Journal article

XAI-Explainable artificial intelligence

David Gunning, Mark Stefik, Jaesik Choi, Timothy Miller, Simone Stumpf, Guang-Zhong Yang

Science Robotics | American Association for the Advancement of Science | Published : 2019

Abstract

Explainability is essential for users to effectively understand, trust, and manage powerful artificial intelligence applications.

Grants

Awarded by Institute for Information and Communications Technology Planning and Evaluation (IITP)


Awarded by Defense Advanced Research Projects Agency (DARPA)


Funding Acknowledgements

J.C. was supported by an Institute for Information and Communications Technology Planning and Evaluation (IITP) grant (no. 2017-0-01779; A machine learning and statistical inference framework for explainable artificial intelligence). Material within this technical publication is based on the work supported by the Defense Advanced Research Projects Agency (DARPA) under contract FA8650-17-C-7710 (to M.S.). Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the official policy or position of the Department of Defense or the U.S. government.