The DOI matches its Crossref record; 35 of the 35 references it cites were found in a registry or an index.
A comprehensive taxonomy for explainable artificial intelligence: a systematic survey of surveys on methods and concepts
Open accessAbstract
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.
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Cites
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Cited by
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Longo, L., Brcic, M., Cabitza, F., Choi, J., Confalonieri, R., Ser, J. D., Guidotti, R., Hayashi, Y., Herrera, F., Holzinger, A., et al. (2024) · Information Fusion · doi:10.1016/j.inffus.2024.102301 · Cited by 626
Interpretable machine learning for dementia: a systematic review
Martin, S. A., Townend, F. J., Barkhof, F., & Cole, J. H. (2023) · Alzheimer's & Dementia · doi:10.1002/alz.12948 · Cited by 149
On generating trustworthy counterfactual explanations
Del Ser, J., Barredo-Arrieta, A., Díaz-Rodríguez, N., Herrera, F., Saranti, A., & Holzinger, A. (2024) · Information Sciences · doi:10.1016/j.ins.2023.119898 · Cited by 122
Survey on Explainable AI: Techniques, challenges and open issues
Abusitta, A., Li, M. Q., & Fung, B. C. M. (2024) · Expert Systems with Applications · doi:10.1016/j.eswa.2024.124710 · Cited by 98
Metadata
- Authors
- Schwalbe, G., & Finzel, B.
- Venue
- Data Mining and Knowledge Discovery
- Published
- 2023-01-06
- Volume
- 38
- Issue
- 5
- Pages
- 3043-3101
- ISSN
- 1384-5810 (ISSN-L)
- 1573-756X
- OpenAlex
- W4313650676
- Cited by
- 328
- References
- 35
- FWCI
- 28.73
- Type
- journal article
- Language
- en
- Open access
- hybrid
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