Author Description

Learning a fair prediction model is an important research problem with profound societal impacts. Most approaches assume free access to the sensitive demographic data, whereas these data are becoming restricted to use by privacy regulations. Existing solutions are broadly based on multi-party computation or demographic proxy, but each direction has its own limits in certain scenarios. In this paper, we propose a new direction called active demographic query. We assume sensitive demographic data can be queried with cost, e.g., a company may pay to get a customer's consent on using his data. Building on Dwork's decoupled fair models, we propose two active query strategies: the queries for the most controversial data (QmCo) maximally disagreed by the decoupled models, and the queries for the most resistant data (QmRe) maximally deteriorating fairness of the current model. In experiment, we show both strategies efficiently improve model fairness on three data sets.