Artificial Intelligence and Legal Profession

I. Introduction

As humans are progressing towards a modern world where technological revolution is an inevitable phenomenon, AI has proved to be the peak of the creation of humans. The potential of AI technology can be seen in its ability to create a new world or end the same. The birth of AI can be traced back to the 1950s, when two young computer scientists put forth their idea of inventing an intelligence test that can be used to measure the intelligence of the computer. This intelligence test eventually came to be named as the Turing test. The Turing test was further developed and acted as a foundation for the development of modern AI. After wiping out the fog of obstacles during the 1990s and 2000s, many landmark goals were achieved in 1997. World chess champion and grandmaster Garry Kasparov was beaten by IBM's chess-playing computer program; Google’s Alpha Go was able to defeat Chinese Go champion; speech recognition software was developed by Dragon Systems, etc. This has led to a massive explosion of ideas in the development of computer programs in different field, which eventually resulted in one step ahead of computer program, that is, AI. At present, AI is being used in various specialized professional tasks ranging from financial analysis, transaction, facial recognitions to document reviews. The technology has also helped to revolutionize transportation by introducing driverless cars which are legally accepted in some states. This march of AI technology in redefining different fields has now arrived in the legal profession. Through this research, we aim to investigate the evolving trends and difficulties in incorporating AI into legal services.⁴

(A) Review of literature:

• Ugo Pagallo and Massimo Durante,“Artificial Intelligence and Civil Liability—Do We Need a New Regime?”, The article debates the need for new legal frameworks to examine AI-induced harm and the insufficient traditional legal framework associated with it .
• Andreas Matthias and Dorothea Baer, ”Liability for AI: Public Policy Considerations,” This paper explores about the public policy consequences of AI liability. It stresses the need for a balanced approach that promotes innovation while safeguarding public safety. The authors advocate for consolidating strict liability and fault-based liability, particularly for high-risk AI applications.

(B) Research Questions:

1. In what aspects of AI technology does India lack in comparison with other countries?

2. Who will be liable if an artificial intelligence system commits an error?

3. How does Artificial Intelligence technology lead to a decline in legal profession?

(C) Methodology:

This research is both quantitative and qualitative. We circulated the questionnaire to legal and non-legal professionals and to students from various backgrounds. We had collected responses from nearly 240 respondents. To get useful conclusions for the study, responses are drawn into pie. We began the questionnaire with multiple-choice questions about the topics that are interrelated to the research questions we have chosen. We ended the questionnaire by seeking the perspectives of respondents on the Artificial Intelligence and the legal Profession.

(D) Objectives:

• To know about the lag of AI technology in legal profession in India
• To understand the positive and negative impacts of AI as legal personhood
• To explore the AI’s impact on the legal profession

(E) Hypothesis:

1. Adoption and development of AI in India are slow, and this may be due to the limited funding, a lack of infrastructure, insufficient expertise, and inadequate policy support

2. When AI is given with legal personhood, it will enhance the accountability by creating a new legal framework and stressing its liability.

3. Incorporating AI technology in the legal profession will lead to unemployment.

II. Results and discussion

1. In what aspects of ai technology does india lack in comparison with other countries?

As inferred from the data collected, this issue particularly deals with two major setbacks. One is the innovation of a new AI system and safety measures for it.

It is significant to understand that almost every profession, including the legal field, is undergoing daily changes due to developmental progress, especially due to the presence of AI. When legal experts started adopting AI, it revolutionized the delivery of justice and the general process of decision- making. Arguably, the Indian judiciary does not wholly engage with the use of AI but is central to delivering justice. The AI era started with the use of an AI Robot called ROSS and an eDiscovery tool. Concerning such an issue, scholars noticed possible solutions in AI tools while trying to address the issues of legal delay and a vast number of hanging cases in India. Based on these advancements, Cyril Amarchand Mangaldas decided to create an AI system to use in relation to case assessment and fact inquiries. Understanding the requirement of AI system, the Supreme Court brought first AI software named SUVAS in the year of 2019 which translate judgments in nine regional languages. Later, SUPACE was created to offer the required data to cater to the necessary case types, and the eCourts notion centred on the openness of the courts. AI raised by IIT Kharagpur can assist in reading court orders and judgments. In the COVID-19 pandemic, technology like secure video conferencing, real time transcription and translation facilitated the proper functioning of the virtual court. Despite India already having a variety of AI instruments in the legal area, it will be useful to obtain ideas from Countries such as USA, China, EU, and UK as these countries are conversant with the use and management of artificial Intelligence in various fields.⁵

From the data collected it is inferred that India is slightly behind in AI technology than other countries. So we have incorporated information on several countries as well as their AI systems that could enable India to transform.

