Author(s): Pratyasha Mohanty
Paper Details: Volume 3, Issue 1
Citation: IJLSSS 3(1) 36
Page No: 346 – 361
ABSTRACT
Overhauling the existing classical legal landscape, Artificial Intelligence (AI) is now driving the revolution in justice delivery systems. This paper investigates the relationship between AI and law, where it focuses on history, evolution, and prospects of AI in the legal system. The study goes on to trace, from the beginning of AI’s incursion into the legal system, the history of AI technologies and how they have been adopted into various components of the justice system. It also highlights landmarks from simple rule-based systems to complex ones with machine learning and natural language processing technologies. The papers look into the part that AI currently plays in dispensing justice such as the predictive analytics, virtual legal assistants, or judicial decision making through AI. These technologies completely transform case management, legal research, and decision making, delivering higher efficiency and accuracy for all tasks. However, incorporating AI into the law raises several challenges. Ethical issues, bias and transparency, and the risk of reinforcing existing disparities in the justice system are some of the issues under discussion in this paper. Ultimately, this study aims to contribute to the prospects of AI in creating more access to and simplifying legal processes. To this end, this research tries to provide an elaborate account of the journey of AI in the legal space and to contribute to the discussion on the transformative role of AI for dispensing justice and its implications for the future of law.
INTRODUCTION
Artificial intelligence refers to the branch of computer science responsible for the imitation of intelligent behavior in computers. AI performs activities that are usually associated with human intelligence such as vision and speech recognition, decision-making, and language translation. AI includes an array of technologies, including machine learning, natural language processing, robotics, and computer vision, enabling machines to learn from experience with new inputs and perform tasks that simulate human behavior.[1] The judiciary provides the most significant function in all societies, safeguarding those laws, rights, fairness, and impartiality mandated by law that often intersects with legal methods, which usually lack efficiency, resources, and, to some degree, are filled with human biases. The introduction of AI into the whole judicial apparatus stands to be a hallmark quantitative measure towards achieving efficiency and, if not already so, the much-desired consistency and accessibility in legal processes. AI can change justice by performing mundane tasks, analyzing huge amounts of legal data, and creating data-informed insights for decision-making. Different AI applications in legal contexts are predictive policing, legal research, case management, and sentencing.[2]
The legal sector has, historically, been slow and averse towards integrating new technology; however, AI is changing that paradigm. AI serves to read huge input data to recognize patterns and predict which aspects of various legal work it can improve. Presently, the Judiciary is doing many things to incorporate AI into predictive policing, law research tools, and the administration of court cases with robotic tools for administrative tasks, be it scheduling dates or tracking progress.[3] Nevertheless, to apply AI implies ethical obstacles. One major consideration is the potential bias of the AI algorithm that blatantly drives the wrong conclusion if biased data is fed into its training. Transparency and accountability towards suggesting AI systems will act as vital counterweights against this risk. Well-described guidelines and rules should be in place for AI use within the legal system to safeguard responsible and ethical applications. As AI continues to advance, a major transformation of the justice delivery system draws closer, promoting efficiency and balance in the whole legal system.
ORIGIN AND DEVELOPMENT OF AI IN LAW[4]
The integration of technology into the justice system is a long and very promising story. As years pass, technology is adapting to a steady increase designed to make legal processes efficient, accurate, and widely accessible. This history will ground your judgment about the potential of Artificial Intelligence (AI) to transform the delivery of justice.
EARLY YEARS: LEGAL AI PIONEERING (1970S–1980S)[5][6] :-
The dependence of justice systems in society has always been to maintain the law and safeguard individual rights. Likewise, conventional legal processes, whether civil or criminal, were slow, often manual, and therefore susceptible to human error. The advent of technology: typewriters for drafting legal documents in the early 20th century, and electronic records for their storage, altered almost everything, making it possible faster and far more legible. Beginning in the 1970s, when the idea of an expert system emerged with the Stanford Taxman Project, AI began entering the legal domain. These systems were to mimic the decision-making skills of human legal experts in rendering consistent, accurate opinions concerning tax codes. AI was still in its infancy, yet these early systems did provide a basis for later developments in AI and law.
