AI Explainability 360: Impact and Design


  • Vijay Arya IBM Research
  • Rachel K. E. Bellamy IBM Research
  • Pin-Yu Chen IBM Research
  • Amit Dhurandhar IBM Research
  • Michael Hind IBM Research
  • Samuel C. Hoffman IBM Research
  • Stephanie Houde IBM Research
  • Q. Vera Liao IBM Research
  • Ronny Luss IBM Research
  • Aleksandra Mojsilović IBM Research
  • Sami Mourad IBM Research
  • Pablo Pedemonte IBM Research
  • Ramya Raghavendra IBM Research
  • John Richards IBM Research
  • Prasanna Sattigeri IBM Research
  • Karthikeyan Shanmugam IBM Research
  • Moninder Singh IBM Research
  • Kush R. Varshney IBM Research
  • Dennis Wei IBM Research
  • Yunfeng Zhang IBM Research



AI Explainability, Open Source, Trusted AI


As artificial intelligence and machine learning algorithms become increasingly prevalent in society, multiple stakeholders are calling for these algorithms to provide explanations. At the same time, these stakeholders, whether they be affected citizens, government regulators, domain experts, or system developers, have different explanation needs. To address these needs, in 2019, we created AI Explainability 360, an open source software toolkit featuring ten diverse and state-of-the-art explainability methods and two evaluation metrics. This paper examines the impact of the toolkit with several case studies, statistics, and community feedback. The different ways in which users have experienced AI Explainability 360 have resulted in multiple types of impact and improvements in multiple metrics, highlighted by the adoption of the toolkit by the independent LF AI & Data Foundation. The paper also describes the flexible design of the toolkit, examples of its use, and the significant educational material and documentation available to its users.




How to Cite

Arya, V., Bellamy, R. K. E., Chen, P.-Y., Dhurandhar, A., Hind, M., Hoffman, S. C., Houde, S., Liao, Q. V., Luss, R., Mojsilović, A., Mourad, S., Pedemonte, P., Raghavendra, R., Richards, J., Sattigeri, P., Shanmugam, K., Singh, M., Varshney, K. R., Wei, D., & Zhang, Y. (2022). AI Explainability 360: Impact and Design. Proceedings of the AAAI Conference on Artificial Intelligence, 36(11), 12651-12657.



IAAI Technical Track on Innovative Tools for Enabling AI Application