training it with data that’s generated by the same system in which
minority people are more likely to be arrested for the same behaviour,
you’re just going to perpetuate those same issues,’ she said. ‘You
have the same problems, but now filtered through this high-tech tool.’
Crime algorithms have more limitations than people might think. In
2013, researchers at RAND Corporation outlined four common myths
about predictive policing.[83] The first was that a computer knows
exactly what will happen in the future. ‘These algorithms predict the
risk of future events, not the events themselves,’ they noted. The
second myth was that a computer would do everything, from
collecting relevant crime data to making appropriate
recommendations. In reality, computers work best when they assist
human analysis and decisions about policing, rather than replacing
them entirely. The third myth was that police forces needed a high-
powered model to make good predictions, whereas often the problem
is getting hold of the right data. ‘Sometimes you have a dataset
where the information you need to make the prediction just isn’t
contained in that dataset,’ as Lum put it.
The final, and perhaps most persistent myth, was that accurate
predictions automatically lead to reductions in crime. ‘Predictions, on
their own, are just that – predictions,’ wrote the RAND team. ‘Actual
decreases in crime require taking action based on those predictions.’
To control crime, agencies therefore need to focus on interventions
and prevention rather than simply making predictions. This is true for
other outbreaks too. According to Chris Whitty, now the Chief Medical
Officer for England, the best mathematical models are not necessarily
the ones that try to make an accurate forecast about the future. What
matters is having analysis that can reveal gaps in our understanding
of a situation. ‘They are generally most useful when they identify
impacts of policy decisions which are not predictable by
commonsense,’ Whitty has suggested. ‘The key is usually not that
they are “right”, but that they provide an unpredicted insight.’[84]
I 2012, introduced the ‘Strategic Subjects List’
(SSL) to predict who might be involved in a shooting. The project was
partly inspired by Andrew Papachristos’s work on social networks and