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Key Takeaways

XAI methods are diverse and fragmented: The paper identifies and reviews over 50 surveys on explainable AI, revealing that the field has developed "a wide variety of terminologies, motivations, approaches, and evaluation criteria," making it difficult for practitioners to navigate and select appropriate methods for their needs.
A unified framework is essential for practical adoption: By merging concepts from multiple surveys into a single structured taxonomy covering more than 50 example methods, this work provides practitioners and organizations with a standardized reference to understand different XAI approaches and match them to specific use-case requirements.
Context and use-case matter for XAI selection: The paper emphasizes that choosing the right XAI method requires understanding "traits required by a specific use-case context," suggesting that there is no one-size-fits-all solution and that responsible AI implementation demands careful consideration of particular organizational and operational needs.
The field needs common language: The taxonomy addresses the problem of inconsistent terminology across XAI research, which has hindered communication between researchers and practitioners; establishing unified concepts helps stakeholders discuss and evaluate explainability methods more effectively.
Explainability is multidimensional: By categorizing methods across multiple traits and aspects rather than presenting a simple list, the taxonomy reveals that explainability involves various dimensions and approaches, requiring organizations to think carefully about what "explainability" means for their specific AI systems and stakeholders.

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.

71 pages, 7 figures, 6 tables; published as part of Special Issue on Explainable and Interpretable Machine Learning and Data Mining, in Data Mining and Knowledge Discovery Journal

Key Takeaways

XAI methods are diverse and fragmented: The paper identifies and reviews over 50 surveys on explainable AI, revealing that the field has developed "a wide variety of terminologies, motivations, approaches, and evaluation criteria," making it difficult for practitioners to navigate and select appropriate methods for their needs.
A unified framework is essential for practical adoption: By merging concepts from multiple surveys into a single structured taxonomy covering more than 50 example methods, this work provides practitioners and organizations with a standardized reference to understand different XAI approaches and match them to specific use-case requirements.
Context and use-case matter for XAI selection: The paper emphasizes that choosing the right XAI method requires understanding "traits required by a specific use-case context," suggesting that there is no one-size-fits-all solution and that responsible AI implementation demands careful consideration of particular organizational and operational needs.
The field needs common language: The taxonomy addresses the problem of inconsistent terminology across XAI research, which has hindered communication between researchers and practitioners; establishing unified concepts helps stakeholders discuss and evaluate explainability methods more effectively.
Explainability is multidimensional: By categorizing methods across multiple traits and aspects rather than presenting a simple list, the taxonomy reveals that explainability involves various dimensions and approaches, requiring organizations to think carefully about what "explainability" means for their specific AI systems and stakeholders.

Abstract

71 pages, 7 figures, 6 tables; published as part of Special Issue on Explainable and Interpretable Machine Learning and Data Mining, in Data Mining and Knowledge Discovery Journal

A Comprehensive Taxonomy for Explainable Artificial Intelligence: A Systematic Survey of Surveys on Methods and Concepts

Key Takeaways

Abstract

Paper Info

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A Comprehensive Taxonomy for Explainable Artificial Intelligence: A Systematic Survey of Surveys on Methods and Concepts

Key Takeaways

Abstract

Paper Info

Responsible AI Topics

Links