How is fairness measured in practice? A survey and critique of fairness analyses applied to predictive algorithms

Abstract

The increasingly rapid pace of research and development of prediction algorithms over the past decade has spurred their adoption across seemingly every industry and aspect of life. This growth and the subsequent increase in public awareness have brought a range of concerns into focus regarding the potential bias and fairness impacts of these algorithms. This cross-disciplinary survey assesses peer-reviewed publications from the last 10 years which assessed fairness in a predictive algorithm using real world data. We begin with a descriptive overview of the fields of research, types of publication, and the types of outcomes being predicted. We then engage in a more detailed exploration of the fairness analyses that were applied, the metrics used, and the evidence provided to justify these selections. We also discuss the contexts in which these analyses were conducted. We conclude by highlighting the lack of inclusion of perspectives from people who are impacted by the algorithms being analyzed and discuss directions for future work in this area.