Talking about the 1980s saw the introduction of AI-driven online research tools, most noteworthy LexisNexis and Westlaw, which revolutionized the realm of legal research. Categorized and retrieved pertinent legal documents for law practitioners efficiently, these tools provided quick legal access to case laws, statutes, and regulations. This era marked the advent of digitization of legal information with the application of AI for strengthening the legal research process. The AI saga in law began with rule-based systems, which originated in the 1970s and 1980s. These systems, designated expert systems, were aimed at simulating human expert decision-making and functioned according to specified rules and logical structures to solve specific problems. Despite their limitations, the very first of such systems served as a basis for the later developments of AI in the legal field.
EXPANSION AND SOPHISTICATION (1990S)[7][8] :-
During the late 20th century, computer technology emerged, which caused a major transformation in how the justice system developed. The digitization of legal records through computers improved information management by simplifying storage and retrieval processes. Legal databases such as Westlaw and LexisNexis transformed legal research by offering lawyers and judges immediate access to extensive collections of legal precedents, statutes and case laws. The legal process became more efficient when email and electronic communication emerged because legal professionals, clients, and court officials could communicate faster. Legal software and tools became more advanced because of this technological shift, which improved the justice system’s efficiency.
AI technology advancements during the 1990s resulted in the creation of advanced legal automation tools that included document generation and management as well as billing and case scheduling. The automation tools served to lessen the administrative workload for legal professionals so they could dedicate more time to essential legal tasks. Legal expert systems expanded beyond basic rule-based logic to incorporate case-based reasoning and decision tree analysis. The Legal Problem Solving System (LPSS) used case-based reasoning to help legal professionals with the analysis and resolution of legal issues. Case-based reasoning systems represented a major advancement by using historical legal cases to guide decision-making processes. As early machine learning algorithms gained popularity, they enabled sophisticated data analysis and pattern recognition, which paved the way for additional progress in the legal field. The period witnessed the introduction of case management systems as an important advancement. By combining multiple functions into one platform, case management systems helped law firms and their legal departments streamline operations while increasing efficiency. The popularity of automated document assembly tools rose because they enabled legal professionals to produce complex legal documents with speed and precision.
MODERN ERA: MACHINE LEARNING AND PREDICTIVE ANALYTICS (2000S-PRESENT)[9]:-
NLP, as part of the great technology used in the legal field, has turned the justice dispensation around. AI systems began to understand and interpret human language and even generate human speech, all in the early 2000s. Legal research and document review were never the same. Some of the most notable tools include Westlaw and LexisNexis. Predictive analytics and AI-enabled decision-making started rising in the decade of the 2010s. Their statistical models and machine learning algorithms have been used to predict legal results based on past cases. Such tools can roughly predict what would happen in court: it estimates risk associated with legal options, recommending appropriate decisions based on data-oriented conclusions. These applications also usually boost efficiency by performing most of the routine jobs, thereby saving time used to analyze the law. Typical predictive analytics applications are COMPAS used within criminal justice systems to score likelihood for reoffense, and Lex Machina that delivers predictive insights for intellectual property litigation.
By late 2010s, development of AI-enabled decision-making started gaining momentum where judges and legal practitioners tend to consult the AI before proceeding further to be better informed. Decision assistance, data-recommendation regarding the decision, and a lot of fuss concerning transparency, bias, and the need for human judgment are some key features of AI-built decision-making in the legal domain. It was already in the making within the 2020s that virtual legal assistants or AI-enabled chatbots would come into life to give legal advocacy, case supervision, and facilitate clients’ engagement. DoNotPay is one such tool when it comes to chatbots, with the unique feature of helping users contest parking tickets, while ROSS Intelligence is an AI legal research assistant tool that renders insight and advice.
