A comprehensive taxonomy for explainable artificial intelligence: a systematic survey of surveys on methods and concepts

Abstract

In the meantime, a wide variety of terminologies, motivations, approaches, and evaluation criteria have been developed within the research field of explainable artificial intelligence (XAI). With the amount of XAI methods vastly growing, a taxonomy of methods is needed by researchers as well as practitioners: To grasp the breadth of the topic, compare methods, and to select the right XAI method based on traits required by a specific use-case context. Many taxonomies for XAI methods of varying level of detail and depth can be found in the literature. While they often have a different focus, they also exhibit many points of overlap. This paper unifies these efforts and provides a complete taxonomy of XAI methods with respect to notions present in the current state of research. In a structured literature analysis and meta-study, we identified and reviewed more than 50 of the most cited and current surveys on XAI methods, metrics, and method traits. After summarizing them in a survey of surveys, we merge terminologies and concepts of the articles into a unified structured taxonomy. Single concepts therein are illustrated by more than 50 diverse selected example methods in total, which we categorize accordingly. The taxonomy may serve both beginners, researchers, and practitioners as a reference and wide-ranging overview of XAI method traits and aspects. Hence, it provides foundations for targeted, use-case-oriented, and context-sensitive future research.

Interpretable machine learning for dementia: A systematic review

“…This rapid growth has led to inconsistencies in the terminology used to describe such methods, making it difficult to identify relevant studies. Although many reviews on IML introduce taxonomies that bring clarity to the different methods, 16 there is still inconsistency across research papers when incorporating explanation methods in their analysis. In dementia studies specifically, coupled with the variety of data available for differential diagnosis and prognosis, this has led to a complex landscape of methods that makes it hard to identify best practice.…”

“…Details on these methods and their properties can be found in resources such as Christoph Molnar's guide 17 . Recent reviews of interpretable machine learning have introduced frameworks (taxonomies) that summarize their properties, provide a visual aid, and promote consistency across future work 13,16,18 …”

Explainable artificial intelligence: A taxonomy and guidelines for its application to drug discovery

“…Even when this contribution is not oriented to the chemistry domain, it was useful because graph‐based representations are highly relevant for studying molecules and, for this reason, these kind of XAI methods are one of the most applied in drug discovery. Additionally, other general studies about XAI have been valuable contributions for discussing, refining and improving our proposed taxonomy 7,10,14,21 . Our main goal is deliver an easy to read and intuitive explanation for each family of XAI methods oriented to the general research community on medical chemistry, trying to avoid excessive mathematical or algorithmic descriptions that could require a strongest background in deep learning issues and technologies.…”

Expanding Explainability: From Explainable Artificial Intelligence to Explainable Hardware

“…We focused on reviewing basic concepts in XAI and transferred them to explainable hardware, see Section 5.2. However, there are other advanced concepts and classifications that are discussed in XAI literature, e. g., in the work of Schwalbe and Finzel [91], Sokol and Flach [94], and Speith [96]. Future research could further evaluate and adapt existing XAI research to develop new explainability approaches.…”