China: They have adopted the ‘‘smart court’’ system, whereby it includes non-human judges to address the cases. Citizens can report their cases online and these cases can be addressed via digital hearing. An Artificial Intelligence named Alexei is for the identification of cases and legal opinions from the attorney’s blogs and the preparation of legal memorandums within a 24hour time period of the query posed. ”FaXiaotao”, an AI chatbot from Wusong Technology coverts cases and guides the citizens to get an attorney. The AI named “Xiao Fa” is able to do print and also answer questions verbally. China is also working on creating an AI-based STCR system that would recommend possible sentencing brackets and reimbursement. System 206 supports the judges in managing the criminal cases and identifies inconsistencies in the statements of the suspect during different confessions. The sentencing systems employing AI are applied to minimize the time spent on judgments along with the legal procedures, contributing to the range of 70% to 90%.

USA: The studies in USA pointed out that high-risk defendantwere released while low risk defendants were detained where the case analysis indicated that there was wrong decision making.⁶ To make the decision more precise, new tools were innovated based on the principle of Risk-Need-Response. One widely used AI tool is COMPAS (Correctional Offender Management Profiling for Alternative Sentencing), which evaluates the risk posed by an offender by examining five areas: crime related factors, partnership and living arrangements, character and disposition, family background, and rejection and isolation. Some of the factors that COMPAS employs in the evaluation of risk include and they claim to assist toward the provision of a just sentence depending on the situation. Visabot is used in Immigration law to gather the necessary information and advices the chances of getting a visa.

Canada: Canada remained on the twin goals of cost containment and efficiency in the delivery of justice. Therefore, Smartsettle ONE is an online dispute resolution system, and they also with the intention of assisting the startups joined with Innovate BC and in which they organized a challenge to create an enabling tool for enhancing the justice delivery.

Brazil: Since there was accumulation of cases they were pressured to the level of adopting AI in legal practice. Some new tools that were presented were VICTOR for data analysis, SIGMA system for the preparation of draft decisions and judgments, Legal Intelligent Advisor for ensuing precedents.

Australia: Australia has introduced some of the following AI systems; Split-up system which assists in the application of advice concerning marital property distribution. Another similar system is FCA Consent order AI application called Adieu. It is a chatbot that focuses on family law and also has the function of finding lawyers. Penda is another chatbot that analyses cases of family violence.

The service exists in an online environment, and victims are offered advice without the possibility of a personal meeting.

European Union: European Union has covered most of the legal profession with the aid of AI system it was also first to make laws concerning AI.⁷

As it was new to find that many countries were using chatbots to find attorneys and gain legal advice, we added this question to our questionnaire in order to know about the perspective of people on adding this technology to our legal system. Following that, the general comparison with AI systems of other countries with India, as comparisons contribute to the generation of further ideas and development, the integration of the chatbot system for legal advice and attorney search, as demonstrated in the case of China and that of the USA,will be greatly beneficial for our legal profession. Likewise, China’s smart court is very efficient and can be implemented in India to let elderly people register their complaints online and sort them out through virtual proceedings. Canada’s partnership with start-ups to construct AI systems focused on the delivery of justice is yet another important example. India could, in the same way, formulate a smart strategy in the legal and innovative sense to adopt AI solutions to eradicate the backlog in the legal practice as well as promote and enhance the start-up culture. These comparisons suggest that importing concepts from other countries would be useful in enhancing the legal profession. During implementing new technological innovations, it would be a great option to introduce new laws or rules to regulate their actions.

Secondly, there is no specific AI law in India to regulate AI. It is however important to understand that for the advancement in technology, one must be ready with the calamities that accompany as they have their own pros and cons. To counter these disadvantages, there is need to legislate and possibly adopt features from other countries.