However, like most emerging technologies, the integration of AI throws up a host of ethical challenges. AI can replicate existing prejudice within the data sets it is trained upon, thereby resulting in inaccurate outcomes even at the best of times. Trust in AI-influenced legal instruments must build through the introduction of additional transparency measures. Ethics is also the question raised as to the accountability of automating legal decision-making vis-a-vis whether AI can do the work of judgment that human beings perform. The future promise of these transformations in law includes automated contract analysis, legal analytics, and AI-assisted mediation. Automated contract analysis software could accomplish reviews automatically, isolate clauses of importance and red flags while at the same time minimizing errors and omissions. Legal analytics go more profound into legal analysis, helping the legal professional make data-based decisions. Transforms certainly all importation of AI into justice delivery; however, it must also be ethical to use such an agent responsibly and fairly. In India, budding lawyers are increasingly amending their legal curriculum to include AI-related ethics into their legal education.
CURRENT APPLICATIONS OF AI IN JUSTICE
The justice system, like many fields, has received radical improvement with the advent of AI. It is believed that these technologies will increase efficiency, accuracy, and accessibility while providing transformational solutions to the problems of yore. Below is an in-depth look into some of the current applications of AI for justice:
PREDICTIVE POLICING[10]–
Predictive policing is one of the pioneering and preeminent applications of AI in the justice system. Predictive policing uses AI algorithms to inspect historical crime data, figure out trends, and predict where crimes most likely will occur. With the help of this, law enforcement agencies can make better use of their resources and even intervene to stave off possible criminal activities.
LEGAL RESEARCH[11]–
AI-based legal research tools have changed the game of research for lawyers and legal professionals. These tools allow incredible speed in analyzing various legal documents, case laws, statutes, and regulations to give the most relevant information and insights. Therefore, they save ample hours for legal professionals to acquire complete and up-to-date information. ROSS Intelligence and CaseText, for instance, use natural language processing to comprehend and respond to complex legal queries and hence offer greater efficiency and accuracy in legal research.
CASE MANAGEMENT[12]
AI is finding multiple applications in case management: for automating administrative tasks, scheduling hearings, and case tracking. This dramatically reduces the workload for the court staff and allows cases to be managed well for timely delivery of justice. AI-powered case management tools rank cases in the order of priority by identifying those needing urgent attention and those needing immediate attention for meeting deadlines. These tools can also aid in the formulation of legal documentation to enhance further the legal process.
SENTENCING AND BAIL DECISIONS[13]–
Judges are supported by AI algorithms in sentencing and bail decisions, with the recommendations258 given being data-based insights. These recommendations are based on considerations such as the defendant’s criminal history, the gravity of the offense, and the risk of reoffending by the defendant to minimize human biases and arrive at fairer judgments. The Public Safety Assessment (PSA) has been used in various jurisdictions across the United States to assist judges in making informed pretrial release decisions by assessing the risk of defendants either failing to appear before the court or committing new offenses.
PREDICTIVE ANALYTICS IN LEGAL DECISION MAKING[14]–
Predictive analytics, a branch of artificial intelligence, can predict the outcome of legal matters and help lawyers and clients make informed decisions. Predictive analytics can estimate the likely results of a case, the estimated duration of the trial, and the estimated expenses with such insight afforded by its ability to sift through historical data and track trends. This information is often critical to the development of legal strategies concerning negotiations to settle the dispute. Law firms are still working to facilitate the application of this tool with a competitive edge against others and to provide better services to their clients.
AI IN ALTERNATIVE DISPUTE RESOLUTION (ADR)[15]–
AI technology finds further application in the domain of alternative dispute resolution (ADR), i.e., mediation and arbitration. AI-enabled platforms for online dispute resolution can analyze the parties’ respective positions, generate solutions, and even predict settlement possibilities. Such platforms can tackle very small disputes, such as consumer complaints, and very complex commercial disputes, to improve access and efficiency in the ADR process.
