THE ARTIFICIAL INTELLIGENCE AND CRIME PREVENTION
By Rashi Agarwal, Institute of legal Studies,
Shri Ramswaroop Memorial University, Uttar Pradesh.
https://doi.org/10.5281/zenodo.14077135
ABSTRACT
Science fiction has long included "intelligent machines." But today's artificial intelligence (Al) is a reality, and it's affecting our daily lives in very real and significant ways. AI is changing how we live in a number of areas, including phones, automobiles, finances, and healthcare. Artificial Intelligence (AI) has advanced significantly in several fields. The convergence of technology and many other industries has grown in prominence in today's world of perpetual change, and the criminal justice system is one such area where it is becoming more and more popular. Artificial intelligence (AI) has the potential to completely change how crimes are investigated, defendants are assessed, and penalties are decided due to its exceptional capacity to analyse enormous volumes of data and spot patterns. Applications of artificial intelligence (AI) are present in many facets of our existence, including manufacturing, transportation, education, finance, government, industry, and agriculture. AI is even helping the criminal justice system and public safety. For instance, crime forecasts enable more effective use of law enforcement resources, and traffic safety systems detect infractions and enforce traffic regulations. AI is also being used to determine the likelihood that someone under criminal justice supervision will commit another crime.
Keywords- Artificial Intelligence, Crime, Personal Data, Criminal Justice System etc
INTRODUCTION
Artificial Intelligence (AI) is the programming of machines to mimic human thought processes and learning. It includes technologies like computer vision, natural language processing, and machine learning. The criminal justice system is using artificial intelligence (AI) more and more, with applications in law enforcement, courts, and corrections. These technologies enable computers to analyse and interpret data, making them valuable tools in crime prevention, investigation, and decision-making processes. Artificial intelligence (AI) has the potential to become an indispensable component of our criminal justice system, aiding in investigations and enabling criminal justice officials to better uphold public safety.
Applications for Criminal Justice and Public Safety
The capacity for experience-based learning is one aspect of human intelligence. An use of AI called machine learning imitates this capacity and allows computers and their software to gain experience. From the standpoint of criminal justice, pattern recognition is especially crucial. Humans are adept at identifying patterns, and we regularly acquire the ability to distinguish between many things, people, complicated human emotions, facts, and circumstances via experience. Artificial Intelligence aims to duplicate human capabilities in computer hardware and software algorithms. Self-learning algorithms, for instance, use data sets to learn how to recognise faces from photos, carry out challenging robotics and computational tasks, comprehend online purchasing patterns and habits, identify medical conditions from complex radiological scans, and forecast stock market movements. The researcher want to give three instances of how artificial intelligence is being used in criminal justice systems before continuing.
• COMPAS (Correctional Offender Management Profiling for Alternative Sanctions) is a programme that primarily makes predictions about the likelihood of reoffending.
• PredPol, which forecasts potential crime scenes and then determines the most efficient way to deploy police resources; and
• Utilising the HART (Harm Assessment Risk Tool) to determine whether to prosecute, it also forecasts the likelihood of reoffending.
Numerous approaches are being investigated for using AI as a public safety resource. In both the public and private sectors, facial recognition is one particular AI application that is widely used. For example, intelligence analysts frequently use face photos to determine a person's identify and whereabouts. It takes a lot of time and effort to accurately and quickly review the vast amount of potentially relevant photos and videos, and there is a chance that human error will occur from things like exhaustion and other circumstances. In contrast to people, machines never tyre. Through programmes like the Janus computer-vision project funded by the Intelligence Advanced Research Projects Activity, analysts are testing algorithms that can recognise facial traits and differentiate between individuals in a similar way. Additionally, AI is swiftly emerging as a key fraud detection technology.
Online retailers such as PayPal thwart fraudulent attempts by regularly training their fraud detection algorithms to anticipate and identify unusual behaviours as well as pick up on new trends through massive volumes of data.
The following uses of AI in law enforcement:
• Prediction: In order to identify people or places that are at risk of crime, data is analysed using artificial intelligence (AI). Then, by using this information, law enforcement resources can be allocated more skillfully. Improved public safety and a decrease in crime are possible outcomes of this proactive strategy.
