Using Artificial Intelligence to Address Criminal Justice Needs (NIJ Journal 280)

USING ARTIFICIAL

INTELLIGENCE TO

ADDRESS CRIMINAL

JUSTICE NEEDS

BY CHRISTOPHER RIGANO

NIJ is committed to realizing the full potential of artificial intelligence to promote public safety and

reduce crime.

“I

ntelligent machines” have long been the subject of science

fiction. However, we now live in an era in which artificial

intelligence (Al) is a reality, and it is having very real and

deep impacts on our daily lives. From phones to cars to

finances and medical care, AI is shifting the way we live.

AI applications can be found in many aspects of

our lives, from agriculture to industry, commun-ications, education,

finance, government, service, manufacturing, medicine, and

transportation. Even public safety and criminal justice are benefiting

from AI. For example, traffic safety systems identify violations and

enforce the rules of the road, and crime forecasts allow for more

efficient allocation of policing resources. AI is also helping to identify

the potential for an individual under criminal justice supervision to

reoffend.

Research supported by NIJ is helping to lead the way in applying AI to address criminal justice needs, such

as identifying individuals and their actions in videos relating to criminal activity or public safety, DNA analysis,

gunshot detection, and crime forecasting.

What Is Artificial Intelligence?

AI is a rapidly advancing field of computer science. In the mid-1950s, John McCarthy, who has been credited

as the father of AI, defined it as “the science and engineering of making intelligent machines” (see sidebar, “A

Brief History of Artificial Intelligence”).

Conceptually, AI is the ability of a machine to perceive and respond to

its environment independently and perform tasks that would typically require human intelligence and decision-

making processes, but without direct human intervention.

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One facet of human intelligence is the ability to learn

from experience. Machine learning is an application of

AI that mimics this ability and enables machines and

their software to learn from experience.

Particularly

important from the criminal justice perspective

is pattern recognition. Humans are efficient at

recognizing patterns and, through experience, we

learn to differentiate objects, people, complex human

emotions, information, and conditions on a daily basis.

AI seeks to replicate this human capability in software

algorithms and computer hardware. For example, self-

learning algorithms use data sets to understand how

to identify people based on their images, complete

intricate computational and robotics tasks, understand

purchasing habits and patterns online, detect medical

conditions from complex radiological scans, and make

stock market predictions.

Applications for Criminal Justice and

Public Safety

AI is being researched as a public safety resource

in numerous ways. One particular AI application —

facial recognition — can be found everywhere in

both the public and the private sectors (see sidebar,

“The National Artificial Intelligence Research and

Development Strategic Plan”).

Intelligence analysts,

for example, often rely on facial images to help

establish an individual’s identity and whereabouts.

Examining the huge volume of possibly relevant

images and videos in an accurate and timely manner

is a time-consuming, painstaking task, with the

potential for human error due to fatigue and other

factors. Unlike humans, machines do not tire. Through

initiatives such as the Intelligence Advanced Research

Projects Activity’s Janus computer-vision project,

analysts are performing trials on the use of algorithms

that can learn how to distinguish one person from

another using facial features in the same manner as a

human analyst.

The U.S. Department of Transportation is also looking

to increase public safety through researching,

developing, and testing automatic traffic accident

detection based on video to help maintain safe and

efficient commuter traffic over various locations and

weather, lighting, and traffic conditions.

AI algorithms

are being used in medicine to interpret radiological

images, which could have important implications

for the criminal justice and medical examiner

communities when establishing cause and manner

of death.

AI algorithms have also been explored in

various disciplines in forensic science, including DNA

analysis.

AI is also quickly becoming an important technology

in fraud detection.

Internet companies like PayPal

stay ahead of fraud attempts by using volumes of data

to continuously train their fraud detection algorithms

to predict and recognize anomalous patterns and to

learn to recognize new patterns.

NIJ’s Artificial Intelligence Research

Portfolio

The AI research that NIJ supports falls primarily

into four areas: public safety video and image

analysis, DNA analysis, gunshot detection, and crime

forecasting.

