@conference {maity_statistical_2020,
	title = {Statistical inference for individual fairness},
	booktitle = {The Ninth International Conference on Learning Representations},
	year = {2021},
	abstract = {As we rely on machine learning (ML) models to make more consequential decisions, the issue of ML models perpetuating unwanted social biases has come to the fore of the public{\textquoteright}s and the research community{\textquoteright}s attention. In this paper, we focus on the problem of detecting violations of individual fairness in ML models. We formalize the problem as measuring the susceptibility of ML models against a form of adversarial attack and develop a suite of inference tools for the adversarial loss. The tools allow practitioners to assess the individual fairness of ML models in a statistically-principled way: form confidence intervals for the adversarial loss and test hypotheses of model fairness with (asymptotic) non-coverage/Type I error rate control. We demonstrate the utility of our tools in a real-world case study.},
	url = {https://openreview.net/forum?id=z9k8BWL-_2u},
	author = {Maity, Subha and Xue, Songkai and Yurochkin, Mikhail and Sun, Yuekai}
}