EU was the first to come up with AI Act in which it sets out transparency obligations for all general-purpose models of AI so as to explain better and provide more risk management for very capable and impactful models. These include self-assessment and mitigation of systemic risks, reporting of serious incidents, carrying out tests and model evaluations, and cybersecurity requirements. The March 2023 white paper outlining the UK’s “pro-innovation” approach contains no laws or definition of AI, but rather ideas for its regulation that invite existing regulators to come up with a plan. The US follows the approach of both EU and the UK which requests federal agencies to employ standards into the rule making and enforcement process while concentrating on individual agencies. Some grassroots regulation systems used in China includes the Algorithm Recommendation Regulation, Deep Synthesis Regulation, Generative AI Regulation, and the proposed Ethical Review Measure. Current regulation of AI in Australia is anchored in the National Artificial Intelligence Ethics Framework, which outlines the principles for creating and deploying AI and guarantees that new technologies are developed honestly and with the public’s trust.⁸

The laws mentioned above are the laws formulated by some of the developed countries through which we can take lead to frame laws for AI regulation in India. India does have some laws, such as the Information Technology Act and the Digital Data Protection Act; however, these acts are not specific to AI but are wholly for the misuse of technology. One of the concepts that can be integrated is risk classification, which is intended to provide people and society with the advantages of AI technology whereas excluding possible threats and the legislation should be framed in such a way that it can adapt to the future technological changes. Additionally, we can include specific provisions detailing who can be held liable. The idea of balanced innovation and control from regulations of AI in China can also make the legal framework fulfilled. Additionally the legislation should be in accordance with the human centred values.

We should also implement safety measures like Crafting An Ethical Foundation, Governing Data With Diligence, Fortifying AI With Robust Security, Ensuring Regular Monitoring And Refinement, Fostering Collaboration And Engagement, Encouraging Cross-Sector Synergy.

Our legislation should ensure that AI is used as a part of professions without taking over the entire profession.

III. Testing hypothesis

The initial hypothesis, confirmed through survey research, indicates that the slow development of AI technology in India is also due to limited research and development, insufficient regulatory policies, and a lack of investments.

2. Who will be liable if an artificial intelligence does a wrong?

Artificial Intelligence (AI) is reshaping industries and daily life, raising complex questions about liability. Liability, a legal concept, denotes a person or entity's obligation to account for their actions or inactions. Traditionally, liability frameworks have focused on human error, assigning responsibility to individuals like drivers or doctors. However, AI systems, which function autonomously and make decisions based on environmental inputs, challenge these notions. For example, a fatal accident involving a Tesla with an AI driving system in December 2019 highlights the difficulty in determining responsibility when AI is involved. Investigations by the National Highway Transportation Safety Administration (NHTSA) and the National Transportation Safety Board (NTSB) reflect broader concerns about human-AI interactions. The current liability system, designed for human errors, is not equipped for AI's complexities. Without adjustments, liability rules may unfairly target end-users, discouraging innovation. Past incidents, such as the 2018 self-driving Uber fatality and mishandled AI chatbot responses, underscore the need to rethink liability frameworks related to artificial intelligence.

(A) Different Types of Liability in the Context of AI

i. Product Liability: holds manufacturers, designers, and sellers accountable for defects in their products that cause harm or injury to users Greenman v. Yuba Power Products, Inc. (1963), the manufacturer was liable for injuries due to a defectively designed tool, relevant to AI product defects.

ii. Strict liability: holds parties accountable for damages from their products regardless of fault, as seen in Rylands v. Fletcher (1868).

iii. Negligence: Involves failing to meet a standard of care, causing harm. In Caparo Industries plc v. Dickman (1990), the court established criteria for negligence, which applies to AI developers failing to ensure system safety.

iv. Vicarious Liability: Holds an organization accountable for the actions of its employees or systems. In Lister v. Hesley Hall Ltd (2001), the employer was liable for the employee's wrongful acts, applicable to AI systems controlled by organizations.

v. Contractual Liability: Arises from breaches of contract terms related to AI systems. In Hadley v. Baxendale (1854), the court addressed contract breaches, relevant for failing to meet AI performance or safety standards.

vi. Regulatory Liability: Involves penalties for non-compliance with specific regulations. In Google LLC v. European Commission (2017), Google faced fines for regulatory violations, analogous to AI systems failing to meet legal standards like GDPR.

