition of explainability and show how it can be used to classify existing literature. We discuss why current approaches to explanatory methods especially for deep neural networks are insufficient. Finally, based on our survey, we conclude with suggested future research directions for explanatory artificial intelligence.
A previous survey has attempted to define taxonomies and best practices for a strong science'' of interpretability @cite_19 . The motivation of this paper is similar to ours, noting that the volume of research on interpretability is rapidly growing'' and that there is no clear existing definition or evaluation criteria for interpretability. The authors define interpretability as the ability to explain or to present in understandable terms to a human'' and suggest a variety of definitions for explainability, converging on the notion that interpretation is the act of discovering the evaluations of an explanation. The authors attempt to reach consensus on the definition of interpretable machine learning and how it should be measured. While we are inspired by the taxonomy of this paper, we focus on the explainability aspect rather than interpretability.
@cite_19
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As machine learning systems become ubiquitous, there has been a surge of interest in interpretable machine learning: systems that provide explanation for their outputs. These explanations are often used to qualitatively assess other criteria such as safety or non-discrimination. However, despite the interest in interpretability, there is very little consensus on what interpretable machine learning is and how it should be measured. In this position paper, we first define interpretability and describe when interpretability is needed (and when it is not). Next, we suggest a taxonomy for rigorous evaluation and expose open questions towards a more rigorous science of interpretable machine learning.