Policing ‘troubled families’ through ‘algorithmic regulation’

These dynamic systems can handle unstructured, messy and unpredictable data and respond in real-time to new ways of acting on patterns in the data. Like a shoal of startled fish, the application of this heterogeneous data can sharply change direction at any moment. (McQuillan, 2015: 3)

There are tools available that can help authorities to identify the most vulnerable children and families in their area, even as their circumstances change. Technology can now automatically alert a social worker when a child starts to miss lessons or allow a youth offending practitioner to instantly pull up a history of school exclusions for a child they are visiting. (Neal, 2014)

An interesting, but generally overlooked, aspect of the Troubled Families Programme is the increasing reliance and emphasis on data related issues. I often come across articles on the internet from software companies and management consultancy firms offer ‘data solutions’ to some of the challenges presented by working with ‘complex’ families. The quote above by Phil Neal, MD of Private Eye favourite C(r)apita One is taken from this post on the Public Servant Daily here and other examples, including different ones by Neal, can be found here, here, here and here

I may never have got around to posting this blog, if I hadn’t been sent a brilliant and disturbing paper by Dan McQuillan on ‘Algorithmic states of exception’. It’s very difficult to summarise the paper, so I’m not going to try to do that in the hope that you’ll be sufficiently intrigued to read it yourselves, but McQuillan makes the point that new data structures and new types of database allow for a form of ‘algorithmic regulation’ which can be used to ‘predict’, ‘identify’ and ‘modify’ social problems as they are envisaged by the government. He uses the Troubled Families Programme as an example and when I read this section, I was instantly reminded of a paragraph from Donzelot’s classic text The policing of families. The two excerpts are below, both emphases are mine:

At the start, there are always the figures on delinquency, the statistics of offences committed by minors. Experts in criminology study this first layer and detect in the delinquent minors’ past, in the organization of families, the signs they have in common, the invariables of their situation, the first symptoms of their bad actions. With the help of these findings, the typical portrait of the future delinquent, the predelinquent, the child in danger of becoming dangerous, can be drawn up. An infrastructure of prevention will then be erected around him, and an educative machinery will be set in motion, a timely action capable of stopping him short of a criminal violation. Not only will he be an object of intervention, but by the same token, he will in turn become an object of knowledge. The family climate, the social context that causes a particular child to become a “risk,” will be thoroughly studied. The catalogue of these indications makes it possible to encompass all forms of maladjustment, so as to construct a second circle of prevention. (Donzelot 1977: 97-98)

The way society disciplines citizens through discourses of health, criminality, madness and security (Foucault, 1977) are given categorical foundations in the structures of data. Consider, for example, the category of ‘troubled families’ created by the Department for Communities and Local Government (Department for Communities and Local Government, 2014) to identify families as requiring specific forms of intervention from the agencies in contact with them. The 40,000 or so families whose ‘lives have been turned around’, by being assigned a single keyworker tasked with getting them in to work and their children back to school on a payment by results model, would have been identified through some operations on the data fields that make their existence legible to the government. In turn, various agencies and processes would have operated on those individuals as both effect and affect, as a created intensity of experiential state, in ways that would construct the subjectivity of membership of a so-called troubled family. These actions would, in turn, become new content for data fields and would form the substrate for future interventions. Thus, the proliferation of data does not simply hedge the privacy of enlightenment individuals but produces new subjectivities and forms of action. (McQuillan, 2015: 2)

Essentially, we have seen Donzelot’s prediction ‘re-booted’ and put in a cloud via a ‘technological turn’. McQuillan argues that, as a result of new data structures and increased contact with technology, ‘everyday life is becoming permeated by points of contact with algorithmic systems that can influence the friction or direction of our experience’ (2105: 5). When these technologies seep into the public sector and the realm of social control, it affords states the opportunity to ‘predict’ who will do what in the future, and establish procedures, sanctions and exclusions to stop them from doing so. Of course, some populations, such as those deemed to be ‘troubled’ will have more ‘points of contact’ with the state than others and some will already be ‘known’ to agents of the state.

And then, serendipitously, DCLG published a document on Data Sharing Guidance and Principles, as an Annex to the new Financial Framework for the expanded phase of the Troubled Families Programme. The document suggests ways that the maximum amount of personal and familial data can be shared between service providers in a ‘legally compliant manner’ across each of the six criteria for being a ‘troubled family’. And, as if that wasn’t enough, a final section highlights how frontline workers can ‘nominate’ families where they believe there exist ‘problems of equivalent concern’:

These indicators enable nominations from professionals locally and, depending on the nature of the risk and seriousness of the circumstances, may be undertaken with or without the individual’s consent. In some cases, consent must be obtained by law before a nomination is made. However, in cases where consent is not prescribed by law, individuals should be made aware that their data is being shared and their consent should be sought wherever possible. However, this will be a matter for local assessment and professional judgment in the circumstances of each case (DCLG, 2015: 7)

Nothing to worry about there then….



Donzelot, J. (1997) The Policing of Families, Johns Hopkins: Baltimore

McQuillan, D (2015, forthcoming) ‘Algorithmic States of Exception’, European Journal of Cultural Studies, Vol 18, No 4-5, ISSN 1367-5494


Paul Garrett has also written previously about the ‘electronic turn’ in social work which highlights similar concerns and also highlights some of the potential benefits of social workers engaging with new forms of technology.

And, finally, many thanks to the individual who bought Dan McQuillan’s paper to my attention. Much appreciated.

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