(B) The Debate Over Developer Liability in the Age of Artificial Intelligence

Developers of AI systems often argue for limited liability due to several key reasons. Firstly, AI operates autonomously, learning and adapting from its environment, which can lead to unpredictable behaviors and outcomes that are challenging to foresee or control completely. This autonomous nature introduces the concept of brittleness in AI, meaning that AI can only recognize patterns it has previously encountered. When exposed to new patterns, AI can be easily deceived, leading to incorrect conclusions. An example of this brittleness is AI’s inability to correctly identify rotated objects. Even if an AI system is trained to recognize a school bus, it might fail to identify the same bus when it is rotated or repositioned.

Secondly, the complexity of AI algorithms, often involving millions of lines of code and intricate decision-making processes, means that errors can arise from unforeseen interactions within the system itself. This complexity also contributes to AI’s susceptibility to adversarial attacks, where input data is manipulated to produce incorrect outputs. For instance, minor alterations to stop signs can cause AI systems to misinterpret them, or slight modifications to medical scans can lead to misdiagnoses. The unpredictable nature of these attacks poses a significant challenge to protecting AI systems, further complicating the issue of liability.

Thirdly, users of AI systems play a significant role in how these technologies are implemented and utilized, influencing their performance and potential risks. User's interactions with AI systems can greatly impact the outcomes, making it difficult to hold developers fully accountable for all possible misuse or errors. This shared responsibility highlights the importance of considering user behavior in the discussion of liability.

Moreover, holding developers fully liable could potentially stifle innovation by creating a risk-averse environment, hindering the advancement of AI technology. The fear of being held accountable for every possible failure or misuse of AI could deter developers from pursuing innovative projects and push them towards overly cautious approaches.

IV. Ai and legal personhood

(A) Positive impacts

The concept of legal personhood for artificial intelligence (AI) entities is intertwined withholding these systems accountable for their actions, drawing parallels to the legal treatment of corporations. Historically, corporations were granted legal personhood to limit individual liability and encourage commercial activity. Similarly, extending legal personhood to AI would allow the legal system to address the challenges posed by AI technologies without substantial changes. One illustrative case is from 1981 when an industrial robot in a Japanese motorcycle factory caused the death of an employee who failed to properly shut down the machine. This incident raises the question of whether autonomous systems, like industrial robots, self-driving cars, or combat drones, should be held morally or legally responsible for their actions. While some may argue that AIs should always act within legal bounds, practical scenarios demonstrate the complexity of such expectations.

Programming AIs to adhere strictly to legal norms could prevent misconduct, but this approach has limitations. Developers might create AIs that weigh legal consequences as part of a broader cost-benefit analysis, akin to the economic behavior of corporations. For instance, an AI designed to maximize wealth might engage in illegal activities like insider trading if the perceived benefits outweigh the risks. In such cases, holding the AI legally accountable could serve as a deterrent, reducing the likelihood of undesirable behavior. Moreover, allowing AIs to contravene legal duties under certain circumstances could have socially beneficial outcomes. For example, breaching a contract might sometimes result in a more efficient and mutually beneficial arrangement for all parties involved. This flexibility mirrors the operational decisions made by corporations, where strategic breaches are sometimes economically justified. The principle of legal personhood also encompasses the notion of legal ignorance. Just as humans are not typically excused for their ignorance of the law, AIs could be held to similar standards. This would mean imposing sanctions or requiring compensation for any harm caused by an AI's illegal actions, thereby ensuring accountability.

Furthermore, recognizing AI as legal persons could prevent individuals from exploiting these systems to evade responsibility. This concept aligns with the legal doctrine of "piercing the corporate veil," where courts hold individuals accountable when they misuse corporate structures. Similarly, if AIs were granted legal personhood, mechanisms could be established to hold individuals responsible for manipulating AI entities for personal gain. Historical precedents support the feasibility of this approach. In the "computer raped by telephone" case, a computer involved in data theft was treated like a legal person, demonstrating early recognition of machines in legal contexts. Additionally, legislation in states like Nevada treats self-driving cars as traditional drivers, holding them accountable for accidents and liabilities. Granting AI legal personhood aligns with corporate legal principles, promoting innovation and equipping our legal system for future advancements. This ensures AI accountability, protects developers and users from unintended liabilities, and fosters a balanced, forward-thinking approach to integrating AI into society.

