Abstract
We study an online classification problem with partial feedback in which individuals arrive one at a time from a fixed but unknown distribution, and must be classified as positive or negative. Our algorithm only observes the true label of an individual if they are given a positive classification. This setting captures many classification problems for which fairness is a concern: for example, in criminal recidivism prediction, recidivism is only observed if the inmate is released; in lending applications, loan repayment is only observed if the loan is granted. We require that our algorithms satisfy common statistical fairness constraints (such as equalizing false positive or negative rates - introduced as “equal opportunity” in [18]) at every round, with respect to the underlying distribution. We give upper and lower bounds characterizing the cost of this constraint in terms of the regret rate (and show that it is mild), and give an oracle efficient algorithm that achieves the upper bound.
| Original language | English (US) |
|---|---|
| Journal | Advances in Neural Information Processing Systems |
| Volume | 32 |
| State | Published - 2019 |
| Event | 33rd Annual Conference on Neural Information Processing Systems, NeurIPS 2019 - Vancouver, Canada Duration: Dec 8 2019 → Dec 14 2019 |
Bibliographical note
Funding Information:We thank Nati Srebro for a conversation leading to the question we study here. We thank Michael Kearns for helpful discussions at an early stage of this work. YB and KL were funded in part by Israel Science Foundation (ISF) grant 1044/16, the United States Air Force and DARPA under contract FA8750-16-C-0022, and the Federmann Cyber Security Center in conjunction with the Israel national cyber directorate. AR was funded in part by NSF grant CCF-1763307 and the United States Air Force and DARPA under contract FA8750-16-C-0022. ZSW was supported in part by a Google Faculty Research Award, a J.P. Morgan Faculty Award, a Mozilla research grant, and a Facebook Research Award. Part of this work was done while KL and ZSW were visiting the Simons Institute for the Theory of Computing, and BW was a postdoc at the University of Pennsylvania's Warren Center and at Microsoft Research, New York City. Any opinions, findings and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of JP Morgan, the United States Air Force and DARPA.
Funding Information:
We thank Nati Srebro for a conversation leading to the question we study here. We thank Michael Kearns for helpful discussions at an early stage of this work. YB and KL were funded in part by Israel Science Foundation (ISF) grant 1044/16, the United States Air Force and DARPA under contract FA8750-16-C-0022, and the Federmann Cyber Security Center in conjunction with the Israel national cyber directorate. AR was funded in part by NSF grant CCF-1763307 and the United States Air Force and DARPA under contract FA8750-16-C-0022. ZSW was supported in part by a Google Faculty Research Award, a J.P. Morgan Faculty Award, a Mozilla research grant, and a Facebook Research Award. Part of this work was done while KL and ZSW were visiting the Simons Institute for the Theory of Computing, and BW was a postdoc at the University of Pennsylvania’s Warren Center and at Microsoft Research, New York City. Any opinions, findings and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of JP Morgan, the United States Air Force and DARPA.
Publisher Copyright:
© 2019 Neural information processing systems foundation. All rights reserved.