Public safety video and image analysis

Video and image analysis is used in the criminal

justice and law enforcement communities to obtain

information regarding people, objects, and actions to

support criminal investigations. However, the analysis

of video and image information is very labor-intensive,

requiring a significant investment in personnel with

subject matter expertise. Video and image analysis is

Artificial intelligence has

the potential to be a permanent

part of our criminal justice

ecosystem, providing investigative

assistance and allowing criminal

justice professionals to better

maintain public safety.

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also prone to human error due to the sheer volume of

information, the fast pace of changing technologies

such as smartphones and operating systems, and

a limited number of specialized personnel with the

knowledge to process such information.

AI technologies provide the capacity to overcome such

human errors and to function as experts. Traditional

software algorithms that assist humans are limited

to predetermined features such as eye shape, eye

color, and distance between eyes for facial recognition

or demographics information for pattern analysis. AI

video and image algorithms not only learn complex

tasks but also develop and determine their own

independent complex facial recognition features/

parameters to accomplish these tasks, beyond what

humans may consider. These algorithms have the

potential to match faces, identify weapons and other

objects, and detect complex events such as accidents

and crimes (in progress or after the fact).

In response to the needs of the criminal justice and

law enforcement communities, NIJ has invested

in several areas to improve the speed, quality, and

specificity of data collection, imaging, and analysis

and to improve contextual information.

For instance, to understand the potential benefits of

AI in terms of speed, researchers at the University

of Texas at Dallas, with funding from NIJ and in

partnership with the FBI and the National Institute

of Standards and Technology, are assessing facial

identification by humans and examining methods

for effectively comparing AI algorithms and expert

facial examiners. Preliminary results show that

when the researchers limit the recognition time to

30 seconds, AI-based facial-recognition algorithms

developed in 2017 perform comparably to human

facial examiners.

The implications of these findings

are that AI-based algorithms can potentially be used

as a “second pair of eyes” to increase the accuracy of

expert human facial examiners and to triage data to

increase productivity.

In addition, in response to the need for higher quality

information and the ability to use lower quality

images more effectively, Carnegie Mellon University

is using NIJ funding to develop AI algorithms to

improve detection, recognition, and identification. One

particularly important aspect is the university’s work

on images in which an individual’s face is captured

at different angles or is partially to the side, and

when the individual is looking away from the camera,

obscured by masks or helmets, or blocked by lamp

posts or lighting. The researchers are also working

with low-quality facial image construction, including

images with poor resolution and low ambient light

levels, where the image quality makes facial matching

difficult. NIJ’s test and evaluation center is currently

testing and evaluating these algorithms.

Finally, to decipher a license plate (which could

help identify a suspect or aid in an investigation) or

identify a person in extremely low-quality images or

video, researchers at Dartmouth College are using AI

algorithms that systematically degrade high-quality

images and compare them with low-quality ones to

better recognize lower quality images and video. For

example, clear images of numbers and letters are

slowly degraded to emulate low-quality images. The

degraded images are then expressed and catalogued

as mathematical representations. These degraded

mathematical representations can then be compared

with low-quality license plate images to help identify

the license plate.

Also being explored is the notion of “scene

understanding,” or the ability to develop text that

describes the relationship between objects (people,

places, and things) in a series of images to provide

context. For example, the text may be “Pistol being

drawn by a person and discharging into a store

window.” The goal is to detect objects and activities

that will help identify crimes in progress for live

observation and intervention as well as to support

investigations after the fact.

Scene understanding

over multiple scenes can indicate potentially important

events that law enforcement should view to confirm

and follow. One group of researchers at the University

of Central Florida, in partnership with the Orlando

Police Department, is using NIJ funding to develop

algorithms to identify objects in videos, such as

people, cars, weapons, and buildings, without human

intervention. They are also developing algorithms to

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A Brief History of Artificial Intelligence

1950: Alan Turing publishes his paper on creating thinking machines.

1956: John McCarthy presents his definition of artificial intelligence.

1956-1974: Reason searches or means-to-end algorithms were first developed to “walk” simple

decision paths and make decisions.

Such approaches provided the ability to solve complex mathematical

expressions and process strings of words. The word processing is known as natural language processing.

