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= Predictive Policing = From Wikipedia, the free encyclopedia

Predictive policing refers to the usage of mathematical, predictive analytics, and other analytical techniques in law enforcement to identify potential criminal activity or solve past crimes. This collection and analysis of data aims to statistically predict geospatial areas and individuals at risk of crime in order to develop appropriate police intervention strategies.

Predictive policing methods fall into four general categories: methods for predicting crimes, methods for predicting offenders, methods for predicting perpetrators' identities, and methods for predicting victims of crime.

The technology has been described in the media as a revolutionary innovation capable of "stopping crime before it starts". However, a RAND Corporation report on implementing predictive policing technology describes its role in more modest terms:


 * Predictive policing methods are not a crystal ball: they cannot foretell the future. They can only identify people and locations at increased risk of crime ... the most effective predictive policing approaches are elements of larger proactive strategies that build strong relationships between police departments and their communities to solve crime problems.

Statistical analysis and modelling techniques such as those of predictive policing have been utilized and continuously developing since the start of the computer age. However, in recent years they have been becoming increasingly prominent and popular with predictive policing programs and practices beginning to be implemented across the world.

Methodology
Predictive policing uses data relating to crime history, time variables (i.e. seasons, holidays), opportunity (i.e. population, venues) and precursor events (i.e. escalation) and analyses the patterns and trends in the data in order to predict the level of risk for crime associated with specific times and places. This information provides insight into how to make the best use of resources and prevent future crimes.

Techniques
There are a variety of techniques that predictive policing can utilize which range in complexity and are dependent on the type of data collected.

The Hot Spot Method relates to tracking specific geographical locations or areas with consistently high rates of crime. Predictions are based on historical data and the assumption that the past is a prologue.

The Near-Repeat Method is based on the assumption that criminals return to proximal locations of previous crimes to reoffend. Most commonly seen in burglaries, this method suggests that where a home is burglarized those in the nearby surrounding environment are at an increased risk of being victimized.

The Time-Space Method incorporates variables relating to the environmental and temporal features of crime, in addition to information related to specific instances of crime, in hopes of predicting precise times and locations of future crimes.

Risk-Terrain Analysis generates complex risk profiles by taking socio-economic and infrastructural data and criminological theories into consideration alongside crime-specific data. This method aims to create geospatial risk profiles that identify previously unrecognized locations of high risk.

Applications
Predictive policing has 4 distinct application categories:


 * 1) Predicting crimes applications focus on the forecasting of specific times and places with greater risk.
 * 2) Predicting offenders applications focus on identifying the individuals themselves who are at risk for future offending.
 * 3) Predicting perpetrator identities relates to the creation of general profiles that match likely offenders with previously committed crimes.
 * 4) Predicting victims applications aim to identify groups or victims that are at increased risk of being victimized.

History
Predictive policing technologies were originally developed by the commercial world for commercial use ; which is why it is based on the business model of cyclically collecting and analysing data, making predictions, responding to predictions and recollecting new data.

Whilst this technology has been developing since the 1990s and throughout the computer age, predictive policing as it is known today emerged in 2008 in the United States. Despite large commercial companies developing and selling predictive policing software, police departments across the world are moving towards the development of in-house programs to increase cost efficiency and appropriateness of the programs and algorithms.

As predictive policing practices have spread globally, many countries have developed strategies or established councils to monitor the development and use of predictive policing technologies; for example, the Finish Centre for AI in Finland or the Advisory Council on the Ethical Use of AI and Data in Singapore.

Predictive policing, varies across different countries and appears not to be a one-size-fit-all practice.

United States
PredPol was one of the first major predictive policing companies developed in collaboration between Los Angeles Police Department (LAPD) and the University of California, San Diego. Today the company is used across the United States in 60 police departments such as California, Washington, South Carolina, Arizona, Tennessee, New York and Illinois.

In 2008, Police Chief William Bratton at the LAPD began working with the acting directors of the Bureau of Justice Assistance (BJA) and the National Institute of Justice (NJI) to explore the concept of predictive policing in crime prevention. In 2010, researchers proposed that it was possible to predict certain crimes, much like scientists forecast earthquake aftershocks. ; a proposition that formed the foundations for PredPol.

From 2012, NOPD started a secretive collaboration with Palantir Technologies in the field of predictive policing. According to the words of James Carville, he was impetus of this project and "[n]o one in New Orleans even knows about this".

In 2020 the Fourth Circuit Court of Appeals handed down a decision which found predictive policing to be a law-enforcement tool that amounted to nothing more than reinforcement of a racist status quo. The court also held that to grant the government exigent circumstances exemption in this case would be a broad rebuke to the landmark Terry vs Ohio case which set the standard for unlawful search and seizure. Predictive policing, which is typically applied to so-called 'High crime areas' - "relies on biased input to make biased decisions about where police should focus their proactive efforts", and without it police are still able to fight crime adequately in minority communities.

Europe
Police across Europe are piloting various predictive policing technologies which are becoming well-established; particularly in the Netherlands and Germany.

Germany
Predictive policing practices are used in 6 different federal German states. The prominent program is PreCobs which focusses on predicting where crimes, particularly home burglaries, will occur. This software is also in use in Switzerland.

Berlin has developed its own time-space model that predicts the likelihood of crime occurring; this software is called Skala. The program incorporates police, infrastructural and demographic data to make predictions.

