Contesting and Rethinking Demographic Data Infrastructures for Algorithmic Fairness
Most current algorithmic fairness techniques require access to data on a “sensitive attribute” or “protected category” in order to make performance comparisons and standardizations across groups. In practice, however, data on demographic categories that inscribe the most risk of mistreatment (e.g. race, sexuality, nation of origin) are often unavailable, due in part to a range of organizational barriers and concerns related to antidiscrimination law, privacy policies, and the unreliability of self-reported, proxied, or inferred demographics. Beyond these practical constraints, however, FAccT, HCI, and Critical Studies scholars have surfaced many other issues with how technical work conceptualizes identities and communities in recent years, looking at categories such as race, gender, and disability. A key part of this work is exposing the ways in which these categories do not simply exist in nature--they are co-constructed and reproduced by the sociotechnical infrastructure built around them. Exploring this process of reproduction is thus key to understanding how, if at all, we should be infusing demographics into fairness tools. Additionally, work around issues such as data justice, big data abolition, and Indigenous Data Sovereignty has sought to center the ways in which data collection and use are wielded to exploit and further disempower individuals and their communities. These critiques point to the ways in which data centralization and ownership allows just a few individuals to determine what narratives and economic or political projects the data will be mobilized to support. While these types of work do not center around demographic data or algorithmic fairness specifically, these perspectives can help identify largely unexamined risks of algorithmic fairness’ data requirements. The goal of this workshop is to confront current demographic data collection practices, as well as the implications of continuing to design fairness interventions that presuppose demographic data availability. Through various narrative and speculative exercises, participants will build out a picture of the various underlying technical, legal, social, political, and environmental infrastructures necessary to support most proposed demographic-based algorithmic fairness techniques.