An exploration of combinatorial testing-based approaches to fault localization for explainable AI
| Autor: | Raghu N. Kacker, Ludwig Kampel, Dimitris E. Simos, D. Richard Kuhn |
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| Rok vydání: | 2021 |
| Předmět: |
Computer science
business.industry Applied Mathematics Complex system Fault (power engineering) GeneralLiterature_MISCELLANEOUS ComputingMethodologies_PATTERNRECOGNITION Artificial Intelligence Software testing Combinatorial testing Research questions Artificial intelligence business Structural approach |
| Zdroj: | Annals of Mathematics and Artificial Intelligence. 90:951-964 |
| ISSN: | 1573-7470 1012-2443 |
| DOI: | 10.1007/s10472-021-09772-0 |
| Popis: | We briefly review properties of explainable AI proposed by various researchers. We take a structural approach to the problem of explainable AI, examine the feasibility of these aspects and extend them where appropriate. Afterwards, we review combinatorial methods for explainable AI which are based on combinatorial testing-based approaches to fault localization. Last, we view the combinatorial methods for explainable AI through the lens provided by the properties of explainable AI that are elaborated in this work. We pose resulting research questions that need to be answered and point towards possible solutions, which involve a hypothesis about a potential parallel between software testing, human cognition and brain capacity. |
| Databáze: | OpenAIRE |
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