An exploration of combinatorial testing-based approaches to fault localization for explainable AI View Full Text


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Article Info

DATE

2021-09-20

AUTHORS

Ludwig Kampel, Dimitris E. Simos, D. Richard Kuhn, Raghu N. Kacker

ABSTRACT

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. More... »

PAGES

1-14

References to SciGraph publications

  • 2003-05-13. CAPTCHA: Using Hard AI Problems for Security in ADVANCES IN CRYPTOLOGY — EUROCRYPT 2003
  • 2007-08-03. Locating and detecting arrays for interaction faults in JOURNAL OF COMBINATORIAL OPTIMIZATION
  • 2017-10-29. Approaches to Fault Localization in Combinatorial Testing: A Survey in SMART COMPUTING AND INFORMATICS
  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/s10472-021-09772-0

    DOI

    http://dx.doi.org/10.1007/s10472-021-09772-0

    DIMENSIONS

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