Artificial Intelligence is now vitally important in the legal sector, thereby bringing innovations that have revolutionized and made conventional practices more efficient and accurate. AI legal tools are used anywhere from legal research and contract review to case management and predictive analytics. Some of the prevailing AI-powered legal tools according to their specialized functions are[16]:-
KIRA SYSTEMS[17] –
Noah Waisberg, an ex-M&A lawyer, is the creator of “Kira Systems”-an AI-based platform performing accurate due diligence contract reviews using advanced algorithms. According to the company, Kira’s software accelerates the process by 40% for novices and 90% for pros by making it easy for them to access information along with the original text to conduct comparisons. Kira’s machine-learning models pre-trained can identify over 1,000 different types of contract clauses, which enable accurate and rapid data extraction. The platform’s flexibility also allows legal teams to customize the system to identify clauses specific to their requirements across a range of industries including finance, real estate, and healthcare. Consequently, by automating document review, Kira Systems ultimately enhances efficiency and reduces error probability.
LEVERTON[18] –
Officially a German AI tool in real estate transaction data extraction, document management, and lease compilation, Leverton compiles contracts written in 20 languages, extrapolates useful information, and exports the information accordingly to a spreadsheet format to ease due diligence. Such a cloud platform is of immense help to companies with global localization. Its multilingual platform automates manual entry and review, allowing businesses to concentrate on more functional strategic decisions. Leverton enables organizations to provide transparency and compliance with applicable regulatory standards by digitizing and structuring data.
EBREVIA[19] –
Ned Gannon and Adam Nguyen founded eBrevia, an AI-powered platform that uses Natural Language Processing and machine learning to extract textual data from legal contracts and other documents. In conjunction with Columbia University, the software can analyze more than 50 documents in less than a minute, thereby increasing the accuracy of manual reviews by 10%. Some essential applications of the eBrevia model are for M&A due diligence, abstraction of leases, and compliance audits. This model is flexible enough to make it possible for the end-users to adapt the program to their requirements for contract analyses. The platform is also designed to be integrated with existing enterprise software so that one can reduce time on contract reviews with increased accuracy through lower human intervention.
JPMORGAN’S COIN[20] –
JPMorgan’s COiN (Contract Intelligence) is an AI-powered platform for analyzing commercial loan agreements with the ability to reduce manual review time and mistakes. The system can review thousands of documents in seconds, allowing JPMorgan to save significant expenses in regulatory compliance processes. COiN can extract 150 attributes from 12,000 commercial credit agreements and contracts in seconds, thus saving lawyers and loan officers approximately 36,000 hours of manual work in a year, which is just unimaginable. This tool is a testimony to the fact that financial institutions can use AI to mitigate risks associated with high-stakes legal documentation.
THOUGHTRIVER[21] –
ThoughtRiver is a platform based on AI concerning risk management. The most critical aspect of risk management is the evaluation of contracts, portfolios, and, finally, investigations. Another critical component of risk management in this case is Fathom Contextual Interpretation Engine developed in collaboration with Cambridge University, which would inject an understanding of clauses contained in a contract and flag a risky contract as one with a conditional risk. The AI in this risk management tool thus pre-screens and assesses risk relative to the clause, highlighting risks, modifications suggested otherwise, and providing redlined versions of the contracts. The utilization of this platform is therefore directed toward in-house legal teams and law firms with very easy integration into existing workflows.
LAWGEEX[22] –
Based on machine learning, text analytics, and legal debate, LawGeex is a contract review and approval platform validating contracts against a predetermined repository of policies. Upon failure to meet the standards, the AI suggests changes and approvals. Reduction in costs of 90% and review times of 80%, according to the firm, is what its software, used by LawGeex accomplishes for customers. Deloitte and Sears are said to be current customers of LawGeex. The platform compares contracts against defined legal policies, finds deviations, and suggests redlines. It automates repetitive tasks, thus improving efficiency and decreasing risks associated with human intervention.
LEGAL ROBOT[23] –
Legal Robot of San Francisco provides a software application called Contract Analytics, which is based on machine learning and AI for the quantitative analysis of legal content. It provides ease of reading complex legal wording, automates the analysis of contracts, and compares the contracts with a set of best practice fairness and compliance standards. This tool is advantageous to people and small businesses that lack standard legal representation, thus democratizing these legal services and giving users the confidence to deal with contracts on their own.
