Timezone: »

Fairness Transferability Subject to Bounded Distribution Shift
Yatong Chen · Reilly Raab · Jialu Wang · Yang Liu

Thu Dec 01 02:00 PM -- 04:00 PM (PST) @ Hall J #334

Given an algorithmic predictor that is "fair"' on some source distribution, will it still be fair on an unknown target distribution that differs from the source within some bound? In this paper, we study the transferability of statistical group fairness for machine learning predictors (i.e., classifiers or regressors subject to bounded distribution shift. Such shifts may be introduced by initial training data uncertainties, user adaptation to a deployed predictor, dynamic environments, or the use of pre-trained models in new settings. Herein, we develop a bound that characterizes such transferability, flagging potentially inappropriate deployments of machine learning for socially consequential tasks. We first develop a framework for bounding violations of statistical fairness subject to distribution shift, formulating a generic upper bound for transferred fairness violations as our primary result. We then develop bounds for specific worked examples, focusing on two commonly used fairness definitions (i.e., demographic parity and equalized odds) and two classes of distribution shift (i.e., covariate shift and label shift). Finally, we compare our theoretical bounds to deterministic models of distribution shift and against real-world data, finding that we are able to estimate fairness violation bounds in practice, even when simplifying assumptions are only approximately satisfied.

Author Information

Yatong Chen (UC Santa Cruz, Google Brain)
Reilly Raab (UC Santa Cruz)

My current research involves the dynamics of multiagent systems and the alignment of local incentives with global objectives. My background is in physics, with experience in scientific computing, signal processing, and electronics. I spent a few years between undergrad and grad school backpacking abroad, remotely developing software related to automated circuit design.

Jialu Wang (University of California, Santa Cruz)
Yang Liu (UC Santa Cruz)

More from the Same Authors