These approaches led to the ability to formulate logic and rules to interpret and formulate sentences and

also marked the beginning of game theory, which was realized in basic computer games.

1980-1987: Complex systems were developed using logic rules and reasoning algorithms that mimic

human experts. This began the rise of expert systems, such as decision support tools that learned the

“rules” of a specific knowledge domain like those that a physician would follow when performing a

medical diagnosis.

Such systems were capable of complex reasoning but, unlike humans, they could not

learn new rules to evolve and expand their decision-making.

1993-2009: Biologically inspired software known as “neural networks” came on the scene. These

networks mimic the way living things learn how to identify complex patterns and, in doing so,

can complete complex tasks. Character recognition for license plate readers was one of the first

applications.

2010-present: Deep learning and big data are now in the limelight. Affordable graphical processing

units from the gaming industry have enabled neural networks to be trained using big data.

Layering

these networks mimics how humans learn to recognize and categorize simple patterns into complex

patterns. This software is being applied in automated facial and object detection and recognition as well

as medical image diagnostics, financial patterns, and governance regulations.

Projects such as Life Long

Learning Machines, from the Defense Advanced Research Projects Agency, seek to further advance AI

algorithms toward learning continuously in ways similar to those of humans.

Notes

1. Alan Turing, “Computing Machinery and Intelligence,” Mind 49 (1950): 433-460.

2. The Society for the Study of Artificial Intelligence and Simulation of Behaviour, “What is Artificial Intelligence.”

3. Herbert A. Simon, The Sciences of the Artificial (Cambridge, MA: MIT Press, 1981).

4. Daniel Crevier, AI: The Tumultuous Search for Artificial Intelligence (New York: Basic Books, 1993), ISBN 0-465-02997-3.

5. Ibid.

6. Pamela McCorduck, Machines Who Think, 2nd ed. (Natick, MA: A.K. Peters, Ltd., 2004), ISBN 1-56881-205-1, Online

Computer Library Center, Inc.

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identify actions such as traffic accidents and violent

crimes.

Another important aspect of AI is the ability to predict

behavior. In contrast to the imaging and identification

of criminal activity in progress, the University of

Houston has used NIJ funding to develop algorithms

that provide continuous monitoring to assess activity

and predict emergent suspicious and criminal

behavior across a network of cameras. This work also

concentrates on using clothing, skeletal structure,

movement, and direction prediction to identify and

reacquire people of interest across multiple cameras

and images.

DNA analysis

AI can also benefit the law enforcement community

from a scientific and evidence processing standpoint.

This is particularly true in forensic DNA testing, which

has had an unprecedented impact on the criminal

justice system over the past several decades.

Biological material, such as blood, saliva, semen,

and skin cells, can be transferred through contact

with people and objects during the commission of a

crime. As DNA technology has advanced, so has the

sensitivity of DNA analysis, allowing forensic scientists

to detect and process low-level, degraded, or

otherwise unviable DNA evidence that could not have

been used previously. For example, decades-old DNA

evidence from violent crimes such as sexual assaults

and homicide cold cases is now being submitted

to laboratories for analysis. As a result of increased

sensitivity, smaller amounts of DNA can be detected,

which leads to the possibility of detecting DNA

from multiple contributors, even at very low levels.

These and other developments are presenting new

challenges for crime laboratories. For instance, when

using highly sensitive methods on items of evidence,

it may be possible to detect DNA from multiple

perpetrators or from someone not associated with the

crime at all — thus creating the issue of DNA mixture

interpretation and the need to separate and identify (or

“deconvolute”) individual profiles to generate critical

investigative leads for law enforcement.

AI may have the potential to address this challenge.

DNA analysis produces large amounts of complex

data in electronic format; these data contain patterns,

some of which may be beyond the range of human

analysis but may prove useful as systems increase

in sensitivity. To explore this area, researchers at

Syracuse University partnered with the Onondaga

County Center for Forensic Sciences and the New York

City Office of Chief Medical Examiner’s Department

of Forensic Biology to investigate a novel machine

learning-based method of mixture deconvolution.

