Article by Peter M. Asaro.
Published in IEEE Technology and Society Magazine.
The adoption of data-driven organizational management – which includes big data, machine learning, and artificial intelligence techniques – is growing rapidly across all sectors of the knowledge economy. There is little doubt that the collection, dissemination, analysis, and use of data in government policy formation, strategic planning, decision execution, and the daily performance of duties can improve the functioning of government and the performance of public services. This is as true for law enforcement as any other government service.
Significant concerns have been raised, however, around the use of data-driven algorithms in policing, law enforcement and judicial proceedings. This includes predictive policing–the use of historic crime data to identify individuals or geographic areas with elevated risks for future crimes, in order to target them for increased policing. Predictive policing has been controversial for multiple reasons, including questions of prejudice and precrime and effectively treating people as guilty of (future) crimes for acts they have not yet committed and may never commit. This central controversy over prejudice and precrime is amplified and exacerbated by concerns over the implicit biases contained in historic data sets, and the obvious implications for racial, gendered, ethnic, religious, class, age, disability, and other forms of discriminatory policing, as well as how it shapes the psychology and behavior of police officers . . .
About the Author
Peter Asaro is a philosopher of science, technology and media. His work examines artificial intelligence and robotics as a form of digital media, the ethical dimensions of algorithms and data, and the ways in which technology mediates social relations and shapes our experience of the world. He is Associate Professor and Director of Graduate Studies in the School of Media Studies at The New School, New York, NY. He is also Visiting Professor at the Munich Center for Technology in Society at TU Munich, and Affiliate Scholar at Stanford Law School’s Center for Internet and Society.