Catherine Kung, Renzhe Yu. Interpretable Models Do Not Compromise Accuracy or Fairness in Predicting College Success. In David Joyner, René F. Kizilcec, Susan Singer, editors, L@S'20: Seventh ACM Conference on Learning @ Scale, Virtual Event, USA, August 12-14, 2020. pages 413-416, ACM, 2020. [doi]
@inproceedings{KungY20,
title = {Interpretable Models Do Not Compromise Accuracy or Fairness in Predicting College Success},
author = {Catherine Kung and Renzhe Yu},
year = {2020},
doi = {10.1145/3386527.3406755},
url = {https://doi.org/10.1145/3386527.3406755},
researchr = {https://researchr.org/publication/KungY20},
cites = {0},
citedby = {0},
pages = {413-416},
booktitle = {L@S'20: Seventh ACM Conference on Learning @ Scale, Virtual Event, USA, August 12-14, 2020},
editor = {David Joyner and René F. Kizilcec and Susan Singer},
publisher = {ACM},
isbn = {978-1-4503-7951-9},
}