With an NIJ research award, the Syracuse University

team worked to combine the strengths of approaches

involving human analysts with data mining and AI

algorithms. The team used this hybrid approach

to separate and identify individual DNA profiles

to minimize the potential weaknesses inherent in

using one approach in isolation. Although ongoing

evaluation of the use of AI techniques is needed and

there are many factors that can influence the ability

to parse out individual DNA donors, research shows

that AI technology has the potential to assist in these

complicated analyses.

7. Navdeep Singh Gill, “Artificial Neural Networks, Neural Networks Applications and Algorithms,” Xenonstack, July 21,

2017; Andrew L. Beam, “Deep Learning 101 - Part 1: History and Background” and “Deep Learning 101 - Part 2: Multilayer

Perceptrons,” Machine Learning and Medicine, February 23, 2017; and Andrej Karpathy, “CS231n: Convolutional Neural

Networks for Visual Recognition,” Stanford University Computer Science Class.

8. Beam, “Deep Learning 101 - Part 1” and “Deep Learning 101 - Part 2.”

9. Karpathy, “CS231n.”

10. Defense Advanced Research Projects Agency, “Toward Machines that Improve with Experience,” March 16, 2017.

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The National Artificial Intelligence Research and Development Strategic Plan

On May 3, 2016, the White House announced a series of actions to spur public dialogue on artificial

intelligence (AI), identify challenges and opportunities related to this technology, aid in the use of Al

for more effective government, and prepare for the potential benefits and risks of Al. As part of these

actions, the White House directed the creation of a national strategy for AI research and development.

Following is a summary of the plan’s areas and intent.

Manufacturing

• Increase U.S. manufacturing by using robotics

• Improve worker health and safety

• Improve product quality and reduce costs

• Accelerate production capabilities

• Improve demand forecasting

• Increase flexibility in operations and the

supply chain

• Predict impacts to manufacturing operations

• Improve scheduling of processes and

reduce inventory requirements

Logistics

• Improve supply chains with adaptive

scheduling and routing

• Provide more robust supply chains

Finance

• Allow early detection of risk

• Reduce malicious behavior and fraud

• Increase efficiency and reduce volatility

• Prevent systemic failures

Transportation

• Improve structural health monitoring and

infrastructure management

• Reduce the cost of repair and reconstruction

• Make vehicular travel safer

• Provide real-time route information

• Improve transportation networks and

reduce emissions

Agriculture

• Improve production, processing, and storage

• Improve distribution and consumption of

agricultural products

• Gather data about crops to remove weeds

and pests more efficiently

• Apply treatments (water, fertilizer, etc.)

strategically

• Fill labor gaps

Marketing

• Provide a better match of supply with demand

• Drive up revenue for private-sector

development

• Anticipate consumer needs, and find

products and services

• Reduce costs

Communications

• Maximize efficient bandwidth use

• Automate information storage and retrieval

• Improve filter, search, translation, and

summarization functions

Science and Technology

• Assist in knowledge accumulation

• Refine theories

• Generate hypotheses and perform

experiments using simulations

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Gunshot detection

The discovery of pattern signatures in gunshot

analysis offers another area in which to use AI

algorithms. In one project, NIJ funded Cadre

Research Labs, LLC, to analyze gunshot audio files

from smartphones and smart devices “based on the

observation that the content and quality of gunshot

recordings are influenced by firearm and ammunition

type, the scene geometry, and the recording device

used.”

Using a well-defined mathematical model,

the Cadre scientists are working to develop algorithms

to detect gunshots, differentiate muzzle blasts

from shock waves, determine shot-to-shot timings,

determine the number of firearms present, assign

specific shots to firearms, and estimate probabilities

of class and caliber — all of which could help law

enforcement in investigations.

Crime forecasting

Predictive analysis is a complex process that uses

large volumes of data to forecast and formulate

potential outcomes. In criminal justice, this job rests

mainly with police, probation practitioners, and other

professionals, who must gain expertise over many

years. The work is time-consuming and subject to bias

and error.

