AI Explainability 360: An Extensible Toolkit for Understanding Data and Machine Learning Models

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. Journal of Machine Learning Research, 21, 2020. [doi]

@article{AryaBCDHHHLLMMP20-0,
  title = {AI Explainability 360: An Extensible Toolkit for Understanding Data and Machine Learning Models},
  author = {Vijay Arya and Rachel K. E. Bellamy and Pin-Yu Chen and Amit Dhurandhar and Michael Hind and Samuel C. Hoffman and Stephanie Houde and Q. Vera Liao and Ronny Luss and Aleksandra Mojsilovic and Sami Mourad and Pablo Pedemonte and Ramya Raghavendra and John T. Richards and Prasanna Sattigeri and Karthikeyan Shanmugam and Moninder Singh and Kush R. Varshney and Dennis Wei and Yunfeng Zhang},
  year = {2020},
  url = {http://jmlr.org/papers/v21/19-1035.html},
  researchr = {https://researchr.org/publication/AryaBCDHHHLLMMP20-0},
  cites = {0},
  citedby = {0},
  journal = {Journal of Machine Learning Research},
  volume = {21},
}