Poster Paper: Disharmonious Skills and Values: Obstacles in Data Driven Policing

Saturday, November 5, 2016
Columbia Ballroom (Washington Hilton)

*Names in bold indicate Presenter

Kimberly Gardner and Eric Lindquist, Boise State University

Scholars in public policy ardently wish to provide policy makers with useful and actionable information that can support rational policy decisions in all phases of the policy cycle. To this end, much attention from academia is devoted to extolling the virtues of techniques infused with and dependent upon data as the logical alternative to techniques dependent on subjective experience, which require considerable resources and often lead to perverse outcomes. This framing of alternatives is valuable to policy scholars wishing to be relevant, as the promises of data driven techniques have indeed caught the attention of policy decision makers, especially in the field of criminal justice. For example, as Attorney General for the state of New Jersey, Ann Milgram sought to reform the criminal justice system by implementing a dashboard to aid judges in their discernment of an individual offender’s potential risk to reoffend. As governor of Maryland, Martin O’Malley implemented Citi stat, a system of data driven decision making, he claimed cut crime rates in the area by 43%.

The promises of data driven policies have also caught the attention of the current administration. In response to the tragic events of the fatal shootings such as those of Walter Brown in  Ferguson and Walter Scott in South Carolina, which have shattered trust between residents and police, the Obama administration released the White House Police Data Initiative, which seeks to encourage the delivery of policing services through data driven techniques that will not only decrease crime rates through greater efficiency, but will also increase government transparency through the opening up of government performance data. It is assumed that increases in the efficient delivery of services and government transparency will then lead to greater levels of satisfaction and trust among citizens.

A fly in this ointment is that the Initiative presumes that data driven policing techniques will be effective if they are implemented in tandem with the techniques associated with community oriented policing, yet community policing depends upon precisely the “subjective experience” and devoted resources that techniques dependent on data are intended to “correct.”  Data driven policing stresses skills and values generally associated with the “professional model of policing” which emphasizes outcomes such as reduced crime rates or number of arrests. Community oriented policing, on the other hand, emphasizes skills and values often associated with social case work, such as building relationships with community leaders and an intimate knowledge of place. In an era of limited resources in the public sector, it is not unlikely that one emphasis will take precedence and drive out the other. Through a textual analysis of government documents, this paper will show that the skills and values of data driven policing techniques are discordant with those of community oriented policing techniques. Further, the paper will show that the emphasis on data driven techniques may have the effect of displacing the resources needed to effectively carry out community oriented policing, which has been empirically shown to contribute to increased levels of trust between community residents and police officers.