Showing posts with label Minority Report. Show all posts
Showing posts with label Minority Report. Show all posts

Artificial Intelligence - Predictive Policing.


Predictive policing is a term that refers to proactive police techniques that are based on software program projections, particularly on high-risk areas and periods.

Since the late 2000s, these tactics have been progressively used in the United States and in a number of other nations throughout the globe.

Predictive policing has sparked heated debates about its legality and effectiveness.

Deterrence work in policing has always depended on some type of prediction.

Furthermore, from its inception in the late 1800s, criminology has included the study of trends in criminal behavior and the prediction of at-risk persons.

As early as the late 1920s, predictions were used in the criminal justice system.

Since the 1970s, an increased focus on geographical components of crime research, particularly spatial and environmental characteristics (such as street lighting and weather), has helped to establish crime mapping as a useful police tool.

Since the 1980s, proactive policing techniques have progressively used "hot-spot policing," which focuses police resources (particularly patrols) in regions where crime is most prevalent.

Predictive policing is sometimes misunderstood to mean that it prevents crime before it happens, as in the science fiction film Minority Report (2002).

Unlike conventional crime analysis approaches, they depend on predictive modeling algorithms powered by software programs that statistically analyze police data and/or apply machine-learning algorithms.

Perry et al. (2013) identified three sorts of projections that they can make: 

(1) locations and times when crime is more likely to occur; 

(2) persons who are more likely to conduct crimes; and 

(3) the names of offenders and victims of crimes.

"Predictive policing," on the other hand, generally relates mainly to the first and second categories of predictions.

Two forms of modeling are available in predictive policing software tools.

The geospatial ones show when and where crimes are likely to occur (in which area or even block), and they lead to the mapping of crime "hot spots." Individual-based modeling is the second form of modeling.

Variables like age, criminal histories, gang involvement, or the chance of a person being engaged in a criminal activity, particularly a violent one, are used in programs that give this sort of modeling.

These forecasts are often made in conjunction with the adoption of proactive police measures (Ridgeway 2013).

Police patrols and restrictions in crime "hot areas" are naturally included in geospatial modeling.

Individuals having a high risk of becoming involved in criminal behavior are placed under observation or reported to the authorities in the case of individual-based modeling.

Since the late 2000s, police agencies have been progressively using software tools from technology businesses that assist them create projections and implement predictive policing methods.

With the deployment of PredPol in 2011, the Santa Cruz Police Department became the first in the United States to employ such a strategy.

This software tool, which was inspired by earthquake aftershock prediction techniques, offers daily (and occasionally hourly) maps of "hot zones." It was first restricted to property offenses, but it was subsequently expanded to encompass violent crimes.

More than sixty police agencies throughout the United States already employ PredPol.

In 2012, the New Orleans Police Department was one of the first to employ Palantir to perform predictive policing.

Since then, many more software programs have been created, including CrimeScan, which analyzes seasonal and weekday trends in addition to crime statistics, and Hunchlab, which employs machine learning techniques and adds weather patterns.

Some police agencies utilize software tools that enable individual-based modeling in addition to geographic modeling.

The Chicago Police Department, for example, has relied on the Strategic Subject List (SSL) since 2013, which is generated by an algorithm that assesses the likelihood of persons being engaged in a shooting as either perpetrators or victims.

Individuals with the highest risk ratings are referred to the police for preventative action.

Predictive policing has been used in countries other than the United States.

PredPol was originally used in the United Kingdom in the early 2010s, and the Crime Anticipation System, which was first utilized in Amsterdam, was made accessible to all Dutch police departments in May 2017.

Several concerns have been raised about the accuracy of predictions produced by software algorithms employed in predictive policing.

Some argue that software systems are more objective than human crime data analyzers and can anticipate where crime will occur more accurately.

Predictive policing, from this viewpoint, may lead to a more efficient allocation of police resources (particularly police patrols) and is cost-effective, especially when software is used instead of paying human crime data analysts.

On the contrary, opponents argue that software program forecasts embed systemic biases since they depend on police data that is itself heavily skewed due to two sorts of faults.

To begin with, crime records appropriately represent law enforcement efforts rather than criminal activity.

Arrests for marijuana possession, for example, provide information on the communities and people targeted by police in their anti-drug efforts.

Second, not all victims report crimes to the police, and not all crimes are documented in the same way.

Sexual crimes, child abuse, and domestic violence, for example, are generally underreported, and U.S. citizens are more likely than non-U.S. citizens to report a crime.

For all of these reasons, some argue that predictions produced by predictive police software algorithms may merely tend to repeat prior policing behaviors, resulting in a feedback loop: In areas where the programs foresee greater criminal activity, policing may be more active, resulting in more arrests.

To put it another way, predictive police software tools may be better at predicting future policing than future criminal activity.

Furthermore, others argue that predictive police forecasts are racially prejudiced, given how historical policing has been far from colorblind.

Furthermore, since race and location of residency in the United States are intimately linked, the use of predictive policing may increase racial prejudices against nonwhite communities.

However, evaluating the effectiveness of predictive policing is difficult since it creates a number of methodological difficulties.

In fact, there is no statistical proof that it has a more beneficial impact on public safety than previous or other police approaches.

Finally, others argue that predictive policing is unsuccessful at decreasing crime since police patrols just dispense with criminal activity.

Predictive policing has sparked several debates.

The constitutionality of predictive policy's implicit preemptive action, for example, has been questioned since the hot-spot policing that commonly comes with it may include stop-and-frisks or unjustified stopping, searching, and questioning of persons.

Predictive policing raises ethical concerns about how it may infringe on civil freedoms, particularly the legal notion of presumption of innocence.

In reality, those on lists like the SSL should be allowed to protest their inclusion.

Furthermore, police agencies' lack of openness about how they use their data has been attacked, as has software firms' lack of transparency surrounding their algorithms and predictive models.

Because of this lack of openness, individuals are oblivious to why they are on lists like the SSL or why their area is often monitored.

Members of civil rights groups are becoming more concerned about the use of predictive policing technologies.

Predictive Policing Today: A Shared Statement of Civil Rights Concerns was published in 2016 by a coalition of seventeen organizations, highlighting the technology's racial biases, lack of transparency, and other serious flaws that lead to injustice, particularly for people of color and nonwhite neighborhoods.

In June 2017, four journalists sued the Chicago Police Department under the Freedom of Details Act, demanding that the department provide all information on the algorithm used to create the SSL.

While police departments are increasingly implementing software programs that predict crime, their use may decline in the future due to their mixed results in terms of public safety.

Two police agencies in the United Kingdom (Kent) and Louisiana (New Orleans) have terminated their contracts with predictive policing software businesses in 2018.

~ Jai Krishna Ponnappan

Find Jai on Twitter | LinkedIn | Instagram

You may also want to read more about Artificial Intelligence here.

See also: 

Clinical Decision Support Systems; Computer-Assisted Diagnosis.

References & Further Reading:

Collins, Francis S., and Harold Varmus. 2015. “A New Initiative on Precision Medicine.” New England Journal of Medicine 372, no. 2 (February 26): 793–95.

Haskins, Julia. 2018. “Wanted: 1 Million People to Help Transform Precision Medicine: All of Us Program Open for Enrollment.” Nation’s Health 48, no. 5 (July 2018): 1–16.

Madara, James L. 2016 “AMA Statement on Precision Medicine Initiative.” February 25, 2016. Chicago, IL: American Medical Association.

Morrison, S. M. 2019. “Precision Medicine.” Lister Hill National Center for Biomedical Communications. U.S. National Library of Medicine. Bethesda, MD: National Institutes of Health, Department of Health and Human Services.

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