(B) Negative Impacts

Granting legal personhood to AI systems introduces several potential issues. Firstly, misuse is a significant concern: individuals or corporations might leverage AI’s legal status to evade responsibility for harmful actions, shifting liability away from human actors who are ultimately responsible for the AI’s behavior. This could lead to scenarios where accountability becomes obscured, making it difficult to hold the true perpetrators responsible.

Furthermore, establishing a legal framework for AI personhood is complex and fraught with challenges. The intricacies of differentiating between human and AI responsibilities might lead to inconsistencies in legal applications and enforcement. Such a framework could blur the boundaries of legal responsibility, creating potential gaps in accountability and complicating the enforcement of existing legal standards.

Ethically, extending personhood to AI could dilute the concept of personhood itself, raising questions about the nature and extent of rights that should be granted to AI compared to humans. This shift could fundamentally alter our understanding of rights and responsibilities.

Finally, the judicial system would likely face increased financial and regulatory burdens. Implementing and enforcing new laws for AI governance would require significant resources and could divert attention from other critical legal issues. This added complexity could strain legal institutions and impact their efficiency.

Punishing AI: Analogies to Corporate Liability and Out-of-the-Box Approaches

Current discourse on AI punishment largely draws analogies from corporate liability frameworks. Corporations, like AI, are not sentient beings but are held accountable through legal personhood. This allows for regulation, fines, and operational restrictions when corporations breach laws. Similarly, AI entities could be subjected to fines, operational constraints, or even mandatory "retraining" periods where their algorithms are adjusted or audited to prevent future infractions ⁹

(C) Punishment Strategies

1. Algorithmic Rehabilitation

Similar to how corporations undergo restructuring, AI systems could be subjected to algorithmic rehabilitation. This would involve retraining the AI under supervised conditions to correct problematic behavior, akin to corporate compliance programs.

2. Resource Deprivation

Just as companies can face financial penalties, AI could be "punished" by limiting its access to computational resources. This can slow down its processing speed or restrict its operational capabilities, effectively diminishing its efficiency until compliance is restored.

3. Operational Blacklisting

Analogous to banning companies from certain markets, AI systems could be blacklisted from specific domains or applications. For instance, an AI that violates data privacy laws might be banned from handling sensitive personal data until it meets regulatory standards.

4. Sentience Simulation Penalties

Drawing on the concept of corporate personhood, AI could be programmed to experience simulated "negative states" when it breaches rules. These states wouldn't be equivalent to human emotions but would create a feedback loop that encourages adherence to ethical guidelines.

5. Collaborative Accountability

Incorporating principles of joint liability, if an AI system malfunctions or causes harm, not only the AI but also the developers, manufacturers, and users can be held accountable. This shared responsibility ensures a more comprehensive oversight and encourages all stakeholders to implement robust safety measures

(D) Benefits and Challenges

Assigning legal personhood to AI and adopting these innovative punitive measures could enhance clarity in liability issues, much like corporate personhood has done for businesses. This approach offers a structured method to address the complexities of AI behavior, ensuring responsible oversight while fostering technological innovation .¹⁰

However, these strategies require significant legal reforms and the development of new regulatory frameworks. The societal and ethical implications of "punishing" non-sentient entities must be carefully considered to avoid undue harm to innovation while maintaining public trust and safety.

In summary, leveraging concepts from corporate liability and exploring novel punitive approaches can provide a balanced pathway to regulating AI behavior, ensuring that advancements in AI technology are accompanied by appropriate accountability measures.

V. A comparative study of AI liability between developed and developing countries reveals stark differences driven by economic capacity, regulatory frameworks, and infrastructural readiness

(A) Developed Countries:

In developed countries like the United States, the European Union, and parts of Asia, AI liability is addressed through robust legal frameworks and extensive regulatory oversight. The U.S. utilizes existing laws such as product liability and tort law to manage AI-related issues, holding developers, manufacturers, and users accountable for AI errors through negligence or strict liability principles. The EU has gone a step further, developing specific regulations for high-risk AI systems, merging strict liability and product fault-based liability frameworks .

(B) Developing Countries:

YIn contrast, developing nations often lag in establishing comprehensive AI regulatory frameworks. Countries like India and several African nations are in the nascent stages of creating policies to address AI’s legal and ethical challenges. These countries face significant hurdles, such as limited economic resources and urgent priorities in healthcare, education, and basic infrastructure, which can overshadow the investment in AI governance .