ROSS INTELLIGENCE[24] –
ROSS Intelligence is a legal research platform powered by AI with natural language search capabilities to assist legal practitioners in legal research. It leverages machine learning algorithms to analyze vast amounts of legal data, which in turn helps ROSS to improve its answers over time. BakerHostetler uses ROSS with great success in its bankruptcy department to sift through 27 terabytes of data for accurate replies with references. ROSS performs contextual searches to help legal professionals quickly find relevant precedents. It is widely accepted by law firms and corporate legal departments as a boost to research productivity.
CASETEXT[25] –
Casetext provides a powerful tool called CARA, the best case-text application available today, brings in AI to ensure lawyers can anticipate the arguments of the opposing counsel and identify cases where they have been treated negatively or proved unreliable. It provides AI-powered legal research tools, including CoCounsel, which certainly helps lawyers in brief drafting, contract analysis, and case law research. It employs all the modern technologies to ensure that legal professionals receive accurate and timely information. User-friendly and cheap, it has drawn in both DLA Piper and Ogletree Deakins among its adopters.
LEX MACHINA [26]–
Lex Machina is focused on legal analytics, which provides insight into the outcome of case-related activities, damages, and court tendencies. They analyze historical data that helps lawyers formulate data-driven strategies concerning litigation and evaluation of the case. The platform is well-known within the arena of intellectual property (IP) law but has since spread into employment practices and commercial litigation. The predictive aspect of Lex Machina gives firms an edge in having greater certainty in anticipating outcomes.
DONOTPAY[27] –
DoNotPay has been coined as the first robotic legal attorney in the world. This platform that uses artificial intelligence brings personal matters, such as appealing for a parking ticket, terminating a subscription, or seeking compensation from delayed travel, to an automated interface. The service is cheap and easy to use. By redefining how legal assistance is harnessed, it is relevant to many underserved communities. The product may be less featured or highly advanced compared to those offered to enterprises, but the difference is, these kinds of services are changing in an individual’s path to self-empowerment in legal matters.
BENEFITS OF AI IN JUSTICE[28]
INCREASED EFFICIENCY AND SPEED –
The biggest benefits of Artificial Intelligence in the justice system would be the greater efficiency and increased speed. Wherever the common administrative tasks such as document reviews, case management, or legal research are being performed, with an intended utility of the AI solutions for being automated, the legal professionals would be able to spend this time on other intricate and strategic work. Legal processes are accelerated and relieved of the burden of human resources, hence resulting in shorter case durations and quicker resolution of the legal matters.
CONSISTENCY AND FAIRNESS –
AI can make increases in the legal decision-making system regarding consistency and fairness. In contrast to a human being, an AI system cannot be influenced by emotion, bias, or external pressure. By providing factual data consistently without illusions and also encourages legal decision-making to be result-oriented, as opposed to subjective judgment. By way of example, AI algorithms employed to determine sentence and bail decisions could provide data-driven recommendations, thus reducing the possibility of making hostile or inconsistent outcomes. A greater equity and equality would be achieved in delivering justice.
ACCESS TO JUSTICE –
Artificial Intelligence can make legal services available to the most vulnerable and underserved populations. The usual costs of legal services put an individual out of reach of even the more basic offerings. Using AI such as virtual legal assistants or online dispute resolution platforms will offer lower-cost, more easily accessible avenues for obtaining legal support. Such tools can take users through legal procedures’ steps, answering questions, or providing preliminary advice to aid one’s navigation of the justice system.
IMPROVED LEGAL RESEARCH –
AI-enabled legal research instruments have revolutionized the methods through which lawyers and other legal practitioners use in their research activities. These tools can analyze a massive number of legal documents, case laws, statutes, and regulations within a flash and extract relevant information and insights with accuracy. Employing this natural language processing coupled with machine learning means AI tools read those convoluted lawyering queries and give back very precise results. It not only saves time, but it also guarantees that legal practitioners have current and complete information for improving their legal research.