• Automatic licence plate readers: These gadgets are able to automatically recognise cars linked to criminal activity by scanning licence plates. This can assist law enforcement in finding suspects and stopping criminal activity.
• With the aid of facial recognition technology, suspects can be recognised in mugshots or surveillance footage. This can speed up the investigation of crimes by law enforcement. Even though facial recognition poses ethical concerns, it can be an effective weapon in the fight against crime when applied properly.
• Crime scene analysis: AI can be used to analyze crime scene data, such as fingerprints and DNA, to help investigators identify suspects and solve crimes.
Artificial Intelligence in the Courts:
• AI decision-making: Judges can utilise AI to assist them in deciding on bail, sentences, and parole. This may contribute to the accuracy and fairness of choices.
• Risk assessment: AI can be used to evaluate an offender's likelihood of recidivism. Decisions regarding parole and sentence can then be made using this data.
• AI can be used to automate case management duties, including filing cases and scheduling hearings. This can allow court employees to concentrate on other duties.
• Legal research: Artificial intelligence (AI) can help lawyers identify pertinent information rapidly by analysing legal documents, statutes, and case precedents. Legal professionals can focus on higher-value work by using these AI-powered tools, which can save hours of manual research and eventually increase the efficiency of the legal system.
AI is being utilised in corrections for:
• Rehabilitation: AI is capable of creating individualised rehabilitation programmes for criminals. By doing this, offenders may be able to address the underlying problems that motivated their illegal actions.
• Risk assessment: AI can be used to determine the likelihood that offenders will use violence or flee. Programming and security decisions can then be made using this knowledge.
• Staffing: AI can be used to match treatment programmes and personnel with suitable offenders. This can guarantee that criminals get the treatment they require.
The use of AI in the criminal justice system is a rapidly evolving field. There are many potential benefits to using AI, but there are also some risks that need to be considered. It is important to carefully evaluate the risks and benefits of AI before deploying it in the criminal justice system.
These are a few advantages of integrating AI into the criminal justice system.
• Enhanced productivity: AI can automate processes presently completed by humans, freeing up resources to concentrate on other important tasks.
• Increased accuracy: AI is capable of faster and more accurate data analysis than humans, which can result in more intelligent decisions.
• Enhanced fairness: Artificial intelligence (AI) can eliminate human bias from decision-making, which may produce more equitable results.
Here are a few dangers associated with applying AI to the criminal justice system:
• Discrimination: Biased AI systems have the potential to treat some groups of people unfairly. It can sustain discrimination in the outcomes of the criminal justice system if it is not properly constructed and controlled. In order to avoid exacerbating already-existing disparities, it is imperative to guarantee that AI systems are trained on objective data and routinely audited for fairness.
• Privacy: AI systems have the potential to gather and retain a lot of personal information, which presents privacy issues. For instance, concerns about potential power abuse and individual privacy are raised by facial recognition technologies. To safeguard people's right to privacy, it is crucial to have precise laws and policies governing the gathering, storing, and use of personal data.
• Accountability: Getting AI systems to answer for their actions might be challenging. It's critical to establish precise protocols for the generation of AI results and to guarantee that human oversight continues to be a part of the decision-making processes.
It's crucial to thoroughly weigh the advantages and disadvantages of AI before integrating it into the criminal justice system. The criminal justice system may become more effective, accurate, and equitable with the use of AI, but only if it is applied responsibly and ethically. The AI research that NIJ supports falls primarily into four areas: public safety video and image analysis, DNA analysis, gunshot detection, and crime forecasting.
ANALYSIS OF PUBLIC SAFETY IMAGES AND VIDEOS
In order to help criminal investigations, the criminal justice and law enforcement communities use video and image analysis to gather information about individuals, items, and behaviours. On the other hand, the labor-intensive nature of video and image processing necessitates a large investment in subject-matter experts. Artificial intelligence (AI) has the potential to become an indispensable component of our criminal justice system, aiding in investigations and enabling criminal justice officials to better uphold public safety.