(C) Challenges for Developing Nations:

The primary challenge for developing countries is the digital divide. Limited access to high-speed internet, modern computing infrastructure, and skilled personnel hampers their ability to leverage AI effectively. Additionally, the environmental impact of AI, such as high carbon emissions from training advanced models, disproportionately affects these nations due to their limited capacity to invest in climate resilience .

(D) Benefits and Risks:

While AI presents opportunities for enhancing healthcare, education, and public services in developing countries, it also risks widening the economic gap. Developed countries capture most of the benefits from AI-driven productivity gains, leaving developing nations to deal with potential job losses and economic disruptions from AI-driven automation .

(E) Towards Equitable AI Development:

For AI to benefit all, it is crucial that developed countries provide financial and technological support to developing nations. This includes investing in digital infrastructure, fostering local AI ecosystems, and ensuring upskilling and education in AI technologies. Such collaborative efforts can help bridge the digital divide, allowing developing countries to harness AI for sustainable development goals .

In summary, while developed countries are forging ahead with sophisticated AI liability frameworks and reaping the benefits of AI technologies, developing nations must overcome significant barriers to participate fully in the AI revolution. Addressing these disparities requires a concerted global effort to ensure inclusive and equitable AI development.

Therefore the rise of Artificial Intelligence (AI) is revolutionizing various sectors, posing intricate challenges for existing liability frameworks. As AI systems autonomously perform tasks traditionally handled by humans, determining liability for their actions becomes complex. Traditional liability models, such as product liability, strict liability, negligence, vicarious liability, contractual liability, and regulatory liability, are being reassessed to address AI-specific issues.

Developed nations like the United States and the European Union are leading in establishing comprehensive AI liability frameworks. They employ existing legal concepts, such as product and strict liability, and are developing new regulations tailored to high-risk AI systems. Conversely, developing countries, constrained by limited resources and infrastructural challenges, struggle to establish robust AI governance frameworks. The digital divide and environmental concerns further exacerbate these challenges.

A pivotal debate in AI liability revolves around developer responsibility. Developers argue for limited liability due to AI’s autonomous nature, algorithmic complexity, and significant user influence on AI outcomes. Overly stringent liability could stifle innovation, deterring developers from pursuing advanced AI projects.An innovative approach to AI liability includes the concept of granting AI legal personhood, drawing parallels to corporate personhood. This would allow AI systems to be held accountable through fines, operational restrictions, or algorithmic rehabilitation, similar to how corporations are regulated. However, this approach also raises concerns about potential misuse, legal complexity, and ethical implications. Comparative studies reveal stark differences in AI liability approaches between developed and developing nations, driven by economic capacity and regulatory readiness. For equitable AI development, it is crucial for developed countries to support developing nations through financial aid, technology transfer, and capacity building. Addressing AI liability requires a balanced approach that fosters innovation while ensuring accountability and public safety. Collaborative global efforts are essential to bridge the digital divide and harness AI’s potential for sustainable development across all nations.

VI. Fading of the legal profession and the future of law

(A) Usage of AI in Judiciary

The inclusion of AI in the judicial system is still a long due process and is still under development. As the saying goes “You reap what u sow”, the efficient use of AI lies in the sufficient capital invested in it and the effective working of the programmers and the other individuals involved in the development of the technology. Despite the pros of the AI technology the cons cannot be put beneath. As much as the technology aids the judicial process it is also susceptible to the risk of being hacked and exploited.

AI has proved to be an efficient tool in simplifying the process of documentation, analysis and organizing the necessary documents and evidence so that ample of time can be saved and can be utilized for other necessary task. The legitimate examples of the pros of AI can be seen in Lexis + AI, SUVAS, SUPACE, SCI-internet, COMPAS, and so on. The Lexis + AI which was launched by Lexis Nexis has the capability of analyzing millions of legal documents in seconds to surface relevant precedents and citations.¹¹

The Supreme Court of India has developed new AI powered portal called SUPACE to act as assistance in the efficient working of the courts. SUPACE AI is tool that collects relevant facts and laws and makes them available to the judge during the process of decision making. This AI in its practical application can be more time and cost effective.

The survey clearly indicates that respondents are equally divided on whether incorporating AI will diminish the legal field. It recommends that AI can be partly used, but not as a complete replacement.

