http://www.privacysos.org/predictive

Another exaple of misguided comments about predictive policing. Some of the remarks are valid but the conclusion would lead to being pro in stead of contra predicitive policing.

"The accuracy of predictive policing programs depends on the accuracy of the information they are fed."There's a valid remark about the limitations. The data available are limited to the incidents reported. However, that's no different from the current situation. Police can only respond to reported incidents or to observed incidents. Their current reporting, analysis and decision making is also only focused on reported incidents. If there is a large discrepancy between reported incidents and actual incidents this is not a problem of the analytic approach used but a societal & cultural problem that should be discussed as such and not as a counter argument to the analytical approach being used.

In the follow up arguments the author suddenly moves from reported incidents to arrests, which are completely different events. Police presence is an important inhibitor for crime incidents1. The example used in this post is one of the possible application of predictive analytics in policing that focuses on force deployment. This application is not focused at proactive and preventative prosecution but to smarter deployment of police resources to pro-actively prevent crimes by being present based on reported incidents (not arrests).

There's no use of personal data of individuals. For this application data will for example be used about crimes (type of crime, location, time, modus operandi,...), about triggers of crime (local events (small & large), pay days, holidays,..), data about enablers of crime (police presence, weather, status of streetlights, other protection measures (f.e. cctv or private security firms),...) and information about the location (distance to ATM, number of empty buildings, average distance between buildings, alarm systems...).

Data relating to individuals can be of importance to use in other areas of Crime Prediction & Prevention. For example models that predict repeat victimization that allow the Police to better identify, protect and help people that are very vulnerable. Also in models for predicting repeat offenders this type of data may play a role. Having a better insight in re-offending risks allows for better offender management (trying to prevent people from recidivism).

“The predictive policing model is deceptive and problematic because it presumes that data inputs and algorithms are neutral, and therefore that the information the computer spits out will present police officers with objective, discrimination-free leads on where to send officers or deploy other resources. This couldn't be farther from the truth.”

In this statement the author is partly correct. Which also implies (s)he's partly incorrect. The algorithms are neutral but they function within a normative environment that determines the way decisions are being made. These norms are set not by the analytical approach but by the societal/political context. On the input side they are influenced by limitations (privacy, legal,

1 See for example:http://www.fsu.edu/news/2005/06/24/more.cops/http://www.cops.usdoj.gov/Publications/e040825133-web.pdfhttp://gemini.gmu.edu/cebcp/KoperHotSpots.pdf

technical, ...) and choices (political (what constitutes a crime?) type of crimes, geographic divisions, information recorded,....) made in the collection of data. On the output side there's a wealth of influencers that the determine how the results of the algorithms are being used in decision making. Major influencers on the output side are:➢ Political:

How important is crime on the political agenda? Which crimes are “high priority crimes”? How do we measure police performance? Number of arrests? Number of

Convictions? Number of “high priority crimes”? …... What budget is allocated to the police? …..

➢ Police strategy Is our strategy focused on or what is composition of the the mix of focus on

repression, prevention, solution,...? How are we translating the political objectives to organizational goals &

measurement? How are we organizing departments and allocating budgets to the different

branches/activities (patrol, investigative, offender management, traffic, citizen services,....)?

How are we going to make our decisions and what are we basing it on? …..

➢ Police tactics How are we going to work? Who are we working with? How are we going to tackle specific issues? …..

➢ Police operations How are we going to decide where to deploy which resources? How many resources do we have available? How is that going to influence our goals? …...

This is not different from the processes the police is currently using. The difference is that if it is based on the data in stead of human interpretation is used more consistently and in the same way across all entities, minimizing the effect of prejudices & biases and other human decision making frailties and judgment errors. It also makes police decision making more transparent since the algorithms will only deliver the required results if the environment in which they function has been made more explicit. I usually refer to this as the decisioning architecture: the definition and rules on how algorithms will be used for decision making within the political, strategical, tactical and operational objectives, goals, opportunities and constraints. This decisioning architecture is defined and therefor influenced not by the algorithms or the technology but by the societal, political and management layers surrounding the application.

“Law officers like to say that predictive policing helps them dodge questions about racism and unequal policing. But data isn't neutral, and neither are the algorithms tasked to sort through and make sense of those pieces of information.”

Predictive Policing will, if it is place within the context of a decisioning architecture, advance transparent decision making and enable eradication of prejudice and bias in police decision making as far as the political (and societal) environment forces them to. The police will also be more accountable.

“It's sort of a cultural axiom in the United States that high-powered bankers and lawyers have tastes for expensive cocaine and prostitutes”The author is exactly doing what (s)he accuses the police of. “Cultural axiom” is a euphemism for “prejudice”. Is there any data supporting this or is it based on urban myths? Or maybe the author fell into the traps of decision making heuristics?2

“If an algorithm is only fed unjust arrest data, it will simply repeat the injustice by advising the police to send yet more officers to patrol the black area. In that way, predictive policing creates a feedback loop of injustice.”The opposite is true. It will require the identification for each arrest if it was unjust or not. For example as a separate field in the registration or based on the outcome of the arrest (for example “subject released within x hours”). Using predictive modelling each arrest could be evaluated on “unjustness”. The models would also give police management insight into the drivers of unjust arrests. They could then change their strategy, tactics and policies to try and prevent them.

2 http://en.wikipedia.org/wiki/Heuristics_in_judgment_and_decision_making