Author :SV Srividya and  a student at ICFAI Law School.

ABSTRACT

The integration of Artificial Intelligence (AI) into the field of law is rapidly reshaping legal practices and systems worldwide. This abstract explores the intersection of AI technology and law, examining its current applications, implications, challenges, and future prospects. AI technologies, are being used to improve access to justice. These technologies enable the automation of repetitive tasks like contract review, legal research, and document analysis, thereby reducing time and costs associated with legal proceedings. In the realm of legal research, AI-powered tools can efficiently sift through vast amounts of case law, statutes, and legal texts to extract relevant information and provide predictive analytics. This capability not only accelerates the research process but also assists lawyers in formulating stronger arguments and strategies. Virtual legal assistants and chatbots equipped with AI can provide preliminary legal advice and guidance, particularly beneficial for individuals and businesses without easy access to traditional legal services.

However, the integration of AI in law also raises significant challenges and ethical considerations. Issues such as bias in algorithms, data privacy concerns, and the need for transparency in automated decision-making systems require careful attention. Moreover, the ethical implications of relying on AI for legal judgments, particularly in sensitive cases involving human rights or criminal justice, necessitate robust frameworks and guidelines.

Looking forward, the future of AI in law holds promise for further innovation and adaptation. Collaboration between legal professionals, technologists, and policymakers will be crucial in navigating these developments responsibly and effectively.

In conclusion, AI’s integration into the field of law represents potential benefits in easy accessibility, and efficient decision-making. Addressing challenges and ethical concerns proactively will be essential to harness AI’s full potential while upholding the principles of fairness, justice, and accountability in legal systems.

INTRODUCTION

Artificial Intelligence (AI) technology is increasing rapidly, impacting various sectors from healthcare and finance to transportation and security. However, these advancements come with significant legal and ethical challenges that are needed to be addressed on a global scale. To understand easily, it can be said that AI swiftly performs those types of task which usually require human intelligence and cognitive thinking when humans are performing them. That means we apply high-order cognitive processes in order to complete such type of tasks where AI can do it so easily, efficiently, swiftly and can also provide with the best solution possible. For example, when we humans play chess, we apply high level thinking process and various cognitive processes like reasoning, strategising, planning and decision making. In short, when engineers automate an activity that requires cognitive activity when performed by humans, it is common to describe this as an application of AI. AI does this tasks by acquiring data through detecting patterns or through information that has been specifically coded by humans into a form which can be run/processed by computers. Using this computational process, AI is able to produce rather good results on tasks which are often considered as complex by humans and also require human intelligence. But AI systems computational processes cannot be compared to or matched with human thinking.

AI approaches fall into 2 categories: 1. Machine learning and 2. Logical rules and knowledge representation.

1. Machine learning: Based on the term, one might assume that machines/systems are learning in a way how humans usually do. But that is not whats happening here, rather when we use the term learning, we usually mean one’s progress at a particular task over the time. The same can be applied in this scenario. Machines constantly improve their performance themselves by assessing data, and any additional patterns that is being provided. Let us try and understand how machine learning systems use patterns in data to produce intelligent results with an example. Consider an e-mail spam filter. Most e-mail software detect incoming spam e-mails and divert them into a separate spam folder. Now the question arises how does such a machine-learning system automatically identify spam? To answer this, it can be said that they train the system by giving it multiple examples of spam e-mails and multiple examples of “wanted” e- mails. The machine-learning software then detect patterns across these example e-mails and later uses them to determine whether the new incoming e-mail is either spam or wanted. For instance, when a new e-mail arrives, users are usually given the option to mark the e-mail as spam or not. Every time users mark an e-mail as spam, they are providing a training example for the system.