Artificial intelligence (AI) technologies offer the ability to overcome such human shortcomings and perform expert functions. When it comes to facial recognition or pattern analysis, traditional software algorithms that help people are restricted to preset criteria like eye colour, shape, and distance between eyes. Beyond what humans could think, AI video and picture algorithms not only learn difficult tasks but also independently create and establish sophisticated facial recognition elements and parameters. These algorithms can detect complicated events like accidents and crimes (in progress or after the fact), match faces, and recognise guns and other things.The idea of "scene understanding," or the capacity to create text that explains the relationships between objects (people, locations, and things) in a sequence of photos to provide context, is also being investigated. The wording might say, "Pistol being drawn by a person and discharging into a store window," for instance. Identifying items and behaviours that will facilitate both live surveillance and intervention in the event of a crime being committed as well as post-mortem investigation support is the aim.
DNA EXAMINATION
From a scientific and evidence processing perspective, artificial intelligence (AI) can also help the law enforcement community. This is especially true with regard to forensic DNA testing, which over the past few decades has had an unparalleled effect on the criminal justice system.
When committing a crime, biological material can be spread by coming into touch with people or items and transferring things like blood, saliva, semen, and skin cells. The sensitivity of DNA analysis has increased along with DNA technology, enabling forensic experts to find and handle previously unusable DNA evidence that is low-level, deteriorated, or otherwise unviable. For instance, DNA evidence dating back decades from severe crimes like sexual assaults and cold case homicides is already being sent to labs for examination. Smaller amounts of DNA can be found due to enhanced sensitivity, which opens the door to the possibility of finding DNA from several contributors, even at extremely low levels.
AI could be able to help with this problem. Large volumes of intricate data in electronic format are produced by DNA analysis; these data include patterns, some of which may be too complicated for human study but may become helpful as systems become more sensitive. In order to investigate this field, Syracuse University researchers collaborated with the Onondaga County Centre for Forensic Sciences and the Department of Forensic Biology at the New York City Office of Chief Medical Examiner to look into a unique machine learning-based technique for mixture deconvolution.
DETECTION OF GUNSHOTS
Another application for AI systems is the finding of pattern signatures in gunshot analysis. In one project, the National Institute of Justice (NIJ) provided funding to Cadre Research Labs, LLC to analyse gunshot audio files from smartphones and other smart devices. This was done "because it was observed that the type of firearm and ammunition used, the geometry of the scene, and the recording device used all affect the content and quality of gunshot recordings.”. The Cadre scientists are developing algorithms to identify gunshots, distinguish muzzle blasts from shock waves, calculate shot-to-shot timings, count the number of firearms present, identify individual shots to firearms, and estimate probabilities of class and calibre. All of these tasks could aid law enforcement in their investigations. These algorithms are being developed using a well-defined mathematical model.
FORECASTING CRIME
Large data sets are utilised in the intricate process of predictive analysis to foresee and create possible outcomes. In the field of criminal justice, police, probation officers, and other professionals primarily handle this task; they must acquire experience over many years. The work takes a lot of time and is prone to bias and mistakes.
AI can also assist in identifying possible elderly financial and physical abuse victims. The algorithms have the ability to identify the perpetrator, victim, and contextual elements that differentiate financial exploitation from other types of elder abuse. Additionally, they can distinguish between "hybrid" financial exploitation—in which physical abuse or neglect coexists with financial exploitation—and "pure" financial exploitation, which occurs when the victim of financial exploitation receives no other harm. In order for practitioners to accurately assess the possibility of financial exploitation and take prompt action, the researchers anticipate that these data algorithms can be developed into web-based tools.
DEVELOPMENTS IN THE FIELD OF AI AND CJS IN INDIA
In India, the application of AI in a variety of fields has grown steadily. One well-known example of this is the essay "National Strategy for Artificial Intelligence #AI4ALL" released by NITI Aayog, which aims to raise awareness about the fair use of AI. It demonstrates how artificial intelligence (AI) may be effectively applied in India's five most important industries: transportation, smart cities and infrastructure, healthcare, education, and agriculture.
Through the eCourts Project, the Indian judiciary has already established communication technologies and a basic information infrastructure. Now, it is attempting to take advantage of artificial intelligence's potential. The AI committee of the Supreme Court has begun work.
