Studying up: reorienting the study of algorithmic fairness around issues of power

Abstract

Research within the social sciences and humanities has long characterized the work of data science as a sociotechnical process, comprised of a set of logics and techniques that are inseparable from specific social norms, expectations and contexts of development and use. Yet all too often the assumptions and premises underlying data analysis remain unexamined, even in contemporary debates about the fairness of algorithmic systems. This blindspot exists in part because the methodological toolkit used to evaluate the fairness of algorithmic systems remains limited to a narrow set of computational and legal modes of analysis. In this paper, we expand on Elish and Boyd's [17] call for data scientists to develop more robust frameworks for understanding their work as situated practice by examining a specific methodological debate within the field of anthropology, frequently referred to as the practice of "studying up". We reflect on the contributions that the call to "study up" has made in the field of anthropology before making the case that the field of algorithmic fairness would similarly benefit from a reorientation "upward". A case study from our own work illustrates what it looks like to reorient one's research questions "up" in a high-profile debate regarding the fairness of an algorithmic system - namely, pretrial risk assessment in American criminal law. We discuss the limitations of contemporary fairness discourse with regard to pretrial risk assessment before highlighting the insights gained when we reframe our research questions to focus on those who inhabit positions of power and authority within the U.S. court system. Finally, we reflect on the challenges we have encountered in implementing data science projects that "study up". In the process, we surface new insights and questions about what it means to ethically engage in data science work that directly confronts issues of power and authority.