In this timely and ambitious work, Koopman explores how data technologies, often taken to be neutral tools, can perpetuate inequality when built on flawed notions of algorithmic fairness. It is not enough, Koopman explains, that algorithms engage everyone’s data with the same measuring stick. Input data are all too often structured in ways that obscure and exacerbate stratifying distinctions and hierarchies. Koopman contends that we must also work to ensure that those people subject to computational assessment enter data systems on equal terms. Part philosophical argument, part practical guide (focusing on AI-driven personalization in education technology), Data Equals offers novel methods for realizing democratic equality in a digital age.