With AI, volumes of information on law and legal

precedence, social information, and media can

be used to suggest rulings, identify criminal

enterprises, and predict and reveal people at risk

from criminal enterprises. NIJ-supported researchers

at the University of Pittsburgh are investigating and

designing computational approaches to statutory

interpretation that could potentially increase the speed

Education

• Provide automated tutoring and instruction

• Improve personalized programs and evaluation

• Provide life-long learning and new skills for

the total population

Medicine

• Use bioinformatics to identify genetic risk

from large-scale studies

• Predict safety and efficacy of pharmaceuticals

• Develop new pharmaceutical compounds

• Customize medicine

• Diagnose conditions and recommend treatment

Law

• Analyze case law history

• Assist with discovery process

• Summarize evidence

Personal Services

• Provide natural language systems for an

easier interface and user experience

• Provide automated personal assistants

• Allow group scheduling

Security and Law Enforcement

• Detect patterns and anomalous behavior

• Predict crowd behavior and crime patterns

• Protect critical infrastructure

• Uncover criminal networks

Safety and Prediction

• Predict infrastructure disruptions with

distributed sensor systems and pattern

information

• Adapt operations for minimal impact

Note

1. Networking and Information Technology Research and Development Subcommittee of the National Science and

Technology Council, National Artificial Intelligence Research and Development Strategic Plan, Office of Science and

Technology Policy, October 2016, 8-11.

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and quality of statutory interpretation performed by

judges, attorneys, prosecutors, administrative staff,

and other professionals. The researchers hypothesize

that a computer program can automatically recognize

specific types of statements that play the most

important roles in statutory interpretation. The goal

is to develop a proof-of-concept expert system to

support interpretation and perform it automatically for

cybercrime.

AI is also capable of analyzing large volumes of

criminal justice-related records to predict potential

criminal recidivism. Researchers at the Research

Triangle Institute, in partnership with the Durham

Police Department and the Anne Arundel County

(Maryland) Sheriff’s Office, are working to create an

automated warrant service triage tool for the North

Carolina Statewide Warrant Repository. The NIJ-

supported team is using algorithms to analyze data

sets with more than 340,000 warrant records. The

algorithms form decision trees and perform survival

analysis to determine the time span until the next

occurrence of an event of interest and predict the risk

of reoffending for absconding offenders (if a warrant

goes unserved). This model will help practitioners

triage warrant service when backlogs exist. The

resulting tool will also be geographically referenced so

that practitioners can pursue concentrations of high-

risk absconders — along with others who have active

warrants — to optimize resources.

AI can also help determine potential elder victims of

physical and financial abuse. NIJ-funded researchers

at the University of Texas Health Science Center

at Houston used AI algorithms to analyze elder

victimization. The algorithms can determine the victim,

perpetrator, and environmental factors that distinguish

between financial exploitation and other forms of

elder abuse. They can also differentiate “pure”

financial exploitation (when the victim of financial

exploitation experiences no other abuse) from “hybrid”

financial exploitation (when physical abuse or neglect

accompanies financial exploitation). The researchers

hope that these data algorithms can be transformed

into web-based applications so that practitioners

can reliably determine the likelihood that financial

exploitation is occurring and quickly intervene.

Finally, AI is being used to predict potential victims of

violent crime based on associations and behavior. The

Chicago Police Department and the Illinois Institute

of Technology used algorithms to collect information

and form initial groupings that focus on constructing

social networks and performing analysis to determine

potential high-risk individuals. This NIJ-supported

research has since become a part of the Chicago

Police Department’s Violence Reduction Strategy.

The Future of AI in Criminal Justice

Every day holds the potential for new AI applications in

criminal justice, paving the way for future possibilities

to assist in the criminal justice system and ultimately

improve public safety.

Video analytics for integrated facial recognition,

the detection of individuals in multiple locations via

closed-circuit television or across multiple cameras,

and object and activity detection could prevent crimes

through movement and pattern analysis, recognize

crimes in progress, and help investigators identify

suspects. With technology such as cameras, video,

and social media generating massive volumes of

data, AI could detect crimes that would otherwise go

undetected and help ensure greater public safety by

investigating potential criminal activity, thus increasing

community confidence in law enforcement and the

criminal justice system. AI also has the potential to

assist the nation’s crime laboratories in areas such as

complex DNA mixture analysis.

