Introduction-

Technology has become an unavoidable aspect of human lives. Technology has helped, assisted, and served human life in their personal and work lives both at individual and organizational level. Whereas humans have accepted technology, the new disruption in technology known as the age of Artificial Intelligence has not only been accepted but has been used in ways that have revolutionized the way humans work. Humans have invented various AI based models that can not only help in theoretical work but physical work too. Such as the invention of

Roomba, the smart vacuum that uses AI to scan room size, identify obstacles and remember the most efficient routes for cleaning. The self-deploying Roomba can also determine how much vacuuming there is to do based on a room’s size, and it needs no human assistance to clean floors. Or, IBM Watson which is an AI-enabled tool that can derive the meaning and context of structured and unstructured data that might be critical for selecting a treatment plan and then analyzing the patient’s medical record to identify potential treatment. In other words, IBM Watson functions like a human doctor,[1] Hi Arya, a food-tech company, in collaboration with LeewayHertz, has built a robotic tea maker based on AI and IoT capabilities. The smart tea maker enables users to create their own recipe from a web interface, mobile app, and the machine itself. [2] And then, Self-driving cars are a new face of automobiles that may substitute manual driving hence, making driving and travel completely free of human effort. The companies such as Tesla and Tata and have already launched their Semi self-driving cars. These cars have the capability to dominate the automobile industry.

This disruption in technology has helped in increasing efficiency as well as lessening the burden on the human force. One of the most talked about AI algorithms is Predictive analysis which is a powerful tool that uses AI algorithms to forecast future outcomes based on historical data.

The AI tools based on Predictive analysis can generate answers when questions are posed and can also assist in making decisions if asked.

AI has no doubt revolutionized the face of technology but along with that, it has also led to increased dependency of humans on technology. One such dependency aspect is Using AI tools to make decisions pertaining to Law. The legal industry has seen a commendable increase of AI-based technology for research as well as decision-making in recent times which has raised serious questions pertaining to the ethical and responsible use of technology especially in the legal industry where any decision taken can have catastrophic effects. The use of AI-based technology in critical decision-making questions the integrity of the Legal industry and could potentially lead to a dystopian future where machines have the power to make crucial judgments, ultimately leading to "Judgment Day."

The Rise of AI in Law Decision-Making

The legal industry acts as a compass for society by making laws and regulations as per needs and changing the dynamics of society. These laws, regulations, and policy change is often based on well-researched recommendations given by people belonging to different cliches and simultaneously accepted and executed in the legal world by jurists. Jurists have not only applied the Law made by the legislative, they have also in their functionary helped sustain the law for a better future. In doing so, jurists continue to play an important role in shaping the Law policy formulation of a particular country, their insights and critical thinking have been proven useful more times than one can count.

In India, the Judiciary has time and again evoked its Power given under Article 32 and Article 226 in furtherance of public good objectives which becomes a guiding light for legislation. While the judiciary has important functions to perform, the rise in AI technology and the use of such AI-based tools for research and decision-making may cloud the objective mind and reasoning of jurists furthering the Dark side of AI.  

Over the past decade, the legal industry has witnessed a significant rise in the use of AI and predictive analysis in decision-making processes. The ability of AI algorithms to quickly sift through vast amounts of data and identify patterns has revolutionized the way legal professionals approach cases. This technology has proven to be useful in various aspects of law, including contract analysis, legal research, and predicting case outcomes in criminal trials, etc.

It is prudent that we know what the kinds of AI-powered tools that the Legal industry is using, hereupon there are various examples such as:

1. Legal Research Tools: AI-powered legal research tools help lawyers and legal researchers find relevant case law, statutes, regulations, and other legal documents quickly and accurately. These tools use natural language processing (NLP) algorithms to analyze vast amounts of legal data and provide comprehensive search results. Examples of such tools include Westlaw Edge, LexisNexis, and Casetext.

2. Contract Analysis Tools: AI tools for contract analysis automate the process of reviewing and analyzing contracts. These tools use machine learning algorithms to extract key provisions, identify potential risks or anomalies, and compare contracts against predefined templates or standards. By automating contract analysis, these tools save time and reduce the risk of human error. Examples of contract analysis tools include Kira Systems, eBrevia, and Luminance.

3. Due Diligence Tools: Due diligence is a critical process in legal transactions such as mergers and acquisitions. AI tools can assist in due diligence by automatically reviewing large volumes of documents to identify relevant information, flag potential issues or risks, and provide summaries or visualizations of the findings. Due diligence tools like Relativity Trace, Diligen, and Legatics help streamline the due diligence process and improve accuracy.

4. Document Review Tools: Document review is a labor-intensive task in litigation where large volumes of documents need to be reviewed for relevance, privilege, or confidentiality. AI-powered document review tools leverage machine learning algorithms to classify documents based on predefined criteria and prioritize them for human review. These tools can significantly reduce the time and cost associated with document review. Examples of document review tools include Relativity, Everlaw, and Catalyst.

