Besides the possible economic burden, the task of making the algorithmic process transparent is not necessarily a simple one. For obvious strategic reasons, the police would not want their predictive algorithms too transparent, as it might give criminals an advantage. If one wants the algorithms to be understandable by non-experts, the models have to be simplified. Models that are too simplified may have to sacrifice the accuracy that made them desirable in the first
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place (Ridgeway 2013, 38). It has also been pointed out that a system where one may be apprehended without anyone really being able to explain why, opens the door for increasingly Kafkaesque scenarios.51 Insight into why one has been selected by the algorithmic process may therefore prove essential if predictive policing is to take civil liberties into consideration. As Ferguson points out, some of these concerns are already being addressed in the pilot programs for PredPol. In the program launched by the LAPD, a blind test has been used to determine the actual effects of the system. In addition, there is a continuous monitoring of the project by independent academics and other experts, who are granted insight into the process and the system (Ferguson 2012, 320). The department uses three years of crime statistics, supplementing the older stats with continually updated crime data to ensure that the
predictions are relevant. Similar checks and balances could possibly be built into the system, and be implemented as the technology is evolving and negotiated.
7.9 Summing up
Having presented a wide range of actors, groups, arguments, and issues that both overlap and come into conflict with each other, how can the social negotiation around predictive policing be understood in light of the STS literature? For the sake of clarity, I will briefly summarize the analysis thus far. As the clearest example of an undeniably pro-predictive policing group, we find the law enforcement representatives who are eager to bring the technology into wider use. By representing the technology as a matter of resource-allocation and increased policing efficiency, members of this group take the technical achievements of Big Data analytics as sufficient reason to recommend and support its implementation. Furthermore, by addressing the history of law enforcement methods and tools at their disposal, they argue that predictive analytics is simply a natural evolution of the direction that policing has been taking for the last decades. If CompStat and similar Intelligence Led Policing methods have been
measurable successes, the increased efficiency and markedly improved statistics of predictive policing seem to speak for themselves as far as this group is concerned.
51 Morozov (2013) looks at how increasingly automatic bureaucratic processes may lead to improved efficiency
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The technological merits of predictive policing are not as readily accepted by the technology- skeptic group. By pointing out that Big Data has a tendency to black box the analytical process, these critics are concerned about how human error and biases may be hidden behind a shield of objectivity, especially if the technology is widely adopted. Rather than focusing on the budgetary or statistical bottom line, members of this group emphasize that the
technological process behind predictive policing must be scrutinized. If the proponents of the technology are unable to properly address these issues, the skeptics cannot accept the artefact as functional. As a more cautionary supporter of Big Data analytics, Tal Zarsky represents another twist on the technological argument. He acknowledges that the sort of problems that the technology-skeptics are concerned about do exist, but points out that similar problems are already common in traditional policing methods. Zarsky proposes that these problems can be remedied if the technology is made interpretable and transparent, allowing for continuous evaluation of the algorithmic process. If this is implemented into the technology, he argues, predictive policing might actually be a means to reduce systemic injustice. This brings Zarsky’s argument into the realm of ethics, where groups such as the ACLU are arguing against predictive policing on similar grounds. These groups are worried about these injustices being further entrenched by becoming black boxed with the algorithms, in some ways echoing the technology-skeptics’ point about a shield of objectivity.
Putting aside the technological and the ethical implications, Ferguson and Zarsky represent two sides of the negotiation process within a legal context. Ferguson is apprehensive of the technology because, as he sees it, predictive policing might endanger legal principles such as probable cause. If the technology is accepted, then the result may be a crumbling of civil rights and the erosion of the Fourth Amendment. Zarsky takes a different position, acknowledging the problematic aspects, but arguing that the very principles Ferguson are protective of may already be flawed in respect to existing methods. Shifting the issue back to the technology itself, he emphasizes that whereas current methods leave a significant part of police-work at the behest of the individual officer’s discretion, the codified and quantifiable nature of Big Data analytics means that predictive policing could make it easier to identify cases in which civil rights are being infringed upon. As Zarsky sees it, the best way to proceed is to implement predictive policing, but with the caveat that a tailored system of checks and balances is introduced. Both Zarsky and Milakovich point out that with the
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necessary training of individuals, and a sufficiently transparent system, data mining might be the best solution to the problems facing law enforcement (and other governmental
institutions). The issue comes full circle when noting that Zarsky and Milakovich’s solutions are likely to be quite costly, both in manpower and in terms of economic investments. Thus the original point made by the law enforcement proponents, that predictive policing is the best solution to the problems of lacking manpower and resources, might collapse upon itself.