Pattern analysis of data could be used to disrupt,

degrade, and prosecute crimes and criminal

enterprises. Algorithms could also help prevent victims

and potential offenders from falling into criminal

pursuits and assist criminal justice professionals

in safeguarding the public in ways never before

imagined.

AI technology also has the potential to provide

law enforcement with situational awareness and

context, thus aiding in police well-being due to better

informed responses to possibly dangerous situations.

Technology that includes robotics and drones could

also perform public safety surveillance, be integrated

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into overall public safety systems, and provide a safe

alternative to putting police and the public in harm’s

way. Robotics and drones could also perform recovery,

provide valuable intelligence, and augment criminal

justice professionals in ways not yet contrived.

By using AI and predictive policing analytics integrated

with computer-aided response and live public safety

video enterprises, law enforcement will be better

able to respond to incidents, prevent threats, stage

interventions, divert resources, and investigate and

analyze criminal activity. AI has the potential to be a

permanent part of our criminal justice ecosystem,

providing investigative assistance and allowing

criminal justice professionals to better maintain public

safety.

About the Author

Christopher Rigano is a senior computer scientist in

NIJ’s Office of Science and Technology.

This article discusses the following grants:

• “Design and Implementation of Forensic Facial Identification

Experts Test,” grant number 2015-IJ-CX-K014

• “A Simultaneous Low Resolution and Off-Pose Angle Face

Matching Algorithm as an Investigative Lead Generative Tool

for Law Enforcement,” grant number 2013-IJ-CX-K005

• “Studying the Impact of Video Analytics for Pre, Live and

Post Event Analysis on Outcomes of Criminal Justice,” grant

number 2015-R2-CX-K025

• “Learning Models for Predictive Behavioral Intent and

Activity Analysis in Wide Area Video Surveillance,” grant

number 2009-MU-MU-K004

• “DeGrade It,” grant number 2016-R2-CX-0012

• “A Hybrid Machine Learning Approach for DNA Mixture

Interpretation,” grant number 2014-DN-BX-K029

• “Development of Computational Methods for the Audio

Analysis of Gunshots,” grant number 2016-DN-BX-0183

• “A Recommendation System for Statutory Interpretation in

Cybercrime,” grant number 2016-R2-CX-0010

• “Applying Data Science To Justice Systems: The North

Carolina Statewide Warrant Repository (NCAWARE),” grant

number 2015-IJ-CX-K016

• “Elder Financial Exploitation Victimization,” grant number

2013-IJ-CX-0050

• “Chicago Police Predictive Policing Demonstration and

Evaluation Project,” grant number 2011-IJ-CX-K014

Notes

1. Erik Brynjolfsson and Andrew McAfee, “The Business of

Artificial Intelligence: What It Can — and Cannot — Do for

Your Organization,” Harvard Business Review (August 2017);

and Giosué Lo Bosco and Mattia Antonino Di Gangi, “Deep

Learning Architectures for DNA Sequence Classification,”

Fuzzy Logic and Soft Computing Applications — 2017:

Revised Selected Papers From the 11th International

Workshop, WILF 2016, Naples, Italy, December 19-21,

2016, eds. Alfredo Petrosino, Vincenzo Loia, and Witold

Pedrycz (London: Springer Nature, 2018), 162-171,

doi:10.1007/978-3-319-52962-2.

2. The Society for the Study of Artificial Intelligence and

Simulation of Behaviour, “What is Artificial Intelligence.”

3. Bernard Marr, “What Is the Difference Between Deep

Learning, Machine Learning and AI?” Forbes (December 8,

2016).

4. National Science and Technology Council and the

Networking and Information Technology Research and

Development Subcommittee, The National Artificial

Intelligence Research and Development Strategic Plan,

Washington, DC: Office of Science and Technology Policy,

October 2016, https://www.nitrd.gov/PUBS/national_ai_rd_

strategic_plan.pdf.

5. The Intelligence Advanced Research Projects Activity,

“Janus,” Washington, DC: Office of the Director of

National Intelligence, https://www.iarpa.gov/index.php/

research-programs/janus.

