%0 Conference Paper %1 ijcai2017-371 %A Ross, Andrew Slavin %A Hughes, Michael C. %A Doshi-Velez, Finale %B Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence, IJCAI-17 %D 2017 %K ai explainable function lime loss models %P 2662--2670 %R 10.24963/ijcai.2017/371 %T Right for the Right Reasons: Training Differentiable Models by Constraining their Explanations %U https://doi.org/10.24963/ijcai.2017/371

@inproceedings{ijcai2017-371, added-at = {2020-09-16T10:46:54.000+0200}, author = {Ross, Andrew Slavin and Hughes, Michael C. and Doshi-Velez, Finale}, biburl = {https://www.bibsonomy.org/bibtex/2955bd7fb8fd2b2839b02911e6254edc9/schwemmlein}, booktitle = {Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence, {IJCAI-17}}, doi = {10.24963/ijcai.2017/371}, interhash = {e3d5b2f7141ad065b3668706319b08c4}, intrahash = {955bd7fb8fd2b2839b02911e6254edc9}, keywords = {ai explainable function lime loss models}, pages = {2662--2670}, timestamp = {2020-09-16T10:46:54.000+0200}, title = {Right for the Right Reasons: Training Differentiable Models by Constraining their Explanations}, url = {https://doi.org/10.24963/ijcai.2017/371}, year = 2017 }