Origins of Predictive PolicingEdit
The first symposium on predictive policing was in Los Angeles in 2009.  Predictive policing seeks to track trends in crime and try to better prevent it. Though predictive policing has come to focus on technological solutions, a major precursor to more modern Artificial Intelligence (AI) is the collection of crime statistics into a database to track crime. According to Charlie Beck, police chief of the LAPD, “The predictive vision moves law enforcement from focusing on what happened to focusing on what will happen and how to effectively deploy resources in front of crime, thereby changing outcomes.” Common data analysis strategies used in predictive policing include data mining, crime mapping, geospatial prediction, and social network analysis. These strategies are used to predict high-crime locations and aid in police decisions. Results of predictive policing are increased surveillance and patrolling of areas deemed to be high risk and increased surveillance of individuals marked at risk of being involved in criminal activity. 
CompStat, a performance management system and the direct precursor to today’s AI predictive policing, was an early software that allowed police to create a database of information, allowing them to make their own predictions of future crime from the accumulated data. By collecting this data, it leads departments to “place a strategic focus on identifying problems and their solutions.” According to Garry McCarthy, Superintendent of the Chicago PD, “CompStat is not a solution. It’s a method to obtain solutions.”  This is a difference between CompStat and the AI systems that followed it as these systems took what CompStat did a step further, using the data to guide police resources and directly providing solutions.
PredPol was one of the earliest AI software to be used in predictive policing and it was created back in 2010 by UCLA scientists working in conjunction with the LAPD.  PredPol works by identifying locations where serious crimes are likely to occur over a period of time, based on the data input of the town or city's past criminal activity. This is then frequently used to direct officers to specified locations for their patrols. PredPol claims to be twice as accurate as human analysts but this has not been confirmed by independent study. 
The American Civil Liberties Union (ACLU) and other civil rights organizations have raised serious concerns about racial biases in the results from this software. These concerns have lead to lawsuits raised against departments employing the use of predictive policing technologies. Independent studies have also found that targets of predictive policing technology tend to align closer to race rather than actual criminalized activity. One such simulation study tracking PredPol targets for policing against actual estimated drug use by race found that drug use by white residents was over triple that of the percent PredPol targeted for policing. 
A large number of major police departments employ predictive policing, including the NYPD, and other police departments have only ended their long running programs in 2020, including the Chicago PD and the LAPD. Some departments feel that by using predictive policing programs, they can more accurately police high crime areas, and say that even if the programs do not directly predict crimes, just knowing that there is a predictive policing program in place will be a deterrent for criminals. Despite multiple complaints of racial biases in predictive policing by advocacy groups and citizens, departments continue to use the programs, with one LAPD spokesman saying "It is math, not magic, and it is not racist."
At this time, no police departments or police affiliated groups have come out against predictive policing.
Companies that Create Predictive Policing SoftwareEdit
Companies that sell predictive policing technology, such as Palantir or PredPol, often market their solutions as being “data driven” and reducing the work of police officers. Palantir aggregates data about individual arrest records or citations in order to target specific people who they consider likely to commit crimes, while PredPol compiles location data in order the identify hot spots where crimes are likely to occur.
Transparency issues tend to plague these programs, and multiple police departments have been sued in efforts to find out how they work. Most of these lawsuits are driven by claims of racial biases within the programs, which police departments deny, although they often refuse to release data to back up their claims. Neither PredPol nor Palantir has directly responded to racial bias complaints, although PredPol has a section on their website where they reiterate that their data is not based on demographics.
The Communities Being PolicedEdit
Community members report feeling targeted and over-policed, with one person saying that asking "how often do I see police in my area is like asking me how many times do I see a bird in the day.” Because predictive policing is based on previous police data, many communities argue that it simply reinforces existing racial biases in policing, creating a "feedback loop" where communities that have historically been over-policed will continue to be over-policed. This is supported by studies which show that information in police databases can be heavily racially biased, since police are more likely to stop people of color rather than white people; this leads to predictive policing programs producing heavily biased outcomes.
Advocacy groups include both large organizations like the ACLU and the Brennan Center, and smaller community based ones like the Stop LAPD Spying Coalition (SLSC). These groups do not support predictive policing, with the ACLU saying that American policing “is systemically biased against communities of color and allows unconscionable abuses of police power,” and that giving them a supposedly neutral algorithm allows them to deny allegations of racism. The ACLU and the Brennan Center have both sued police departments to try and gain information about their predictive policing programs.
With LAPD's predictive policing program being shut down, many members of the SLSC feel hopeful about future fights, with cofounder Hamid Khan saying "Predictive policing has roundly been discredited. This [decision] was clearly [the result of] the organizing that was done. This was clearly the community rising up.”
Case Study: Predictive Policing in LAPD in the 2010sEdit
The Los Angeles Police Department has a long history with racism and abuse (the beating of Rodney King in 1992), and predictive policing software it injected into its structure in the early 2010’s has helped revitalize a push against the organization.
Beginning as a research project in 2009, and first being put into use by the LAPD 2011, the Los Angeles Strategic Extraction and Restoration program is a point automation system used to flag citizens in Los Angeles who may be likely to commit a crime. A target is assigned and accumulates points based on interactions with police officers, non-violent crimes, and parole status. If one collects enough points, they will be added to a list of “Chronic Offenders”. The LAPD will then send officers to monitor that person periodically until they manage to get off the list.
Created as a collaboration between the LAPD and UCLA Anthropology professor Jeffrey Brantingham, PredPol is a crime predication software that analyzes historical records to generate estimated times of crime incidents in a particular area around the city. PredPol is mostly used for motor vehicle related crime, specifically burglary and grand theft auto, and is stated as largely a preventative program, seeking to reduce crime rates by increasing police presents in areas predicted to have a crime happen.
Community and LAPD ResponseEdit
Both of these predictive policing software tools have come under fire from both academics (at UCLA and elsewhere), as well as concerned citizens represented in the SLSC. These groups argue that the presumption of bias-free algorithms is negated by the use of historical crime data as input, and also criticize Professor Brantingham’s role in the collaboration as a violation of anthropology’s ethical obligation to not harm the civilization of study. 
The LAPD also published an internal report on the use of predictive policing for the past 10 years. The study found that both the LASER program and PredPol software were ineffective at producing crime prevention and generating information at a rate any greater than the police officers already patrolling the city. The report notes that the racial makeup of both the hot districts and "Chronic Offenders" match the historical data of the 2000s and 2010s well, but this presumes absolute equity in reported crime and arrests for this period as well. 
Future of Predictive Policing in the LAPDEdit
As it stands now, in response to the 2019 internal study regarding the inefficacy of current predictive policing algorithms, the LAPD phased out the use of the LASER program in 2019, and PredPol in 2020. Note that this was internally and externally communicated as disappointment with the outcomes of the software and a lack of funds due to the Covid-19 pandemic, and not the underlying principles that some groups are concerned with. A major roadblock is the fact that predictive policing data (and sometimes even use) is not publicly available, and the LAPD has not allowed outside research professionals or LA organizations access.
Predictive policing remains a controversial technology but is still being used in departments across the country. Police departments don't want to get rid of it because they see it as a useful tool to help them perform their job, while the people being policed do not trust the latent functions. In the future, some extensions of this research that may be helpful could be the inclusion of research into how other countries have used predictive policing and the issues they have faced. Additionally, as this page was written during the pandemic, it may be helpful to update how police departments change their usage of predictive policing software after the pandemic as many are currently facing pandemic-related budget cuts, causing them to temporarily abandon the technology.
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