@conference {1521044,
	title = {An Empirical Study of the Trade-Offs Between Interpretability and Fairness},
	booktitle = {ICML 2020 Workshop on Human Interpretability in Machine Learning, preliminary version},
	year = {2020},
	abstract = {As machine learning models are increasingly being deployed in critical domains such as criminal justice and healthcare, there has been a growing interest in developing algorithms that are interpretable and fair. While there has been a lot of research on each of these topics in isolation, there has been little work on their intersection. In this paper, we present an empirical study for understanding the relationship between model interpretability and fairness. To this end, we propose a novel evaluation framework and outline appropriate evaluation metrics to determine this relationship across various classes of models in both synthetic and real world datasets.},
	author = {Shahin Jabbari and Ou, Han-Ching and Himabindu Lakkaraju and Tambe, Milind}
}