Few points that can be noted from this example in order to understand machine-learning:

1. The software can learn a useful pattern on its own

2. The software can improve its performance overtime with more and more data

2. Logical rules and knowledge representation: Let us better understand this using an example which is used in legal field. Their is a software called TurboTax, it is basically a tax preparation software. To develop this, the developers in consultation with tax attorneys and others experts in the personal income tax laws, translate the meaning and logic of tax provisions into a set of comparable formal rules in a way that a computer can process. Imagine that there is a tax law that says that for every rupee of income that somebody makes over Rs. 51,000/-, she will be taxed at a marginal tax rate of 20%. A programmer can take the logic of this legal provision and translate it into a coding computer rule that exactly represents the meaning of the law. Which means if the income of an individual is more than or equal to Rs.51,000/-, then he/she should pay a tax of 20%. This is a completely different approach compared to machine-learning. In this approach, the developer must provide with the rules, operation and decision way ahead of time,. Whereas in machine- learning, the computer determines its operating rules on its own with more data.

Let us take another example, one major problem in self-driving vehicles is often there are many unexpected circumstances and it is kind of a difficult and an impossible task to note down all the possibilities that might take place and to learn/train a software about all those possible scenarios. For an instance, if there was an accident blocking an entire road and police offices were temporarily rerouting the vehicles onto a sidewalk. A self-driving vehicle driving may not know what to do in such a case. In such cases one of the popular approaches in self-driving cars is known as remote assist. When a self-driving vehicle encounters a situation where it doesn’t know what to do, then it can essentially call for help to a call center. There, the representatives (humans) can see what is going on through the self-driving car’s sensors and figure out what to do. This is one of the examples where there is a limitation to AI in performing tasks.

AI technology works best for activities in where there are patterns, rules, definitive right answers. AI tends to work poorly in areas that are conceptual, abstract, require common sense or intuition or involve societal norms. That means in general, AI works well for tasks that have definite answers such as right-or-wrong answers, which are clear and unambiguous. For example, one reason that spam detection is susceptible to AI automation is that there are right-or-wrong answers in that domain i.e., in general, a given e-mail either is spam, or it is not. Chess is another example where AI has certainty since it is also case of right or wrong answers, which ends with check-mate. Whereas, when a government takes a decision to put a homeless shelter in one particular neighbourhood is not the type of problem that has an objective answer. It is a public-policy issue open to subjective interpretation and involves costs and balances among societal interests and members. AI tends to work well in situations where the patterns or structure can be discovered in data. E-mail spam detection example can be used here. AI tends to be successful involve problems where fast computation, search, or calculation provides a strong advantage over human capacity.

AI IN LAW

All this might trace back to Gottfried Leibniz in the 1600s. Leibniz, the mathematician who famously co-invented math calculus, was also trained as a counsel and was one of the foremost to probe how fine formalisms might ameliorate the law. Since at least 1987, the International Conference of Artificial Intelligence and Law( ICAIL) has held regular conferences showcasing these operations of AI ways to law.

Pioneering researchers in the area of AI and law include Anne Gardner, L. Thorne McCarty, Kevin Ashley, Guido Governatori, Giovanni Sartor, Ronald Stamper, Carole Hafner, Layman Allen, and many other excellent researchers. One useful way of thinking about the use of AI within law today can be divided it into three categories of AI users: 1. The administrators of law 2. The practitioners of law and 3.Those who are governed by law. These are the 3 main categories of people who use/can use AI for legal studies, research and implementation. AI can be useful in many ways from studying and organizing a case papers to helping us find a relevant case law to support our argument, AI can do many things that may take lot of effort and time when a human does it.