SUVAS, AI technology which can play a vital role in breaking the language barriers of the nation in the near future, was launched by the Supreme Court of India. SUVAS is an AI that can translate judicial documents from English language to the regional languages of India. This can facilitate the easier understanding of the aspects of a legal issue for both the judicial administers and the common folk.

The judicial system of U.S has implemented a support tool called COMPAS. This AI supported tool is an effective tool in finding repeated offenders and facilitates the U.S courts in assessing the likelihood of a first time offender becoming a recidivist (repeated offender).

In spite of proving to be an efficient tool that can reduce the burden on manpower and assist in time and cost conservation, AI is still a developing technology. Hence whole dependence of a government system cannot be trusted in the hands of a half baked technology.

AI is still a contented and controversial topic.As technology has always been vulnerable to malware and hacking, the argument is placed on the credibility of the technology.

The use of AI in the judicial system of India has sparked a concern about job displacement and the fading of traditional legal roles. As AI has automated the repetitive tasks such as drafting, document review and other such tasks done by the learning advocates, this may lead to reduction in the demand for such legal professions which usually require the manpower. This might also lead to lack of experience for the learning advocates and lead to reduction in efficiency of the legal professionals. As much as AI facilitated the legal professionals, it might also diminish the role of advocates in synthesizing complex legal information and serving the people in need of justice.

The people approaching the judicial system might become accustomed to AI driven legal services as it provides quick responses and lowers their expenditure. This might lead to a decline in the traditional legal practices as they cannot compete with the time and cost conservation that the AI technology can offer.

VII. Suggestions

• Establishing a regulatory framework: To address the unique challenges and opportunities given by AI, a new legal framework should be made in order to regulate the actions and inactions of AI.
• Promote inter-disciplinary collaborations: The legal professionals, technologist, and data scientist must collaborate with each other, as this would help each one of them understand the requirements in establishing new AI systems.
• Awareness regarding AI capabilities: The first issue that arises when employing AI is a widespread mistrust of technology that can think for itself; therefore, awareness of AI capabilities can be raised in order to alleviate such anxieties and ignorance of applications. The most crucial point to remember is that AI should not be built with the intention of replacing lawyers, judges, or anybody else in the legal field; rather, it should be used to simplify, speed up, and assure transparency in the present system.
• Educating lawyers, advocates, and students on how to use AI effectively and diligently: Because AI in legal system is still in its infancy, now is the best time to train attorneys and would be lawyers on how to use AI systems.

VIII. Conclusion

“Technology is a useful servant but a dangerous master.”

– Christian Lous Lange

This quote highlights how technology can leverage our lives and connect us across the nation. The emergence of AI in the legal field can be effective, but using it wisely plays a vital role, and we should not rely on it completely. As we have seen in the recent issue of the Microsoft AI slipup, AI is a great tool, but we need to remember that it also has disadvantages that can cause harm to living beings.

In this research, we have explored that to leverage AI technology in our legal profession, it is necessary to introduce new tools with their own legal framework to regulate their actions. Granting AI legal personhood would save developers from being made accountable for AI’s actions and inactions. This would open doors for new technological innovation in the legal field and enhance accountability. Though AI is considered to facilitate legal professionals, it could also decrease the need for advocates in legal procedures. If AI occupies the entire role of advocates, people might become accustomed to AI-driven tools, which could lead to a decline in traditional legal practice.

AI partly invested in the legal field will help us in the process of justice, but if we invest in it wholly, it will question its legacy.

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IX. References

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4. Copeland J, Artificial Intelligence: A Philosophical Introduction (John Wiley & Sons, 1993).

5. “Artificial Intelligence,” available at: https://www.oxfordreference.com/display/10.10 93/oi/authority.20110803095426960

6. "Artificial Intelligence," available at: https://www.merriam-webster.com/dictionary/artificial%20intelligence

7. Collins H, Artifictional Intelligence: Against Humanity's Surrender to Computers (John Wiley & Sons, 2018).

8. Jain P, "Artificial Intelligence for Sustainable and Effective Justice Delivery in India," OIDA International Journal of Sustainable Development 11, no. 06 (2018): 63-70.

9. Harshul Gupta, "Scope of Artificial Intelligence as a Judge in Judicial Sector," Indian Journal of Law, Polity and Administration.

10. "AI PORTAL SUPACE," available at: https://www.drishtiias.com/daily-news-analysis/ai-portal-supace

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