PREDICTIVE ANALYTICS –
Predictive analytics are some Artificial Intelligence techniques to analyze the outcomes of particular legal cases. Through the analysis of historical data and trends, AI can predict the outcome of a case, the length of the trial, and the overall costs involved. This information is extremely valuable in productively drafting legal strategies and for settlement negotiations. Predictive analytics give legal practitioners evidence of data that improve their decision-making process and help them to work towards favorable resolution outcomes for their clients.
RESOURCE ALLOCATION –
AI also supports the effective allocation of resources within the justice system. Predictive policing, as an example, is the use of artificial intelligence algorithms to process crime data to pinpoint places where crime is likely to take place. This would thereby facilitate the efficient allocation of resources by law enforcement to direct such efforts into areas of high crime rates. AI can significantly improve public safety and resource optimization in law enforcement by helping boost the effectiveness of law enforcement.
CHALLENGES AND CONCERNS[29]
AI ALGORITHMS WITH BIAS –
For justice, the biggest problem is posed by the potential for bias within AI algorithmics. AI algorithms are trained on historical data that may already exist with prejudice and injustice. Failure to identify and mitigate these biases can allow the AI algorithm to propagate or even intensify inequality in the system of justice. For example, predictive policing algorithms faced criticism for disproportionately targeting minority communities that were assessed according to prejudiced crime data. Ensuring the fairness and impartiality of AI systems implies robust testing and assessment mechanisms coupled with transparency and longitudinal observation to determine the presence and counter possibilities of bias.
PRIVACY AND SECURITY –
Within the justice system, AI is thus seen as an invasion of privacy considering the massive amount of sensitive and personal data that AI collects, processes, and analyzes. Such developments threaten to have very serious implications on data privacy and security for these very systems. Any sort of wrongful access, loss of data, or misuse of personal information can seriously impact those parties involved in the ongoing legal proceedings. Given this, privacy and security of legal data are paramount in sustaining public faith in AI-supported justice systems. Measures like stringent data protection, encryption, and well-controlled access are paramount to ensuring the safety of such sensitive data.
TRANSPARENCY AND ACCOUNTABILITY –
Considerable opacity envelops AI algorithms and often presents major difficulties in illuminating the trade on decision-making. This imposition of a barrier against viewing raises questions about accountability for AI systems and their potentiality for justice-averse outcomes. Making sure that AI systems will continue to be incomprehensible and, thus, not trustworthy and undeserving of confidence. Legal practitioners, judges, and members of the general public must be able to track the technical workings of these AI algorithms and their decision-making processes concerning predictive factors. The implementation of techniques of explainable AI (XAI) capable of decrypting and clarifying the operations of AI should enhance accountability within the systems.
ETHICAL CONSIDERATIONS –
The ethical issues surrounding the use of AI in the justice system must be given adequate consideration. They cover matters such as how far AI may go in intervening in a decision-making process, whether or not AI may one day replace human judgment, and whether or not AI is impacting the professions and responsibilities of lawyers. There ought to be an ethical framework for the application of AI in justice; such a framework would compel the application of AI systems to conform with such fundamental legal and human rights principles.
LEGAL AND REGULATORY QUANDARIES –
Rapid development of AI has, in a sense, outstripped the development of legal and regulatory structures for its governance. Such framework would become necessary to ensure that AI systems are implemented responsibly and ethically in the justice system. So, guidelines must be developed concerning data privacy, security, fairness, and accountability. Thereafter, any regulations set up by policymakers and lawyers should allow innovative use of AI for justice while safeguarding individual rights.
HEAVY RELIANCE ON AUTOMATION –
The effect of this increasing reliance on AI and technology has been the dependence of the justice sector on automated systems. While such a tool increases efficiency and accuracy, it is important to remind all that AI does not replace the sound judgment of an individual. Legal professionals must learn how to use AI tools effectively but still maintain the balance between technological assistance and human vigilance while keeping in mind the fact that humans should always be central in decision capability.
