Improving Policing with Natural Language Processing (2021)

Abstract: This article explores the potential for Natural Language Processing (NLP) to enable a more effective, prevention focused and less confrontational policing model that has hitherto been too resource consuming to implement at scale. Problem-Oriented Policing (POP) is a potential replacement, at least in part, for traditional policing which adopts a reactive approach, relying heavily on the criminal justice system. By contrast, POP seeks to prevent crime by manipulating the underlying conditions that allow crimes to be committed. Identifying these underlying conditions requires a detailed understanding of crime events - tacit knowledge that is often held by police officers but which can be challenging to derive from structured police data. One potential source of insight exists in unstructured free text data commonly collected by police for the purposes of investigation or administration. Yet police agencies do not typically have the skills or resources to analyse these data at scale. In this article we argue that NLP offers the potential to unlock these unstructured data and by doing so allow police to implement more POP initiatives. However we caution that using NLP models without adequate knowledge may either allow or perpetuate bias within the data potentially leading to unfavourable outcomes.

## introduction this article will first provide a brief overview of problem-oriented policing (pop) and demonstrate that it is an efficient crime prevention strategy. it will show that by implementing pop processes and reducing criminal opportunities less people are likely to commit crime and end up within the criminal justice system. it will then demonstrate that while pop has previously been successful the analytical burden it places on crime analysts is substantial and is an impediment for wider adoption. subsequently, we will argue that nlp methods have the potential to support efforts to overcome these challenges -enabling at-scale systematic extraction of insights from police free text data sets that can support the pop process. we will conclude by discussing several ethical challenges that must be overcome if nlp is to help deliver positive societal outcomes by supporting those who seek to reduce crime. ## problem-oriented policing pop is a model of policing proposed in 1979 by herman goldstein @xcite as an accompaniment to the traditional policing model. traditional policing focuses resources on reactive response, investigations and arrests. arrests lead to prosecution, court, prison and probation costs and the criminalisation of, mostly, young males. by contrast, pop seeks to re-balance this traditional reactive approach @xcite to include preventative efforts which act before the crime or problem arises @xcite . to this end, pop seeks to prevent problems from reoccurring by analysing how previous similar events occurred then intervening in that generation process to prevent recurrence -see fig 1 for a pictorial representation. in this regard, an essential element for conducting pop is understanding the conditions that allowed crime to occur in the first instance. pop is based upon understanding crime as a socio-physical process that occurs when three separate elements coincide. much like a fire relies on a fuel, a spark and oxygen to occur, crime relies on the convergence of a motivated offender, a suitable target in a setting without a capable guardian @xcite . pop seeks to understand how these elements, known as the crime triangle, coalesce and therefore how the triangle can be disrupted to prevent crime opportunities. pop is generally regarded as a successful police model when implemented correctly. there are systematic reviews that provide evidence for pop's increased effectiveness in crime prevention over the traditional model. a recent systematic review @xcite found that pop was much more effective at preventing crime than traditional policing. a second review @xcite , also found that when targeted alongside another police tactic, hot-spot policing, pop was also more successful than traditional policing. moreover, a number of randomized controlled trials have shown that pop is more effective at preventing crime than traditional policing approaches @xcite . from a social justice perspective pop has the effect of reducing opportunities for crime across communities, and thereby reducing the attractiveness of crime in areas where it is traditionally higher. with reference to the crime triangle, high crime areas may contain similar quantities of potential offenders to low crime areas, but lack capable guardians or security measures, thus creating more viable opportunities for crime. a decreased reliance on the criminal justice system also means less people are criminalised. in what follows we outline the pop process, provide some illustrative examples, identify some key criticisms and challenges associated with its application, and describe how nlp might be used to overcome these and facilitate positive impact. ## police free-text in many countries, including the united kingdom, the police have a legal requirement to record and document crimes. this documentation can vary depending on the severity of the crime and procedures within individual agencies. as can be seen from example texts in @xcite and @xcite police free text includes misspellings and specialised vocabulary like acronyms and contractions. police free text is also generally unedited, capital case rules are liberally applied and often there is little formal grammar. all this sets police free-text apart from the data sets that are generally used to train existing nlp models, suggesting that the nature of the text will require model adaptations to reach similar results to those achieved using the types of data sets existing models are trained on. despite these differences, some preliminary experimental work carried out by the author has shown that