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Sociological questions about the coming era of data-driven privatised policing

This insightful article paints a worrying picture of the growth of data-driven policing. The technical challenge of “building nuance” into data systems “is far harder than it seems” and has important practical implications for how interventions operate on the basis of digital data. What I hadn’t previously realised was how readily investigators are using social media on their own initiative above and beyond the systems that are being put into place with the help of outside consultancies: only 9% of police using social media in investigations had received training from their agency. Furthermore the discussion of the life span of data raised some really interesting (and worrying) questions about the organisational sociology of data-driven policing given what seems likely to be increasing involvement of the private sector in policing in the UK:

For the kid listed in a gang database, it can be unclear how to get out of it. In the world of human interaction, we accept change through behavior: the addict can redeem himself by getting clean, or the habitual interrupter can redeem himself by not interrupting. We accept behavior change. But in the database world, unless someone has permission to delete or amend a database record, no such change is possible. Credit agencies are required to forgive financial sins after 7 years. Police are not—at least, not consistently. The National Gang Center, in its list of gang-related legislation, shows only 12 states with policies that specifically address gang databases. Most deny the public access to the information in these databases. Only a few of these twelve mention regular purging of information, and some specifically say that a person cannot even find out if they have a record in the database.

This permanence does not necessarily match real-world conditions. Kids cycle in and out of street gangs the way they cycle in and out of any other social group, and many young men age out of violent behavior. Regularly purging the gang database, perhaps on a one-year or two-year cycle, would allow some measure of computational forgiveness. However, few institutions are good at keeping the data in their databases up-to-date. (If you’ve ever been served an ad for a product you just bought, you’re familiar with this problem of information persistence and the clumsiness of predictive algorithms.) The police are no worse and no better than the rest of us. Criminologist Charles Katz found that despite a written department policy in one large Midwestern police gang unit, data was not regularly audited or purged. “The last time that the gang unit purged its files, however, was in 1993—approximately 4 years before this study was conducted,” he wrote. “One clerk who is responsible for data entry and dissemination estimated, ‘At a minimum, 400 to 500 gang members would be deleted off the gang list today if we went through the files.’ Accordingly, Junction City’s gang list of 2,086 gang members was inflated by approximately 20% to 25%.”

http://www.theatlantic.com/politics/archive/2015/04/when-cops-check-facebook/390882/

This suggests to me that any adequate evaluation of data-driven policing needs to take questions of organisational sociology and information technology extremely seriously. What matters is not just the formulation of data management policies but what we know about how such policies tend to be implemented under the specific conditions likely to obtain in policing. Given the broader trend towards the privatisation of policing, it is increasingly important that we understand how sharing of data operates across organisational boundaries, how it is prepared and how it is perceived by end-users.

My fear is that a form of inter-organisational ‘black-boxing’ could kick in where those utilising the data for interventions trust that others have elsewhere taken responsibility for ensuring its reliability. What scrutiny would the operations of outside suppliers be subject to? Could privatisation intensify the rush towards data-driven policing in the name of efficiency savings? Would a corresponding centralisation of back-office functions compound the aforementioned epistemological risks entailed by outsourcing? These are all urgent questions which could easily be marginalised as budgetary constraint drives ‘innovation’ in policing: data-driven policing and privatised policing will likely go hand-in-hand and we need to analyse them as such.

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