5. Predictive Analytics Tools: Predictive analytics tools in the legal industry use AI algorithms to analyze historical case data and predict case outcomes, judge behavior, or settlement amounts. These tools help lawyers make informed decisions, assess risks, and develop effective legal strategies. Examples of predictive analytics tools include Lex Machina, Premonition, and Ravel Law.

Most of these tools are working on Predictive Analysis AI based algorithm.

Predictive analytics is a branch of advanced analytics that makes predictions about future outcomes using historical data combined with statistical modeling, data mining techniques and

 machine learning.[3]

One of the primary advantages of predictive analysis in the legal industry is its ability to provide lawyers and judges with valuable insights into the potential outcome of a case. By analyzing historical data, AI algorithms can accurately predict the likelihood of success or failure in a legal matter, allowing legal professionals to make more informed decisions regarding case strategies, settlement negotiations, and even sentencing in criminal cases. One of the primary applications of AI predictive analysis in the legal industry could be in the criminal justice system wherein these algorithms access recidivism risk. By analyzing a wide range of factors, such as criminal history, socioeconomic background, and demographic information, AI algorithms can predict the likelihood of an individual committing future crimes. This information can help judges and parole boards make more informed decisions regarding pre-trial release, probation, or parole. AI can analyze vast amounts of crime data to identify patterns and trends, enabling law enforcement agencies to allocate resources effectively. By understanding where crimes are likely to occur, authorities can proactively deploy personnel and implement preventive measures to mitigate criminal activities.

 Additionally, the use of AI in the legal system saves both time and money. Manual research and analysis can be time-consuming and prone to errors, whereas AI algorithms can quickly process large volumes of data and provide results in a fraction of the time. This increased efficiency allows legal professionals to focus on other critical aspects of their work, ultimately leading to improved productivity and better client service.

According to an article in New York Times,

New AI tools such as ChatGPT with its humanlike language fluency and the ability to answer most of the questions could take over much of legal work. The article also claims that the Legal profession is most prone to the risk of elimination now that AI technology has started to take over the work of lawyers and jurists.[4]

However, with the rise of AI in law decision-making, concerns have emerged about the potential consequences of relying too heavily on machines and technology. The ethical dilemmas surrounding the use of predictive analysis in the legal field are numerous, and the risks associated with AI-powered decision-making systems cannot be ignored.

Legal jurisprudence, a NO Entry for AI: The Potential Risks and Ethical Concerns

“If you’re not careful, you risk automating the exact same biases these programs are supposed to eliminate,” says Kristian Lum, the lead statistician at the San Francisco-based, non-profit Human Rights Data Analysis Group (HRDAG)’

While the use of predictive analysis in law decision-making offers numerous benefits, it is essential to acknowledge the potential risks and ethical concerns associated with this technology. As AI algorithms become more sophisticated, there is a growing concern that they may reinforce or even amplify existing biases within the legal system. One of the primary risks is the possibility of algorithmic discrimination. If the AI algorithms are trained on biased historical data, they may unintentionally perpetuate discriminatory patterns, disadvantaging certain individuals or communities.[5]

Some real-life examples of AI Algorithms becoming inherently discriminatory are-·

According to US Department of Justice figures, you are more than twice as likely to be arrested if you are Black than if you are white. The new AI algorithms are based on historical data that has been fed into it which is making the said algorithm discriminatory. A Black person as per these AI algorithms is five times as likely to be stopped without just cause as a white person.[6]·

The program, Correctional Offender Management Profiling for Alternative Sanctions (Compas), was much more prone to mistakenly label black defendants as likely to reoffend – wrongly flagging them at almost twice the rate as white people (45% to 24%), according to the investigative journalism organization, ProPublica.[7]·

In a study[8] of Google Research, it was found that in India only 50 percent people are Internet users and based on the said data, collected datasheets under-represented Muslim and Dalit populations due to their lack of Internet use.

The above are some of the significant examples of AI algorithms becoming biased and discriminatory based on whatever data and historical background is fed.

Another major concern while using AI algorithms is the credibility of data that is available and that is generated in response to your query. OpenAI, the company that launched AI-based Chatbot called ChatGpt recently said that, ‘

ChatGPT will occasionally makeup facts or ‘hallucinate’ outputs.” In this instance, “hallucinate” means that the AI says things that are inaccurate because of limitations in its training or a lack of understanding of the real world’. The fact that AI-based algorithms can reach a conclusion based on limited information, led to inaccuracy and lack of transparency in AI decision-making.

AI systems often operate as black boxes, making it difficult for individuals to understand how decisions are reached. This lack of transparency can undermine trust in the legal system and limit accountability. Additionally, there are ethical considerations regarding the use of personal data in predictive analysis. As AI technologies collect and analyze vast amounts of data, there is a heightened risk of privacy breaches and potential misuse of sensitive information.

AI algorithms heavily rely on vast amounts of accurate and unbiased data. In the criminal justice system, historical data may be incomplete, outdated, or biased, which can impact the accuracy and fairness of AI predictions. Additionally, privacy concerns arise when sensitive personal information is used to train and deploy these algorithms. Strict regulations and ethical guidelines are necessary to ensure the responsible and ethical use of AI predictive analysis. AI algorithms can be complex, which are only understandable to people belonging to technology cliché and a person from legal field may or may not be able to interpret and/or authenticate such data, making it challenging to interpret their decision-making processes. This lack of transparency raises concerns regarding accountability and fairness. Efforts should be made to develop explainable AI models that allow for comprehensible decision-making processes and provide justifications for the outcomes

[9].