A tool for neural translation The Supreme Court Vidhik Anuvaad Software (SUVAAS) has been released. It assists in translating judicial documents between English and nine other languages.
A tool for court administration called the Supreme Court Portal for Assistance in Court Efficiency (SUPACE), which is utilised for case monitoring, data mining, legal studies, and other related tasks. In certain areas, this is already planned and being done with the intention of enhancing institutional effectiveness. Therefore, it is clear that the application of AI in the legal industry has started and is expanding quickly.
Case laws:
The Chicago Police Department started utilising PredPol, an AI-powered predictive policing programme, in 2016. The tool determines regions where crimes are most likely to happen by analysing crime statistics. According to the police department, PredPol has assisted them in lowering crime in select Chicago neighbourhoods.
The UK government declared in 2017 that it would utilise artificial intelligence (AI) to assist in identifying and looking into photographs of child sexual assault. Large image databases will be analysed by AI technology to determine which photos are most likely to depict child sexual assault.
The US Department of Justice declared in 2018 that artificial intelligence would be used to assist in determining the likelihood that federal offenders would recidivate.
The Indian Supreme Court ruled in Justice K.S. Puttaswamy (Retd.) and Others v. Union of India and Others (2017) that the right to privacy is guaranteed by the Indian Constitution as a basic right. If it is determined that AI infringes on a person's right to privacy, this ruling may be used to contest the application of AI in the criminal court system.
There have been a few cases involving the employment of AI in the criminal court system in the United States. For instance, the Ninth Circuit Court of Appeals ruled in United States v. Loomis (2018) that the defendant's right to due process was infringed when a pretrial detention decision was made using a risk assessment algorithm.
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.
In addition, artificial intelligence (AI) has the potential to give law enforcement context and situational awareness, which would improve their ability to respond appropriately and safely in potentially hazardous situations.
Drones and robots technology might also be used for public safety monitoring, linked into larger public safety systems, and offer a secure as an alternative to endangering the public and the police. Drones and robotics could also be used for recovery offer insightful information and strengthen criminal specialists in the field of justice in uncontrived ways.
Law enforcement will be better equipped to respond to incidents, prevent threats, stage interventions, redirect resources, and investigate and analyse criminal activity by utilising AI and predictive policing analytics integrated with computer-aided response and live public safety video enterprises. Artificial intelligence (AI) has the potential to become an indispensable component of our criminal justice system, aiding in investigations and enabling criminal justice professionals to better protect public safety.
CONCLUSION
AI technology has the potential to revolutionize the criminal justice system, enhancing crime prevention, investigation, and decision-making processes. From crime prediction and prevention to forensic analysis, facial recognition, and sentencing recommendations, AI can significantly improve the efficiency and fairness of the legal landscape. There is no doubt that AI is becoming part and parcel of our lives. There have already been remarkable advancements in the field of healthcare, finance, security, and transportation by employing Machine learning and AI algorithms. It leads to innovative decision-making and reduces the pendency in courts of law. In addition to this, AI plays a major role in the legal field by assisting lawyers and judges in ensuring fair and transparent investigations. It does not mean that AI and modern technology can replace lawyers and judges as it does not have emotional intelligence. Before AI is readily applied in the Indian legal system, it is imperative to address concerns related to a possible violation of the right to privacy guaranteed by the Indian Constitution. To apply AI, a large chunk of data needs to be fed into the system and at present, there is no legal framework for the collection and protection of data that can be fed into the system for legal and judicial use. Further, on the practical front, legal officers and lawyers will have to be adequately trained before integrating AI into the judicial system. Any legal database will require frequent updates in order to incorporate the latest judicial trends and case laws. Hence, the application of AI to the legal system must be done through evidence and a research-based approach rather than hit and trial method.
However, the responsible implementation of AI in the criminal justice system requires addressing challenges such as bias, privacy concerns, and maintaining accountability. By striking a balance between the benefits and potential risks, we can leverage AI's power to create a more just and efficient criminal justice system.
REFERENCES
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).
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&applicationNumber=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.