Bell, Andrew, Solano-Kamaiko, Ian, Nov, Oded, and Stoyanovich, Julia. It’s Just Not That Simple: An Empirical Study of the Accuracy-Explainability Trade-off in Machine Learning for Public Policy. Retrieved from https://par.nsf.gov/biblio/10348016. ACM Conference on Fairness, Accountability, and Transparency 2022 Pages 248–266 . Web. doi:10.1145/3531146.3533090.

Bell, Andrew, Solano-Kamaiko, Ian, Nov, Oded, & Stoyanovich, Julia. It’s Just Not That Simple: An Empirical Study of the Accuracy-Explainability Trade-off in Machine Learning for Public Policy. ACM Conference on Fairness, Accountability, and Transparency 2022 Pages 248–266, (). Retrieved from https://par.nsf.gov/biblio/10348016. https://doi.org/10.1145/3531146.3533090

Bell, Andrew, Solano-Kamaiko, Ian, Nov, Oded, and Stoyanovich, Julia. "It’s Just Not That Simple: An Empirical Study of the Accuracy-Explainability Trade-off in Machine Learning for Public Policy". ACM Conference on Fairness, Accountability, and Transparency 2022 Pages 248–266 (). Country unknown/Code not available. https://doi.org/10.1145/3531146.3533090. https://par.nsf.gov/biblio/10348016.

@article{osti_10348016,
place = {Country unknown/Code not available}, title = {It’s Just Not That Simple: An Empirical Study of the Accuracy-Explainability Trade-off in Machine Learning for Public Policy}, url = {https://par.nsf.gov/biblio/10348016}, DOI = {10.1145/3531146.3533090}, abstractNote = {}, journal = {ACM Conference on Fairness, Accountability, and Transparency 2022 Pages 248–266}, author = {Bell, Andrew and Solano-Kamaiko, Ian and Nov, Oded and Stoyanovich, Julia}, }