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"AI Explainability 360: An Extensible Toolkit for Understanding Data and ..."

Vijay Arya et al. (2020)

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DOI: —

access: open

type: Journal Article

metadata version: 2023-09-30

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  - electronic edition @ jmlr.org (open access)
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  dblp key:
  - journals/jmlr/AryaBCDHHHLLMMP20
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  persistent URL:
  - https://dblp.org/rec/journals/jmlr/AryaBCDHHHLLMMP20
Vijay Arya, Rachel K. E. Bellamy, Pin-Yu Chen, Amit Dhurandhar, Michael Hind, Samuel C. Hoffman, Stephanie Houde, Q. Vera Liao, Ronny Luss, Aleksandra Mojsilovic, Sami Mourad, Pablo Pedemonte, Ramya Raghavendra, John T. Richards, Prasanna Sattigeri, Karthikeyan Shanmugam, Moninder Singh, Kush R. Varshney, Dennis Wei, Yunfeng Zhang:
AI Explainability 360: An Extensible Toolkit for Understanding Data and Machine Learning Models. J. Mach. Learn. Res. 21: 130:1-130:6 (2020)

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