PREDICTIVE POLICING IS A MORAL TECHNOLOGY THE CASE OF PREDPOL

Transcription

1 PREDICTIVE POLICING IS A MORAL TECHNOLOGY THE CASE OF PREDPOL

2 THE CASE OF PREDICTIVE POLICING

3 Microsoft Public Safety Motorola Hitachi Data Systems Palentir Law Enforcement Bair Analytics (Nexis Lexis) Information Builders Hunchlab (Azavea) Predpol Crime View (Omega

4 SOCIAL MEDIA DATA AND PREDICTIVE POLICING Predicting crime with Twitter is not operational, it's still in the research stage. Geotagged tweets can be used as «topics» (unsupervised classification with topic modeling), and topics work like «independant variables» in a binary logistic regression model (Gerber, 2014). Geotagged tweets can be used as a proxy of ambient population (Andresen, 2016). Twitter user can be seen as a sensor of offline phenomena : disorderrelated posts on Twitter are associated with actual police crime rates (supervised classification of tweets) (Williams, Burnap and Sloan, 2016) Twitter is a good candidate for «event detection» (riots for instance)

5 STARTING POINT Algorithmic drama (Ziewitz, 2016): inscrutability, opacity, critical dispossession. A specific method (Ingold, 2017): touching algorithms, and knowing them from the inside. Hypothesis: Experts who practice machine learning can have different moral visions of their activity that can be understood by analysing the values and material consequences that participate in the assessment by which predictions are built. Prediction can be seen as an inseparably cognitive, moral and material problem.

6 PREDPOL Slogan: More than hotspot As researchers in Predpol declare that their algorithm is inspired by an algorithm used in seismology, and given that for commercial reasons the company refuses to provide access to its algorithm, a promising alternative is to directly consult the seismologists who developed and use this algorithm

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8 SELF EXCITING POINT PROCESS Background density, (exogeneous fluctuation or hotspot) Risk intensity Kernel Contagion The background rate and kernel parameters are estimated by an expectation-maximization algorithm.

9 MARSAN S COMMENTS (SEISMOLOGIST) These results cast strong doubts on the capacity of the models proposed here to outperform simple hotspot maps obtained by smoothing, for the dataset analyzed. The triggering contribution [branching ratio] to the occurrence of future events is small (it accounts only for 1.7 % for the best model). Accounting for memory in the system therefore can only provide a very modest contribution to the effectiveness of the prediction scheme. More importantly, it is assumed that the dynamics of the process stays the same over time. Possible non-stationarity of the process is thus clearly an issue, as it will prevent the use of past information to predict the future. This is for example experienced in this analysis, as 2015 burglary events are clearly not distributed (in time and in space) as they were in This non stationarity is likely due to uncontroled evolutions in the way these acts are performed, but, in situations were new prediction algorithms are set up and exploited by police patrols, could also be a response by burglars to such a change. Unlike natural processes like earthquakes, analyses like the one presented here could therefore have the ability to modify the observed process, making it more difficult to, correctly predict future events. David Marsan, Published draft paper on Mediapart Journal, April 2015.

10 Predictive policing as a public issue

11 MOHLER S REACTION (PREDPOL) Thanks for your and sending along the analysis. I have found your work on nonparametric point processes quite interesting and influential! We have certainly seen the branching ratio vary quite a lot from city to city and crime type to crime type (from 0 to.5). As you point out, it is important to pick such parameters using cross validation in which case it is certainly possible that a simpler model may be favored. It also may be the case that the nonparametric model you are using is over-parametrized (it looks like it has over 30 parameters), so it may be over-fitting the training data. You might need more regularization, or you might want to use a semi-parametric model (you mention using an exponential smoothing kernel, which is essentially a parametric Hawkes process without the background rate). Another thing you bring up is the non-stationarity of the process. I think this is important and something we tried to estimate in the JASA paper (where the background rate depends on time). Disentangling endogenous contagion from exogenous fluctuations in the intensity is a somewhat open problem, though I have done a little work in this area. The non-stationarity of the background rate is one big difference between crime and earthquakes, and you often try to factor in seasonality and other explicit exogenous predictors. λ x, y, t = v(t) μ (x, y) + σ i,ti<t g(x xi, y yi, t ti, Mi).

12 A SIMPLE VISUALISATION OF MEMORY SEISMES CRIMES Why Mohler is not distabilized by the Marsan s criticism?

13 TAKING THE FOLD OF ALGORITHM Bilel, you have to understand. You're a statistician, you don't know much about the problem you're faced with and Police says: "We pay you, we give you the data, give us the best possible model". You get to work and you realize that your model is doing well for a year and a year later, it is not doing well. You're a stateux, you don't know much about the problem. What are you doing? What are you doing? Marsan waits a minute while he looks me in the eye. As a good sociologist, I say nothing. As a statistician, Mohler says that my model is not flexible enough, I'll make it a little more flexible and I'll add v(t). Well, I'd rather go to the Chicago police officers to find out what happened, what changed. Why is 2015 different than 2014? Is this a counting problem? Have the police changed their habits? Anyway, you're trying to figure out what makes it change from year to year. Maybe Molher is trying to understand, but his attitude makes me think that's not too much of a problem. He is looking to improve the predictive efficiency of his algorithm. But since it doesn't work very well, he's trying to loosen up a bit so that things are better. His model isn't flexible enough, so he says my µ (x, y) I'll loosen it up a little bit to make it go better by adding a temporal variation to the background rate. (David Marsan)

