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Using Predictive Policing Cautiously

In the movie, Minority Report (2002), the Precrime unit can arrest criminals before they
commit a crime. They use the vision of three Precogs who can predict the future through dreamsof actual future crimes. The system works, and not a single murder occurs since it is enacted(Spielberg, 2002). Similar ideas called predictive policing are already taken into effect.
Predictive policing relies on algorithms given data from the past to identify potential hotspots
and high-risk persons. In Minority Report (2002), it is later revealed that the Precogs lackconsistent judgment of the future. In addition, the system had been built under great sacrifice(Spielberg, 2002). This should be a lesson that society should learn from — that predictive
policing is not a magical tool that can eliminate crime. This paper will explore the usage ofalgorithm-based prediction in law enforcement in the United States. To avoid the marginalized
population being adversely affected by algorithms learning from inaccurate and misleading data,
predictive policing should be used supplementarily and only when data transparency is ensured.
Definition and Types of Predictive Policing
The idea behind predictive policing is to move from reaction-based to precaution-basedlaw enforcement. The term is used to describe systems that rely on algorithms to predict future
crimes (Sheehey, 2018) in two ways, which Richardson et al. describe as “place-based” and“person-based” (2019). The place-based system focuses on mapping crime hot spots based on
previous arrest records and other information provided by the police (Barrett, 2016). This
includes PredPol, a system that can predict crime risks on areas “as small as 500 by 500 feet”(Taylor, 2020) that was used by the LAPD (Sheehey, 2018). On the other hand, the person-based
system predicts the likelihood of a person to be involved in a crime, both as a perpetrator and as

2a victim (Sheehey, 2018). The most popular form of this is Chicago’s Strategic Subjects List
(SSL) (Sheehey, 2018). According to Sheehey, predictive policing software was already used intwenty-five police departments in the United States as of 2018 (Sheehey, 2018). While both
PredPol and SSL focus on reducing street crime, this is not the only area that it shows potential.
Predictive policing is an attractive system in the area of pretrial release and the prediction ofdomestic violence — both of which require significant improvements to the current systems.
Usage of Predictive Policing
Domestic Violence
According to the London School of Economics and Political Science (2020), the usage ofalgorithm-based risk assessment in domestic violence has intrigued researchers because domestic
violence is often a recurring form of abuse. Thus, the police need to be able to detect victims at
higher risks in the future (London School of Economics and Political Science, 2020). Jeffrey
Grogger, professor of urban policy at the University of Chicago’s Harris School of Public Policy,
Dr. Ivandić, and Tom Kirchmaier, director of the CEP’s policing and crime research group,
found that by using algorithms based on data such as “criminal convictions, incidents of violenceor the number of calls made to the police about domestic abuse”, the negative prediction rate,
which is currently at 11.5% using traditional questionnaires, could be reduced to 6.1% (London
School of Economics and Political Science, 2020). This is not the only way that predictive
policing could help domestic violence victims. Data can also be used to make more informed
decisions about which offenders should be released before the formal hearing (Berk et al., 2016).
As misjudgments regarding pretrial release can have grave consequences that could lead tomurder, the negative prediction rate must be minimized (Berk et al., 2016).
Pretrial Release3
The usage of algorithms in the pretrial context is not limited to cases of domestic
violence. Because the pretrial release is considered a predictive task (Barabas et al., 2018), being
able to use algorithms to predict those who are a higher risk to society or have a greater risk ofescaping would help improve the system in general (Berk et al., 2016). While letting higher-risk
individuals on the loose could lead to a serious threat to society, it is also true that holding people
unnecessarily without proper reason could cause significant consequences to their future (Berk etal., 2016). Currently, the ability to afford cash bail is the primary factor affecting who will be
released or not (Barabas et al., 2018). By using algorithms to perform a more risk-based risk
assessment, the pretrial release could become fairer. Unfortunately, this is not the case, as thedata that the algorithm used is fundamentally flawed. While pretrial risk assessment relies on“historical records of arrest, charges, convictions, and sentences to generate predictions” (Minowet al., 2019), those records are often racially biased and tend to be from the perspective of a“white male population” (Barabas et al., 2018, p.65).
Challenges of Predictive Policing
Racism in Police Records
Despite its popularity in law enforcement, human rights experts including the United
Nations Committee on the Elimination of Racial Discrimination show concern over the usage ofbig data and Artificial Intelligence. They believe that it has the potential to “reinforce alreadyexisting biases and lead to more discriminatory practices” (Cumming-bruce, 2020). This occurs
because police records, which are what the algorithms use to make predictions, are not an
accurate representation of actual criminal activities. The reality is that no more than half ofviolent criminals are arrested (Minow et al., 2019), and arrest records include those who were
wrongly arrested (Minow et al., 2019). This disproportionately affects the black population.