AI has and will become a crucial part of all of our lives making it unavoidable but it is crucial that AI should not eliminate humans entirely, especially in an industry such as that of law wherein any decision taken could lead to life/death. It is essential to strike a balance between the use of AI technology and human expertise to ensure a fair and just criminal justice system.

It is also pertinent to mention here that relying on sentencing in criminal trials based on AI based algorithms may lead to vast imparity in sentencing, judgment, and/or in the process of bail because of the data used by AI algorithms. Penal laws are made to inherently protect the victim as well as the offender and that is why it is largely important to not rely on such algorithms or machinery in criminal sentencing. While AI algorithms can efficiently process vast amounts of data and identify patterns, they lack the ability to understand nuance, context, and morality.

[10]Decisions made solely based on data-driven predictions may fail to consider the unique circumstances of each case, leading to unjust outcomes. Therefore, human judges and legal professionals must retain the power to intervene, question, and challenge the decisions made by AI systems.

Legal professionals, defendants, and plaintiffs should have access to information about how algorithms operate, the dataset they were trained on, and the factors considered in making predictions. By providing this transparency, individuals can question and challenge the outcomes, ensuring accountability. Furthermore, regulatory frameworks are essential to prevent algorithmic discrimination, bias, and privacy breaches. Clear guidelines should be established regarding the circumstances in which the use of AI is appropriate, alongside the necessary checks and balances that must be in place.

It is crucial to strike a balance between efficiency and fairness. While AI can expedite the decision-making process, it must not compromise the principles of justice and equality. There is a risk that AI algorithms, if not carefully designed and regulated, could perpetuate existing biases and inequalities, leading to unjust outcomes. Achieving this balance requires continuous monitoring and evaluation of AI systems. Regular audits should be conducted to identify and rectify any biases that may arise. Additionally, training data used for algorithmic models should be diverse and representative of different demographics to avoid perpetuating racial, gender, or socio-economic biases.

Conclusion: Navigating the future of AI in law decision-making

The critical question that stands in front of us is- ‘Should AI be making these decisions for us’ or more fundamentally ‘Should we rely on such decisions?’

As we have delved into the world of predictive analysis in law decision-making, it becomes clear that the potential benefits of AI are immense. With its ability to streamline processes and enhance efficiency, AI has the power to revolutionize the justice system. However, we must remain vigilant to the risks associated with relying solely on technology to make vital decisions.

The key challenge we face is striking the delicate balance between efficiency and fairness. It is crucial that AI algorithms do not perpetuate the biases and inequalities already inherent in our society. To achieve this, we must implement rigorous evaluation and monitoring systems, conduct regular audits, and ensure that training data is diverse and representative. Moreover, it is vital to harness the power of human judgment in conjunction with AI. While predictive analysis can provide valuable insights, the ultimate decision-making authority should rest in the hands of qualified judges, who can critically assess the information provided by AI systems. Ultimately, the future of AI in law decision-making depends on our ability to navigate these challenges. By continuously refining and improving AI systems, we can ensure that they are fair, just, and uphold the principles of our legal system. Let us forge ahead with caution and resolve, using AI as a tool that complements and supports our pursuit of justice.

Establishing clear guidelines and robust regulatory frameworks is essential to ensure that AI technologies in the legal field are used ethically and responsibly. These frameworks should specify the circumstances in which the use of AI is appropriate and outline the necessary checks and balances to prevent algorithmic discrimination, bias, and privacy breaches. Moreover, incorporating transparency mechanisms into AI systems is vital for building trust and confidence in their decisions. It is crucial to develop methods that allow individuals to understand how the algorithms operate, the dataset they were trained on, and the factors considered in making the predictions. This transparency will enable legal professionals, defendants, and plaintiffs to question and challenge the outcomes, ensuring accountability.

[1] IBM, Watson [2] Akash Takyar, ‘AI use cases and Major Applications in various industries’, LeewayHertz [3] IBM, 2022, ‘Predictive Analysis’ [4] Steve Lohr, April 10, 2023, ‘AI is coming for lawyers, again’, The New York Times (nytimes.com) [5] "The Impact of Artificial Intelligence on the Practice of Law", American Bar Association (ABA) Journal [6] Will Douglas Heaven, July 17, 2000, ‘Predictive policing algorithms are racist’, MIT technology review [7] Stephen Buranyi, May, 2017, ‘Rise of Racist Robots’, , The Guardian [8] Sambasivan, Arnesen, Hutchinson, Doshi, Prabhakaran, Re-imagining Algorithmic ‘Fairness in India and Beyond’, Google Research [9] Thomson Reuters, ‘Artificial Intelligence in Law: The State of Play 2019’,Legal Executive Institute [10] ‘Artificial Intelligence & Law Overview’, Stanford University Libraries