In information societies, operations, decisions and choices previously left to humans are increasingly delegated to algorithms, which may advise, if not decide, about how data should be interpreted and what actions should be taken as a result. More and more often, algorithms mediate social processes, business transactions, governmental decisions, and how we perceive, understand, and interact among ourselves and with the environment. Gaps between the design and operation of algorithms and our understanding of their ethical implications can have severe consequences (...) affecting individuals as well as groups and whole societies. This paper makes three contributions to clarify the ethical importance of algorithmic mediation. It provides a prescriptive map to organise the debate. It reviews the current discussion of ethical aspects of algorithms. And it assesses the available literature in order to identify areas requiring further work to develop the ethics of algorithms. (shrink)

Predictive uses of data are becoming widespread in institutional settings as actors seek to anticipate people and their activities. Predictive modeling is increasingly the subject of scholarly and public criticism. Less common, however, is scrutiny directed at the data that inform predictive models beyond concerns about homogenous training data or general epistemological critiques of data. In this paper, I draw from a qualitative case study set in higher education in the United States to investigate the making of data. Data analytics (...) projects at universities have become more pervasive and intensive to better understand and anticipate undergraduate student bodies. Drawing from 12 months of ethnographic research at a large public university, I analyze the ways data personnel at the institution—data scientists, administrators, and programmers—sort student data into “attributes” and “behaviors,” where “attributes” are demographic data that students “can’t change.” “Behaviors,” in contrast, are data defined as reflective of what students can choose: attending and paying attention in class, studying on campus, among other data which personnel categorize as what students have control over. This discursive split enables the institution nudge students to make responsible choices according to behavior data that correlate with success in the predictive model. In discussing how personnel type, sort, stabilize, and nudge on behavior data, this paper examines the contingencies of data making processes and implications for the application of student data. (shrink)