existing models give sufficient coverage to the language without adaption. work to understand the utility of part-of-speech taggers showed that using a universal dependency parser based on the english web tree bank foot_1 @xcite an overall token accuracy of 90% was achieved when tested on burglary modus operandi text, although that did mean that around 67% of sentences contained at least one error. a further challenge is the sensitivity of police data. police free-text data can contain personal information and so are often subject to local laws and regulatory frameworks (such as gdpr in the uk and eu). these protections present challenges. police agencies, as we have previously discussed, typically do not have the expertise to conduct the detailed analyses in house and almost certainly do not have access to gpus or other accelerators to build some of the more powerful models from scratch. at the same time, timely sharing of sensitive data in ways that facilitate academic research can present significant logistical challenges. this means that the nlp analytical engines will most likely have to travel to the data located in the police it systems, unless systems can be developed to securely move and store the data. any nlp implementation would, ideally, have low hardware requirements and be packaged so that it can be used by practitioners who may be quantitatively competent, but not be experts in nlp or machine learning tech-niques. in order to overcome these data sharing obstacles we have initially adopted a very low risk approach with a partner agencies to release data for experimental research. this approach is characterised by the following methods: clearly these approaches will have an impact on the data received and therefore the generalisation of nlp applicability to different types of data (e.g. witness statements). however this approach does offer a promising beginning to understand how and if nlp can be useful for pop processes. ## related work machine learning, text mining and data science have, unsurprisingly already been seen as useful tools by crime scientists @xcite . however, as a recent review into the intersection of crime and ai has shown (campedelli, 2020), although some methods of ai and machine learning exist in the criminological literature, there is a general paucity of nlp related research compared to other areas. in this section we concentrate on analyses of free-text police data only. much of the existing crime free-text analysis is dominated either by unsupervised learning and revolves around the problem of crime linkage rather than crime reduction @xcite . crimelinkage seeks to identify crimes that are committed by the same individual(s), whereas pop typically requires crimes grouped according to enabling characteristics. notable examples of unsupervised learning with police free-text data are @xcite and @xcite who use unsupervised natural language processing to understand how crimes may be grouped relative to how they were committed rather than traditional crime classifications. @xcite completes this within a single crime classification and @xcite conducted this across multiple crime classifications. this is referred to as crime topic modelling and seeks to understand crime from an ecological perspective. in addition to the previous studies a pair of recent studies conducted with police data from brazil, @xcite utilise unsupervised nlp techniques to cluster crimes with the hope of understanding what policing strategies will be suited to different areas of the city. the authors cluster crimes, then show police officers a representative sample of the clusters and ask them to nominate a suitable policing style (traditional, pop or hot-spot). they do not report if the styles were subsequently adopted or if they were successful. recently the complexities of models used with crime data has increased and there has been work to extract specific information directly from police free text data, see for example the work by karystianis et al. (2018, 2019 however, information extraction requires significant efforts to build rules and dictionaries, and whilst this approach is undoubtedly more effective than manually trawling through thousands of records it still likely represents an implementation hurdle that is too great for routine adoption. for nlp to aid pop, algorithms need to be de-veloped that can assist with the characterisation of crime events. whether this is with known characteristics, such as presence of alcohol or type of victim-offender relationship, or perhaps unknown characteristics that are discovered through unsupervised learning. the extant research discussed above provides a foundation for further explorations into the utility of nlp, but to the authors' knowledge no current research focuses on characterising crime events for the purposes of aiding crime prevention, more so if one also considers the desire for such solutions to operate without the need of high performance computing. thus, the focus of future research to enable pop should be on examining how existing nlp models can be utilised against police generated free text data, in a low resource environment, with the aim of enhancing the characterisation of crime events. ## nlp applications policing encompasses a diverse set of tasks and responsibilities. it is conceivable that nlp methods could be used to support a broad array of processes associated with these functions. this section will focus on those nlp applications that we believe may offer direct benefit to pop processes, in turn reducing the aforementioned analytical burden associated with their application in real world police settings. ## ethical implications while nlp may offer a range of opportunities to police agencies, utilisation of free-text information from police activities will be subject to similar ethical considerations and biases as other usages of nlp. however, in the case of police usage the a key consideration