Netherlands
The Netherlands have developed an in-house system known as “crime anticipation system”, or CAS, which was implemented nationally in 2013. The system draws from a variety of sources of data from the previous 3 years including crime data, socio-economic data and location characteristics. CAS can provide predictions in regards to both locations and people at risk for future crime and focusses predominantly on high-impact crimes.

Amsterdam has also developed a time-space model known as Krimpo. Combining police and socio-economic data, this software focuses on predicting where crime is likely to occur.

United Kingdom
Whilst the UK is yet to fully implement predictive policing practices, one program began development in 2009, known as ProMap. In April of 2018, the UK formed the UK Government Office for Artificial Intelligence with the goal of promoting and encouraging the implementation of such systems as predictive policing in line with the appropriate legislation.

Australia
Predictive policing in Australia began as a response to terrorism attacks following those of 9/11. Whilst there is no wide use of predictive software as seen across Europe, many anti-terror laws focus on preventing the crimes before they occur. This is through the targeting of preparatory offences that may suggest future offending.

In 2019, the Australian Human Rights Commission proposed the development of a new body of governance for the area of artificial intelligence to monitor and regulate the deployment of software like predictive policing.

New Zealand
New Zealand has not widely implemented predictive policing however different precursor intelligence tools have been in use for decades. The country is monitoring use across the world to decide how the practice can be implemented within the country and its laws.

India
In 2015, Delhi Police, in partnership with the Indian Space Research Organization, developed a software known as Crime Mapping, Analytics and Predictive System (CMAPS). The program focusses on geographical and environmental profiling and is being used to advise resource allocation. The government is implementing the program across the country.

China
In China, Suzhou Police Bureau has adopted Predictive Policing since 2013. During 2015-2018, several cities in China have adopted predictive policing. China has used Predictive Policing to identify and target people for sent to Xinjiang re-education camps.

Effectiveness
The effectiveness of predictive policing practices are yet to be well-established due to lack of research in the area as well as mixed and incomplete implementation. However, several police studies have shown a decrease in crime rates following the implementation of predictive policing.

The effectiveness of predictive policing was tested by the Los Angeles Police Department (LAPD) in 2010, which found its accuracy to be twice that of its current practices. In Santa Cruz, California, the implementation of predictive policing over a 6-month period resulted in a 19 percent drop in the number of burglaries. In Kent, 8.5 percent of all street crime occurred in locations predicted by PredPol, beating the 5 percent from police analysts.

One study from the Max Planck Institute for Foreign and International Criminal Law in an evaluation of a 3-year pilot of the Precobs (pre crime observation system) software caution that no definite statements can be made about the efficacy of the software. The 3-year pilot project will enter a second phase in 2018.

In Zurich in 2016, following the implementation of predictive policing software, burglaries in particularly were occurring at their lowest rate since 2009. Similarly, in Munich at the same time, burglaries dropped between 17-58% depending on the area.

Additional benefits of predictive policing programs are that they increases the situational awareness of the police force and also enable departments to work more proactively and efficiently.

Proposed Benefits
Proponents of predictive policing outline five potential benefits of predictive policing should it work as it is theoretically described (1).


 * 1) Accuracy – predictive policing software is able to consider a wider variety of variables and discount irrelevant variables.
 * 2) Objectivity – the tools can be scientifically validated.
 * 3) Fairness – systems are impartial and focus only on relevant facts.
 * 4) Efficiency – software speeds up the policing process and allows action to be taken sooner.
 * 5) Transparency – detail about how the systems work can be provided and explained.

In practice, these benefits are sometimes in competition with each other.

Criticisms
The outcomes and predictions of predictive software are only as good as the data upon which they are based. Having both a high volume and high quality of information is crucial; errors in data often lead to errors in predictions. How the information is initially collected and the relevance of the available information are particularly important.

In its current state, predictive policing may be more appropriately applied to certain crimes over others due to data issues. Highly visible or highly-reported crimes can be more accurately predicted than crimes such as domestic violence which are often underreported and therefore not represented accurately in police data.

Similar issues arise in over-policed, vulnerable or discriminated populations that are overrepresented in data. Predictions mirror this data and exacerbate over-policing and create a feedback loop of self-fulfilling prophecies.

A coalition of civil rights groups, including the American Civil Liberties Union and the Electronic Frontier Foundation issued a statement criticizing the tendency of predictive policing to proliferate racial profiling. The ACLU's Ezekiel Edwards forwards the case that such software is more accurate at predicting policing practices than it is in predicting crimes.

In a comparison of methods of predictive policing and their pitfalls Logan Koepke comes to the conclusion that it is not yet the future of policing but 'just the policing status quo, cast in a new name'.

In a testimony made to the NYC Automated Decision Systems Task Force, Janai Nelson, of the NAACP Legal Defense and Educational Fund, urged NYC to ban the use of data derived from discriminatory or biased enforcement policies.

According to an article in the Royal Statistical Society, 'the algorithms were behaving exactly as expected – they reproduced the patterns in the data used to train them' and that 'even the best machine learning algorithms trained on police data will reproduce the patterns and unknown biases in police data'.

Issues in relation to privacy and protection of an individual’s data also arise with predictive policing practices. The accessing and analysing of data is largely unregulated and unmonitored raising questions about how an individual’s data is protected.