LEGAL IMPLICATIONS

AI is useful in cases of litigation discovery and reviewing. As we know, litigation discovery is the process of obtaining evidence for a lawsuit. Often this amounts to obtaining and reviewing large troves of documents produced by other party’s counsel or our own supporting and relevant docs. It is usually done by advocates, in order to find whether a particular document is relevant to our case or not, whether it is true in nature or not, whether it can be adused as evidence or not. This takes a lot of time and energy. Often this reviewing needs to be done quickly too. It is when in the mid-2000s, an electronic discovery, so-called predictive coding and technology-assisted review became possible. Predictive coding is the general name for a class of computer-based document-review techniques that aim to automatically distinguish between litigation-discovery documents that are likely to be relevant or irrelevant. But in the end attorneys would be the ones to make the decision as to whether the documents are or are not relevant to the case and the law. AI is not capable of dealing with strategy based decisions, that means it does not know how to use a document in our favour to make the case represent easier.  Thus, rather than having attorneys opine over a vast sea of likely irrelevant documents, the software can be used to filter out the most irrelevant documents, to reserve the limited attorney-judgment time. At the end of the day, it is still a person, not a computer, who is making the decision as to whether a document is helpful and relevant to the law and the case at hand. Few scenarios where AI can be of risk are:

Data Privacy and Security: AI systems often rely on vast amounts of data, raising concerns about privacy breaches and data security. Legal frameworks such as the GDPR in Europe and various data protection laws worldwide aim to safeguard individuals’ rights and regulate the use of personal data by AI systems.

Liability and Accountability: Determining liability for actions or decisions made by AI poses a significant legal challenge. Who is responsible when an autonomous vehicle causes an accident, or when an AI-powered medical diagnosis leads to a misdiagnosis? Legal systems are grappling with establishing frameworks to assign liability fairly and ensure accountability. Or when a judge or a policy maker needs to take a decision, AI can be of no help, since AI does not have the capability of taking abstract and common sense based decisions.

Intellectual Property Rights: It is often raised as a big threat in today’s world where everything is basically being runned by computer software. AI systems generate creative works or make inventions. Questions about ownership of AI-generated content and the patentability of AI-generated innovations challenge existing intellectual property laws. And also when a question is being asked to AI, the same answer which is being given by AI can be copied and used as one’s own. This might lead to plagiarism. Since, it is AI’s work that is being copied, the real humans brain is not being used here and we can find AI detected content completely.

Regulation and Governance: Governments worldwide are working to establish regulations that govern the development, deployment, and use of AI technologies. These regulations aim to promote innovation while addressing risks such as bias in AI algorithms, misuse of AI for surveillance purposes, and potential job displacement. AI, though it does not have the skills to decide normal abstract or common sense based questions or decisions but the skills which AI have are incomparable. It can be posed as a great threat.

ETHICAL IMPLICATIONS:

Bias and Fairness: AI systems can perpetuate biases present in training data, leading to discriminatory outcomes in areas such as hiring, lending, and criminal justice. Since, it does not have an ability to take decisions using common sense, it simply finds the data pattern on how they are being hired and follows the exact same pattern. Ethical considerations involve fairness and equity in AI decision-making processes.

Transparency and Explainability: The “black box” nature of some AI algorithms raises concerns about transparency and the ability to explain AI-driven decisions. Ethical guidelines advocate for transparency in AI systems to build trust and accountability.

Impact on Employment: The widespread adoption of AI technology has sparked debates about its impact on jobs and the workforce. Ethical discussions focus on addressing potential job displacement, upskilling workers for new roles, and ensuring a just transition to a digital economy.

Autonomy and Human Control: As AI systems become more autonomous, questions arise about the level of human oversight and control necessary. Ethical frameworks emphasize the importance of maintaining human agency and decision-making authority in critical domains such as healthcare, law enforcement, and warfare.

CONCLUSION

Navigating the legal and ethical frontier of AI technology requires a delicate balance between fostering innovation and addressing societal concerns. Robust legal frameworks and ethical guidelines are essential to harnessing the potential of AI while mitigating its risks. As AI continues to evolve, ongoing dialogue among policymakers, technologists, ethicists, and the public is crucial to shaping a future where AI benefits society responsibly and ethically.

This article explores the multifaceted challenges and opportunities presented by AI technology, aiming to provoke thought and discussion on how we can navigate this complex landscape effectively.

REFERENCES

Artificial Intelligence and Law: An Overview by Harry Surden Georgia State University Law Review: Volume 35 Issue 4 Summer 2019