know where crime is occurring, they generally have to rely on what’s
being reported. When it comes to using models to predict crime, this
can create problems. In 2016, statistician Kristian Lum and political
scientist William Isaac published an example of how reporting might
influence predictions.[81] They’d focused on drug use in Oakland,
California. First they’d gathered data on drug arrests in 2010, and
then plugged these into the PredPol algorithm, a popular tool for
predictive policing in the US. Such algorithms are essentially
translation devices, taking information about an individual or location
and converting it into an estimate of crime risk. According to the
developers of PredPol, their algorithm uses only three pieces of data
to make predictions: the type of historical crime, the place it
happened and when it happened. It doesn’t explicitly include any
personal information – like race or gender – that could directly bias
results against certain groups.
Using the PredPol algorithm, Lum and Isaac predicted where drug
crimes would have been expected to occur in 2011. They also
calculated the actual distribution of drug crimes that year – including
those that went unreported – using data from the National Survey on
Drug Use and Health. If the algorithm’s predictions were accurate,
they would have expected it to flag up the areas where the crimes
actually happened. But instead, it seemed to point mostly to areas
where arrests had previously occurred. The pair noted that this could
produce a feedback loop between understanding and controlling
crime. ‘Because these predictions are likely to over-represent areas
that were already known to police, officers become increasingly likely
to patrol these same areas and observe new criminal acts that
confirm their prior beliefs regarding the distributions of criminal
activity.’[82]
Some people criticised the analysis, arguing that police didn’t use
Predpol to predict drug crimes. However, Lum said that this is missing
the wider point because the aim of predictive policing methods is to
make decisions more objective. ‘The implicit argument is that you
want to remove human bias from the system.’ If predictions reflect
existing police behaviour, however, these biases will persist, hidden
behind a veil of a supposedly objective algorithm. ‘When you’re