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.

  1. child protection services
  2. the probation service
  3. the royal mail
  4. emergency calls to the fire service
  5. the land registry
  6. the NHS blood service
  7. Eurostar
  8. large swathes of the school system
  9. significant policing functions
  10. the motorway network
  11. the met office
  12. ordnance survey
  13. companies house
  14. student loans
  15. the behavioural insights team
  16. Remploy
  17. the NHS
  18. forests
  19. Lloyds
  20. the courts service
  21. the prison system
  22. English Heritage
  23. the Defence Equipment and Support agency
  24. Food and Environment Research Agency
  25. the BBC
  26. the royal mint
  27. HMRC
  28. the forensic science service
  29. the policing of parliament
  30. job centre plus
  31. The Government Pipeline and Storage System

The list would be much longer if it included New Labour. How far will this go…?

My criteria for inclusion on the list are that a service has been privatised, has been subject to an attempted privatisation or that a putative privatisation has been discussed in the media. The concept of ‘privatisation’ is a bit inaccurate when applied across the list but I don’t think this detracts from the underlying point. There’s some quite specific qualifications which could be attached to many of the entries on the list but this isn’t the place for them. I was just interested to fuzzily map the scope of the privatisation agenda, getting beyond the drip-feed of news stories about particular services to try and think about the size of the broader trend.

There’s an absolutely cracking article by Aditya Chakrabortty in today’s Guardian reflecting on the privatisation of the railways. I was astounded to hear John Redwood cite the railways a couple of months ago as evidence in support of the privatisation of the royal mail. I’m fascinated by the question of how much, if at all, people believe this stuff. Or is it simply the case that the present system is straight-forwardly in the interests of too many influential people?

I asked academics at the Centre for Research on Socio-Cultural Change (Cresc) to calculate how much companies such as Virgin and First Group are investing in their services. They looked at their return on capital employed, which is to say the amount train operators made on the money tied up in their business. A low ratio would indicate an industry doing as Norris and his colleagues foretold: ploughing cash into delivering a better service. A really high ratio would indicate the opposite: barely any cash going in.

The figures are astonishing. In the financial year ending in March 2012, the train companies gained an average return of 147% on every pound they put into their business. Forget about high: that is stratospheric. It suggests that – despite all the promises made by the freshly rehabilitated John Major – the train operators are investing barely anything, but making bumper returns.

If you’re a pensioner, imagine a savings account that promised to give you next year a 147% return on your cash, rather than the 1% you’ll typically get now. If you’re a first-time buyer, imagine selling up next year at a 147% markup – impossible even in primest, most central London.

Other businesses would kill for the kind of low-investment, high-returns that Arriva, Stagecoach and the rest are making from their train sets. Big supermarkets get about £1.08 back for every quid they put in: all that stock ties up a lot of cash. Even the supposed profiteers over at Barclays would punch the air at a 10% return. For every pound the railway barons put in, they get £2.47 back.

And that most recent figure isn’t a fluke. The Cresc team went back all the way to the start of the electronic database in 2004, and found that year after year the pre-tax return on capital employed was never less than 100%. Just as remarkable are the train operators’ dividends: pretty much all the profit after tax was paid to shareholders.

No wonder Richard Branson is a billionaire with his own private island. No wonder Tim O’Toole, boss of FirstGroup, and Brian Souter, head of Stagecoach, are on more than a million quid a year each. They are rewarded handsomely for handing over every spare penny to their shareholders.

But by the same token, no wonder passengers in cattle class can’t get free Wi-Fi, or even a seat on the evening train out of Euston: there’s no cash left to make the services worth the often excessive fares. The really big improvements, such as the west coast mainline upgrade now enjoyed by Branson’s business, are funded by taxpayers. Heads they win, tails we lose.

http://www.theguardian.com/commentisfree/2013/nov/04/rail-privatisation-train-operators-profit

My defining image of the abstract concept of privatised railways is the long walk past a sequence of near empty first class carriages which so frequently greets those travelling to the midlands from Euston prior to their boarding a packed standard class. It is wasted capacity of a form so ridiculously gratuitous that it would be possible to inductively derive a thesis on the operation of neoliberal ideology from seeking to explain how invocations of public sector waste are taken so seriously yet something like this rarely even enters into the conversation, let alone becoming a part of the common sense which would see waste of this particular form as endemic to a ‘market’ of this type.

This line of thought has also crystallised into an increasing objection to HS2 in my own mind. At some point I would like to read about how capacity on the WCML is calculated because the long-term underuse of many first class carriages at peak times leave me slightly baffled as to how the cited capacity figures can be true. It would also be interesting to know what the 1st class to standard class capacity ratio was at varying times throughout the day.