14 A MORAL STATEMENT Ça se trouve ce n est pas la bonne approche. Ça se trouve c est même la contagion qui est différente d une année sur l autre. Il faudrait rechanger les kernels de contagion. Mais c est le plus pénible à ajuster. C est plus simple d ajouter une variable temporelle. C est très basique ce qu il fait. En sismo, on fait des choses beaucoup plus complexe pour faire évoluer le taux de fond en fonction du temps, pour tenir compte de la non stationnarité. L étape essentielle après l article de Predpol serait de comprendre la nonstationnarité. Hors, ils avancent à l aveugle. Moi, je pense que tu ne peux pas traiter tes données sans questionner la réalité qu elle représente. Si tu veux nous, on n est pas mue par le même moteur. Nous ce qui nous intéresse en sismo, ce n est pas de faire de la prédiction, c est de comprendre la forme du Kernel. La contagion nous intéresse car elle nous donne des indices sur les mécanismes qui font qu un tremblement de terre va en enclencher un autre. Elle nous intéresse parce qu elle nous apprend quelque chose sur le processus sismogénique. On ne va pas s imposer une forme a priori car c est la forme qui nous intéresse. Lui il ne s intéresse pas à la forme de la contagion. Il n a pas envie de comprendre comment la contagion va avoir lieu. Il a envie de faire une prédiction. Ça n a rien avoir. Dans notre domaine, on retrouve le même type de chercheur. On a des gens qui font de la prédiction, mais qui n ont pas envie de comprendre le processus. On est beaucoup à penser que ça mène à une impasse.

15 TWO WAYS TO VALUING PREDICTION Marsan assesses the accuracy of the algorithm: it is the ability of the algorithm to reveal a close link between the mathematical model and a coherent conception of the phenomenon that is evaluated. According to Predpol, If the algorithm improves the precision of prediction scores, then the algorithm is good enough.

16 We now need to track the network that is deployed in these two different ways of valuing prediction. Let us recall this basic principle of the sociology of science: phenomena are defined by the response they give to the tests that scientists make them undergo in their laboratory

17 AFTERSHOCKS: TRACES TO UNDERSTAND SEISMICITY In his analyses of the aftershocks, the seismologist doesn't just count them. First of all, it is in these turbulent periods that it is most likely to catch a major earthquake in the grids of its measurement networks. If the recordings are of sufficient quality and number, he will be able to scan the fault break. Even without a large aftershock, he will learn many of the small ones, especially about the directions of the tectonic constraints, which he can deduce from their mechanisms. [ ] as the images of the aftershock became more accurate, their interpretation seemed impossible in detail, which depended on uncontrollable parameters related to the unknowable strength and state of stress of the peripheral faults. "(Bernard p. 108, reading recommended by Marsan.)

18 THE MARSAN S NOMINALISM Even though great progress has been made in the last decade [about declustering algorithm], there are still many open questions, i.e., starting with the physical triggering of earthquakes (aftershocks), effects of uncertainties in the catalog on the results of declustering, or the effect of censored data (selection in time, space and magnitude range) on the outcome. In summary, care should be taken when interpreting results of declustering or results that depend on a declustered catalog, because these results cannot reflect the exact nature of foreshocks, mainshocks and aftershocks; indeed the exact nature of these events may not exist at all! (David Marsan)

19 TWO WAYS TO MOBILIZE REPETITIONS Marsan is interested in replicas because they have the power to help him conceptualize the process of seismicity in a new way. Repetitions are of interest to Predpol researchers because they have the ability to add an additional regularity alignment process to hotspot mapping. Repetition of crimes are mobilized for their ability to capture the largest possible proportion of events.

20 Replicas (aftershock or repeat victimization) are what they do based on what we try to make them do.

21 PRACTICAL CONSEQUENCE AND PREDICTION In Chambéry, classes of measured entities exist in an area where prediction refers to demonstrable consequences, which is not the case in the police area. The moral of this controversy is that the robustness of a prediction is inversely proportional to its practical consequences.

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23 MORAL ANTHROPOLOGY Different styles of moral reasoning are embedded in different kinds of social circumstances, and that forms of moral reasoning only flourish in those social circumstances that are well suited to them. Consequentialist moral reasoning, for example, only works where people have a sense that the social world they inhabit is relatively predictable, such that the probable consequences of an action appear relatively easy to gauge with certainty. Where such conditions do not hold, deontological approaches make much more sense even in situations in which one cannot control the consequences of one s actions, one can control whether or not they conform to a rule or set of rules. Joel Robbins, 2010, On the Pleasures and Dangers of Culpability. Critique of Anthropology.30(1);

24 CONSEQUENTIALIST VS DEONTOLOGICAL APPROACH consequentialist in seismology, as the goal in this case is to measure the practical consequences of an action in the near future. deontological approach in the fight against crime, as the respect for legal principle contained in the algorithm allows the police to concentrate on the immediacy of these acts.

25 THE DIVINARITY PART OF PREDICTIVE MACHINES The attitudes of Pentecostals analyzed by Robbins and the integration of predictive machines into the police organization can be more closely related in this respect than one might think. Some situate the future in the hands of gods, others between those of a machine in which police leaders hope to find salvation. When operating in this style of ethical moral reasoning, the predictive machines of artificial learning are not only made up of technique, science and organization, but also contain a part of divination. For future surveys, an analysis of the modalities of prediction in the world of machine learning and the more occult world of witchcraft or astrology could prove fruitful.