NIST · ИТ · e24783d25f5aba5f5e6e
An exploration of combinatorial testing-based approaches to fault localization for explainable AI
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
NIST · ИТОфициальный источник · 2021-09-20
{
"title_en": "An exploration of combinatorial testing-based approaches to fault localization for explainable AI",
"designation": "",
"series": "",
"status": "",
"date": "2021-09-20",
"date_original": "September 20, 2021",
"url": "https://www.nist.gov/publications/exploration-combinatorial-testing-based-approaches-fault-localization-explainable-ai",
"source": "NIST Publications",
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"authors_en": "Ludwig Kampel, Dimitris Simos, D. Richard Kuhn , Raghu N. Kacker",
"abstract_excerpt_en": "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",
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{
"title_en": "An exploration of combinatorial testing-based approaches to fault localization for explainable AI",
"designation": "",
"series": "",
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"date": "2021-09-20",
"date_original": "September 20, 2021",
"url": "https://www.nist.gov/publications/exploration-combinatorial-testing-based-approaches-fault-localization-explainable-ai",
"source": "NIST Publications",
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"authors_en": "Ludwig Kampel, Dimitris Simos, D. Richard Kuhn , Raghu N. Kacker",
"abstract_excerpt_en": "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"
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