This piece is part of a series of research posts which explores various themes under AI and the Law. The posts are authored by student interns working under the project ‘Exploring Digital Transformation of India’s Consumer Grievance Redressal System through GenAI’.
In November 2023, NLSIU announced a project with IIT Bombay and the Dept. of Consumer Affairs to enhance India’s Consumer Grievance Redressal system using AI. This marked a significant step in the integration of technology within the legal sector. This initiative reflects the broader trend and potential of leveraging Large Language Models (LLMs) to streamline legal processes and enhance access to justice.
Large Language Models (LLMs) demonstrate capabilities that could substantially impact legal operations. Their proficiency is highlighted by instances such as a model like GPT-4 passing the American Uniform Bar Examination and the All-India Bar Examination, where it not only outperformed previous iterations of LLMs but also surpassed the average scores of human test-takers. This achievement underscores the model’s capability in legal reasoning and understanding, hinting at a future where LLMs can automate repetitive analytical tasks, thereby enhancing lawyers’ focus on strategy and complex legal work. This milestone not only showcases the model’s superiority over prior LLMs and human counterparts but also foreshadows its potential role in revolutionising legal practice.
Studies on the commercial AI application sector have projected that up to 44% of legal tasks might be susceptible to automation by AI. This assertion stems from the premise that a significant proportion of legal work comprises the analysis of cumbersome yet structured documents—a task well-suited to the algorithmic precision of LLMs. If these projections hold true, we may see LLMs assume a substantial role in due diligence and contract analysis, potentially freeing lawyers to focus on more nuanced aspects of legal work. By incorporating AI, firms may be able to reimagine their economic structures, moving from a quantitative to a value-driven service model, ultimately fostering a legal environment that prioritises results and client satisfaction over billable time. Richard Susskind, an expert on legal services and their transformation, predicts a shift from time-based to value-based billing in law firms driven by AI advancements. This change aims to enhance cost efficiency, benefiting clients and legal practices alike.
LLMs have the potential to change various facets of legal practice. AI can significantly assist in four key areas: legal research, document generation, legal information dissemination, and intricate legal analysis. This assistance transcends mere task automation and ventures into augmenting the quality of legal services. For instance, LLMs can refine the precision of case retrieval systems by redefining and enhancing search queries. Such advancements not only expedite the retrieval process but also ensure that the most relevant and accurate legal precedents and documents are accessible to legal practitioners, which is crucial for effective case preparation and client representation.
Building on this foundation, envision a sophisticated Online Dispute Resolution (ODR) mechanism that processes case details through an LLM. For example, LLMediator utilises the computational prowess of GPT-4 for streamlining resolutions in high-volume low-stakes disputes. It simplifies user interactions, intelligently drafts responses, and autonomously participates in discussions to facilitate smoother negotiations. This platform not only demonstrates the potential to refine the ODR process but also to significantly enhance access to justice. Promising initial evaluations suggest that LLMediator is paving the way for the future of AI-assisted legal mediation, reinforcing the transformative impact of AI on the legal profession.
In the realm of arbitration, LLM’s capacity to organise and analyse vast quantities of documentation could revolutionise the disclosure and document production phases. By parsing through documents and identifying relevant materials, AI stands to introduce efficiencies that significantly reduce both the time and cost traditionally associated with these stages.
Furthermore, LLM tools can expedite the review of pleadings and submissions by generating concise summaries and suggesting preliminary counterarguments based on the available evidence, thus aiding legal practitioners in swiftly assimilating complex submissions. The efficiency of AI in these tasks could be unparalleled, yet a rigorous check by lawyers remains indispensable to avoid the presentation of misleading information to the tribunal.
On the judicial front, LLMs’ potential in contract interpretation within courts is a significant development. By analysing the language and context of contracts, LLMs can help judges and lawyers understand the nuances and intentions behind the words. This technology doesn’t replace the need for human judgment but supports it by providing a more nuanced view of the contract terms. This could lead to decisions that honour the actual agreement between parties, ensuring fairness and adherence to the contract’s spirit as well as its letter.
Moving beyond solely contractual cases, LLMs have the potential to influence broader judicial decision-making. Currently, judges consider each case on its own merits, but LLMs could offer necessary guidance to ensure a more uniform approach to interpreting laws and precedents. This doesn’t mean a one-size-fits-all justice system but rather a supportive tool that offers consistent legal interpretations while still allowing for the human touch in final judgments. Such a balance has the potential to enhance the efficiency and equity of the judicial process, signalling a shift towards a more modernised, fair, and just legal system.
However, LLMs are not without their own challenges. Reliance on LLMs must be met with caution. Notably, instances of “hallucinations” — when LLMs generate convincing yet false information — pose risks, as seen when a New York judge critiqued lawyer Steven A. Schwartz for using ChatGPT-sourced arguments in a statute of limitations dispute. Challenges are exacerbated by the complex, jargon-laden nature of legal texts, the sheer volume and non-digitized state of many documents, and the high privacy stakes involved in handling sensitive data. For this, Closed AI systems offer a solution by securing user information and ensuring client confidentiality.
Moreover, AI systems must navigate potential biases that can skew outcomes related to gender, religion, or other protected characteristics. Lawyers must also ensure that the AI tools they use adhere to stringent privacy and security standards to prevent breaches and maintain the integrity of legal processes. The deployment of AI-powered solutions also opens up vulnerability to claims of plagiarism.
As we stand on the verge of a legal revolution powered by LLMs, the potential for enhanced efficiency and justice is palpable. However, this technological leap brings with it the necessity for rigorous oversight and ethical frameworks. The European Union’s approach to AI regulation offers a blueprint for responsible deployment of AI, emphasising transparency, accountability, and human rights. Similarly, the United States’ National AI Initiative Act demonstrates a commitment to advancing AI with careful consideration for safety, civil liberties, and socio-economic impacts. Adapting such regulatory wisdom, alongside dedicated research, is crucial to ensure that as LLMs transform the legal domain, they do so in a manner that upholds the rule of law and public trust. Balancing innovation with these safeguards is key to maximising the benefits of AI in law, avoiding potential pitfalls, and securing a just and equitable legal future for all.
Authors
- Mohit Kumar Meena (B.A., LL.B. (Hons.) 1st Year)
- Suresh Gehlot (LL.B. (Hons.) 2nd Year)
- Aman Sunil Patidar (B.A., LL.B. (Hons.) 4th Year)
- Suraj Harish (LLM)
- Meenal Jain (B.A., LL.B. (Hons.) 2nd Year)