must be the potential societal impact of biases. there is a real risk that improper or careless uses of nlp may introduce or perpetuate biases that serve to undermine relationships with the communities that the police are there to serve, thus adding to problems rather than solving them. for this reason it is imperative that ethical considerations, particularly around potential biases are considered before implementation and at all stages of the utilisation, by those devising analytical solutions, analysts who apply them, and those officers that formulate the pop responses. here we envisage three main areas where use of nlp may be effected by bias. typically these areas are likely to produce resource allocation biases @xcite . ## pre-trained language models with the recent proliferation and success of large pre-trained natural language models (e.g. bert @xcite ), it is natural to ask whether any of these models can be utilised in the contexts described above. not only have these models proven powerful across a range of nlp tasks and domains @xcite @xcite , but they also reduce some of the pre-processing burden such as feature engineering and embedding generation. for example, hugging face have recently introduced an autonlp 3 service that allows access to high powered nlp models with very little training. while pre-trained language models are good candidates for facilitating pop through nlp, the ethical challenges discussed above remain pertinent. commercial offerings of pre-packaged auto-nlp have the potential to be successful within police agencies, and are likely to offer good general results with a relatively low training burden. however, as suggested above, the richness and completeness of the data and the selection and usage of particular models are all potential sources of bias. to combat against these biases, users of the system must be able to understand the models, or be partnered with an agency that can, so that the models can be leveraged in an appropriate fashion. police will need to delve beneath the surface of potential headline metrics to ensure that the models are not creating new, or perpetuating existing biases. if the police are ill-equipped to do this then it is, in our view, the responsibility of the academic community to investigate these potential problems before systems are used in an operational settings. ## societal implications authors submitting to the nlp for positive impact workshop were challenged to define what they felt positive impact meant to them in the context of their work. positive impact for us would be, firstly, the more wide spread adoption of problem oriented policing. this would see more police agencies devoting more of their time to proactive activities and thus to crime prevention rather than focusing on reactive detection and arrest of offenders. the positive societal impact of this would be less people embroiled in the criminal justice system, as the conditions for crime would not manifest them-3 https://huggingface.co/autonlp selves as often, and so the opportunities to commit crime would be reduced @xcite . these may seem lofty aims for an analytical technique, and perhaps they are, but in this instance nlp would serve as one part of a new approach to understanding crime. nlp can be the key enabler to unlock the latent potential in a policing technique that will allow a shift away from the contentious response-arrest based policing style to a more balanced system. a balanced system that promotes preventing people, often young and disadvantaged, from becoming criminalised. a system that values a problem prevented over an arrest made or a person incarcerated. in this context, a positive impact would see police agencies more aligned with their communities needs and more focused on preventing crime harms before they occur. ## conclusion problem-oriented policing (pop) can be an effective method for reducing crime. empirical evidence suggests it is more effective than the traditional response model in many situations. however, the key requirement of effective pop is an understanding of the crime event, information that is often stored but is too resource intensive to extract from police administrative free text data. here we have argued that nlp has the potential to be applied in a range of ways that could lower the analytical burden of police who seek to take a pop approach, thus enabling it to be adopted more extensively. widespread adoption of pop has the potential to have a positive impact on society. by reducing opportunities for crime, pop is capable of reducing the societal harms that stem from both victimisation and offending. moreover, the preventative approach advocated by pop relies less heavily on traditional arrest-based response method of policing which can create tensions between the police and local communities that they serve alongside producing a range of social and economic costs downstream. nlp is not, however, without its drawbacks, and chief among these are the technical knowledge required to utilise the models and a need to account for potential biases. this all means that the introduction of nlp to police agencies will have to be carefully considered, with biases understood, quantified and addressed in ways that minimise undue harm. generally speaking, police agencies do not have the expertise to do this themselves, and pri-vate providers who might offer such expertise often have a vested interests in protecting their technologies which in turn reduces transparency. as such, it is incumbent on the academic community to investigate how nlp might support such policing efforts and better understand how the aforementioned challenges might be met prior to them manifesting in negative outcomes. if applied correctly and with appropriate safeguards, nlp has the potential to unlock the power of prevention-focused policing techniques, thereby reducing crime and the diverse societal harms associated with its occurrence.