It’s Just Not That Simple: An Empirical Study of the Accuracy-Explainability Trade-off in Machine Learning for Public Policy

Author:

Bell Andrew1,Solano-Kamaiko Ian1,Nov Oded1,Stoyanovich Julia1

Affiliation:

1. New York University, USA

Funder

National Science Foundation

Publisher

ACM

Reference73 articles.

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4. Hiva Allahyari and Niklas Lavesson . 2011 . User-oriented Assessment of Classification Model Understandability. In Eleventh Scandinavian Conference on Artificial Intelligence, SCAI 2011 , Trondheim, Norway, May 24th - 26th , 2011(Frontiers in Artificial Intelligence and Applications, Vol. 227), Anders Kofod-Petersen, Fredrik Heintz, and Helge Langseth (Eds.). IOS Press, 11–19. https://doi.org/10.3233/978-1-60750-754-3-11 10.3233/978-1-60750-754-3-11 Hiva Allahyari and Niklas Lavesson. 2011. User-oriented Assessment of Classification Model Understandability. In Eleventh Scandinavian Conference on Artificial Intelligence, SCAI 2011, Trondheim, Norway, May 24th - 26th, 2011(Frontiers in Artificial Intelligence and Applications, Vol. 227), Anders Kofod-Petersen, Fredrik Heintz, and Helge Langseth (Eds.). IOS Press, 11–19. https://doi.org/10.3233/978-1-60750-754-3-11

5. Kasun Amarasinghe Kit Rodolfa Hemank Lamba and Rayid Ghani. 2020. Explainable machine learning for public policy: Use cases gaps and research directions. arXiv preprint arXiv:2010.14374(2020). Kasun Amarasinghe Kit Rodolfa Hemank Lamba and Rayid Ghani. 2020. Explainable machine learning for public policy: Use cases gaps and research directions. arXiv preprint arXiv:2010.14374(2020).

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