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According to Minow et al. (2019), at age 27, African-Americans are 235% more likely to bearrested for marijuana than. However, usage rates do not significantly differ (Minow et al.,2019). In addition, an analysis conducted by The L.A. Times found that the LAPD pulled over
27% of black drivers in a city where only 9% of the population was black (Poston & Chang,2019). Unfortunately, the lack of correlation between the number of crimes and arrests is not the
only issue that police-owned data has.
Inaccurate Data
Human error and forgery can also jeopardize the accuracy of police data. According toRichardson et al. (2019), over a hundred retired NYPD captains as well as higher-ranking
officers admitted that they have manipulated crime statistics (Richardson et al., 2019). To meetquotas and to give the impression of community control, they would tamper with evidence orfalsify records to increase the number of street arrests (Richardson et al., 2019). On the other
hand, serious crimes were artificially reduced by convincing people not to file reports so thatthey could avoid reporting them to the FBI (Richardson, 2019). The LAPD had also been caught
for misreporting 14,000 serious assaults as minor offenses from 2005 to 2012 (Richardson,2019). This may not have been an issue if these data were corrected before using it for predictive
policing. However, Richardson et al. (2019) have discovered that in Chicago, dirty data wasbeing used for predictive policing.
Racism Enhanced
The concern shared by human rights experts is not just hypothetical. ProPublica analyzed
COMPAS (Correctional Offender Management Profiling for Alternative Sanctions), which is aperson-based predictive policing software that predicts recidivism rates of an individual (Angwin
et al., 2016). After examining 10,000 criminal defendants in Florida, they found that black5
defendants had a 77% greater chance of being marked as high-risk persons who would commit acrime in the future. This was after fixing criminal history and recidivism, age, and gender(Angwin et al., 2016). They also found that COMPAS overestimated the predicted recidivism
rate of black defendants while the white defendants who were labeled as lower risk, had a higher
rate of re-offending in reality (Angwin et al., 2016). Hamilton also found that the system affected
Hispanics in similar ways (2018), indicating that predictive policing gives more power to themajority groups (Barrett, 2016).
Even location-based systems such as PredPol, which intuitively seem less invasive, havebeen subject to criticism. The issue with systems like PredPol is that it creates a “selfperpetuating feedback loop” (Barrett, 2016, p.337). Google’s failure with Google Flu Trends inthe past illustrates what this looks like. Google Flue Trends was an algorithm to predict influenza
cases based on people’s flu-related searches (Lum & Isaac, 2016). After it missed the 2009 flu
pandemic, it constantly over-predicted flu cases between 2011 to 2014 (Lum & Isaac, 2016). It
became apparent later that Google’s new recommendation system was suggesting flu-related
words to healthy people. Because of these recommended searches, people started searching for
flu-related terms even without symptoms. (Lum & Isaac, 2016). Location-based predictive has
the potential to go down the same road. By relying on systems like PredPol, police departments
will begin to station more police in areas where the system predicted a higher crime rate. This
would lead to more arrests, and the algorithm learns from this new information. This is how the
feedback loop works. Even worse, the original criminal data is flawed, and non-white
communities tend to have a higher arrest rate (Taylor, 2020) regardless of the number of criminal
activities. Even location-based systems can be racist. For this reason, Santa Cruz became the first6
to ban the usage of PredPol in 2020 as they believe it targets non-whites disproportionately(Asher-Schapiro, 2020).
How to Approach Predictive Policing
Data Transparency
Given the severe negative impact that the system could have on society, increasedtransparency in data collection methods and usage of personal information is vital. Predictive
policing itself has the potential to improve law enforcement if used properly. The system is
supposed to promote safety. However, without more transparency, the public will not feel safe atall. For example, in the pretrial context discussed earlier, “defendants are typically not entitled toaccess the calculations or input data that were used to calculate the final tabulations” (Barabas etal., 2018). If the information on what the estimation is even based on is not disclosed, thedefendant has no reason to feel satisfied with the pretrial risk assessment results. In addition, in a
municipality in Denmark where algorithms were used to predict children’s risks of abuse, thegovernment faced criticism because the citizens were never asked for their consent (Mchangama& Liu, 2018). Chicago Police’s “heat list” of individuals at high risk to be involved in a crime (asa perpetrator or a victim) was also created without any further warnings. The Chicago Police
Department paid Robert McDaniel, a 22-year-old black man, an unannounced visit telling him
not to commit any further crimes (Lum & Isaac, 2017). However, McDaniel had no priorcriminal record (Lum & Isaac, 2016). Algorithms are already ambiguous by nature (Sheehey,2018). Therefore, consent plays an important role. There should be higher transparency as towhat information is being used and how. This is especially true as it could put those who are
already vulnerable, in a weaker position. Richardson et al. (2019) explain that groups who do are7
not comfortable with the police tend not to report crimes — if this happens consistently, thesystem will continue to make them more vulnerable.
Using Predictive Policing Supplementarily
The issue arises when predictive policing is used as the only tool. In the 1970s, theintroduction of regression modeling, an early form of prediction-oriented policing wasintroduced, incarceration rates skyrocketed as “policies shifted away from rehabilitativeinterventions to more administrative approaches” (Barabas, et al., 2018, p.64). A similar situation
can happen with predictive policing. The emphasis could shift from finding ways to provide
rehabilitation opportunities, to focusing on the numbers and data. There is also automation bias,
where people tend to regard automated decisions as more reliable than decisions that they made(Barrett, 2016), which could especially be alarming when pretrial or court decisions are made.
The algorithms developed for predictive policing could be beneficial if done carefully. It could
benefit society if it served as a tool to understand why it might be the case that certain districts orindividuals are labeled as higher risk it could potentially even help uncover implicit racism
ingrained in society, and help law enforcement move forward.
Conclusion
In Minority Report, the seemingly “perfect” system was built with significant violationsof human rights. Anderton later finds out that the Precogs were children of mothers who wereaddicted to an early version of the neuron. One of the mothers, Anne Lively, wanted to have herdaughter back — but the system only worked if her daughter was there (Spielberg, 2020). Annewas murdered in the end. Without the sacrifice of her life and the dehumanization of the Precogs,
this system would not have existed. The algorithm-based predictive policing emerging today also
has its consequences. Without proper cautioning, it could even change someone’s life entirely.

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One minor criminal record, or even a wrongful arrest, could mean that they have to live as a“high-risk” individual, monitored carefully by the police. Blind reliance on this technology could
be detrimental, but with greater transparency and proper use as a supplementary tool, it has thepotential to improve the current police system.

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