1.**A human-grounded evaluation benchmark for local explanations of machine learning.**
*Sina Mohseni and Eric D Ragan.*2018. [paper](https://arxiv.org/pdf/1801.05075).
Marco Tulio Ribeiro, Sameer Singh, and Carlos Guestrin. Anchors: High-precision model- agnostic explanations. In AAAI Conference on Artificial Intelligence (AAAI), 2018.
*Marco Tulio Ribeiro, Sameer Singh, and Carlos Guestrin.*AAAI 2018. [paper]
Andrew Slavin Ross, Michael C. Hughes, and Finale Doshi-Velez. Right for the right reasons: Training differentiable models by constraining their explanations. In Pro- ceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence, IJCAI 2017, Melbourne, Australia, August 19-25, 2017, pages 2662–2670, 2017a. doi: 10.24963/ijcai.2017/371. URL https://doi.org/10.24963/ijcai.2017/371.
Andrew Slavin Ross, Michael C Hughes, and Finale Doshi-Velez. Right for the right rea- sons: Training differentiable models by constraining their explanations. arXiv preprint arXiv:1703.03717, 2017b.
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1.**Right for the right reasons: Training differentiable models by constraining their explanations.**
*Andrew Slavin Ross, Michael C. Hughes, and Finale Doshi-Velez.* IJCAI 2018. [paper](https://doi.org/10.24963/ijcai.2017/371)