6. Yunlong Zhang and Lori M. Bruce, Mississippi Transportation

Research Center: Automated Accident Detection at

Intersections (Project Number: FHWA/MS-DOT-RD-04-150),

Jackson, MS: Mississippi Department of Transportation

and U.S. Department of Transportation Federal Highway

Administration, March 2004.

7. Rachel Z. Arndt, “Artificial Intelligence Takes on Medical

Imaging,” Transportation Hub (July 8, 2017).

8. Brynjolfsson and McAfee, “The Business of Artificial

Intelligence”; and Lo Bosco and Di Gangi, “Deep Learning

Architecture for DNA Sequence Classification.”

9. Ajit Jaokar, “Artificial Intelligence in Fraud Detection,”

Envision Blog, March 15, 2017.

10.

Eric Knorr, “How PayPal Beats the Bad Guys With Machine

Learning,” Ahead of the Curve, InfoWorld (April 13, 2015).

10 Using Artificial Intelligence to Address Criminal Justice Needs

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11.

“Design and Implementation of Forensic Facial Identification

Experts Test” at the University of Texas at Dallas, NIJ award

number 2015-IJ-CX-K014; and P. Jonathon Phillips, “A

Cross Benchmark Assessment of a Deep Convolutional

Neural Network for Face Recognition,” paper presented at

the 12th IEEE International Conference on Automatic Face &

Gesture Recognition, 2017, 705-710.

12.

“A Simultaneous Low Resolution and Off-Pose Angle Face

Matching Algorithm as an Investigative Lead Generative Tool

for Law Enforcement” at Carnegie Mellon University, NIJ

award number 2013-IJ-CX-K005.

13.

“DeGrade It” at Dartmouth College, NIJ award number

2016-R2-CX-0012.

14.

“Studying the Impact of Video Analytics for Pre, Live and

Post Event Analysis on Outcomes of Criminal Justice”

at the University of Central Florida, NIJ award number

2015-R2-CX-K025.

15.

“Learning Models for Predictive Behavioral Intent and

Activity Analysis in Wide Area Video Surveillance”

at the University of Houston, NIJ award number

2009-MU-MU-K004.

16.

“A Hybrid Machine Learning Approach for DNA Mixture

Interpretation” at Syracuse University, NIJ award number

2014-DN-BX-K029.

17.

“Detailed Information for Award 2016-DN-BX-0183,”

National Institute of Justice, https://external.ojp.usdoj.

gov/selector/awardDetail?awardNumber=2016-

DN-BX-0183&fiscalYear=2016&applicationNum

ber=2016-90227-IL-IJ&programOffice=NIJ&po=NIJ.

18.

“Development of Computational Methods for the Audio

Analysis of Gunshots” at Cadre Research Labs, LLC, NIJ

award number 2016-DN-BX-0183.

19.

See, for example, “Effects of Human Factors on the

Accuracy of Fingerprint Analysis,” National Institute

of Justice, https://nij.gov/topics/forensics/evidence/

impression/Pages/human-factors.aspx.

20.

“A Recommendation System for Statutory Interpretation

in Cybercrime” at the University of Pittsburgh, NIJ award

number 2016-R2-CX-0010.

21.

“Applying Data Science to Justice Systems: The North

Carolina Statewide Warrant Repository (NCAWARE)” at RTI

International, NIJ award number 2015-IJ-CX-K016.

22.

“Exploring Elder Financial Exploitation Victimization” at the

University of Texas Health Science Center at Houston, NIJ

award number 2013-IJ-CX-0050.

23.

“Chicago Police Predictive Policing Demonstration and

Evaluation Project” at the Chicago Police Department

and Illinois Institute of Technology, NIJ award number

2011-IJ-CX-K014.

Image source: Erika Cross, Frenzel, GaudiLab, ESB

Professional, and Jerome Scholler/Shutterstock;

Maxiphoto/iStock

NCJ 252038

Cite this article as: Christopher Rigano, “Using

Artificial Intelligence to Address Criminal Justice

Needs,” NIJ Journal 280, January 2019, https://

www.nij.gov/journals/280/Pages/using-artificial-

intelligence-to-address-criminal-justice-needs.aspx.