CONCLUSION
While AI’s adoption will change the face of justice systems by fast-tracking legal procedures into efficiency, consistency, and resource allocation, the introduction of AI into the justice system requires definitive and concrete considerations as regards challenges and ethical concerns. Areas under discussion include efficiency and speed; AI-assisted justice devices concerning consistency and fairness; access to justice; issues related to predictive policing; and sentencing and bail considerations. Challenges and concerns include bias in AI algorithms; privacy and security; transparency and accountability; ethical questions; and a strong regulatory framework. Whatever AI algorithm is to be deployed within the justice realm must be made to operate fairly and not perpetuate extant inequalities. Any preventive measures to ensure discrimination-free justice tool development must include rigorous testing, transparency in algorithms, and continuous monitoring to find and fix discrimination. Secondly, AI systems’ popularity in justice can be maintained only by ensuring privacy and security of sensitive legal data. Transparent and accountable AI systems are a prerequisite for trust and confidence. The legal professionals, judges, and the public need to understand how AI algorithms function and on what parameters they have based their reasoning and decisions. Setting up ethical guidelines and principles for the application of AI in justice must be done, which ensures that these technologies are implemented responsibly and ethically.
All said and done, on the one hand, the benefits of integrating AI into the justice system become many; however, challenges and ethical issues must be sorted out for the technology to be deployed responsibly and equitably. We should use AI for a transformative potential to make the justice system more efficient, accessible, and fair through better training, explainable AI, ethical standards, regulatory frameworks, collaborations, citizens’ engagements, monitoring of AI systems, and interdisciplinary research.
Some suggestions that would improve AI involvement in justice are as follows:-
- Training and awareness of legal professionals for understanding and effective use of AI tools should be enhanced.
- Establishment of ethical guidelines for the use of AI in justice, addressing bias, fairness, transparency, and accountability would offer some ethical oversight.
- Regulatory frameworks for AI in the justice system are to be developed, which will include acceptable practices for data privacy, security, fairness, and accountability.
- Promote public discourse regarding AI in justice, which should build trust and provide an environment for AI technologies that accord with societal values and expectations.
- Continuous monitoring and evaluation of AI systems are vital to assure their intended purpose and prevention against biases or inequity. Whenever there is an issue, auditing and assessment would point out the expected and unexpected consequences therefrom.
- Encourage interdisciplinary research to come up with innovative solutions for issues faced by AI in justice.
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[2] Artificial intelligence in the context of the Indian legal profession and judicial system.
[3] “Predictive Policing: How AI is Transforming Law Enforcement” by Michael Johnson, 2021.
[4] “Artificial Intelligence and Legal Analytics” by Kevin D. Ashley
[5] Buchanan, B.G., & Headrick, T. E. (1970), Stanford Law Review, 21(1), 40-62.
[6] “Artificial Intelligence and Legal Analytics” by Kevin D. Ashley
[7] Ashley, K.D. (1991),Modeling legal argument.
[8] Samuel, A.L. (1959), IBM Journal of Research and Development, 3(3), 210-229.
[9] Berk, R.A. (2012), Criminal Justice Forecasts of Risk: A Machine Learning Approach. Springer.
[10] Journal of Law and Technology, 12(2), pp. 45-67
[11] “Artificial Intelligence in Legal Research: Benefits and Challenges” by Doe, J. (2022), Legal Technology Review,
[12] “The Role of AI in Case Management” by Johnson, M. (2021). International Journal of Law and Technology
[13] Journal of Criminal Justice
[14] “Predictive Analytics in Legal Decision-Making” by Miller, D.
[15] “Artificial Intelligence in Alternative Dispute Resolution” by White, K. (2021). International Arbitration Review
[16] International Research Journal of Engineering and Technology (IRJET)
[17] Kira Systems
[18] MRI Contract Intelligence (formerly Leverton)
[19] eBrevia –
[20] JPMorgan’s COiN
[21] ThoughtRiver
[22] LawGeex
[23] Legal Robot
[24] ROSS Intelligence
[25] Casetext
[26] Lex Machina
[27] DoNotPay
[28] AI in the Justice System: Benefits and Challenges” by Jane Doe, 2022
[29] AI in the Justice System: Benefits and Challenges” by Jane Doe, 2022