We study the problem of non-disclosively collecting a sample of data that is
balanced with respect to sensitive groups when group membership is unavailable
or prohibited from use at collection time. Specifically, our collection
mechanism does not reveal significantly more about group membership of any
individual sample than can be ascertained from base rates alone. To do this, we
adopt a fairness pipeline perspective, in which a learner can use a small set
of labeled data to train a proxy function that can later be used for this
filtering task. We then associate the range of the proxy function with sampling
probabilities; given a new candidate, we classify it using our proxy function,
and then select it for our sample with probability proportional to the sampling
probability corresponding to its proxy classification. Importantly, we require
that the proxy classification itself not reveal significant information about
the sensitive group membership of any individual sample (i.e., it should be
sufficiently non-disclosive). We show that under modest algorithmic
assumptions, we find such a proxy in a sample- and oracle-efficient manner.
Finally, we experimentally evaluate our algorithm and analyze generalization
properties.

本研究旨在解决群体成员身份不可用或不被允许使用时，非披露性地收集与敏感群体平衡的数据样本的问题，并通过采用公平性流程视角和使用少量标记数据训练代理函数来实现。