Paving the Way for AI Driven Justice in MACT Tribunals

Introduction

The existing jurisprudential discourse on AI integration in the legal sphere may seem saturated at first, with incessant intonations comparing a myriad of possible assistance and circumvention of the law. However, multiple potential and growing avenues remain to be explored and embraced by the legal community. AI-based innovations to facilitate smoother and quicker legal decision-making in a variety of disputes have been cropping abound, the most recent of which are insurance claims vis-à-vis road traffic accident claims. Currently, at a nascent stage, various innovators and service providers have introduced certain AI model-based systems to help with faster and more accurate data collection, collation, and analysis in accident claims situations to assist the insurance and legal teams.

This blog aims to not only explore the functionality and practicability of these new technologies in the Indian context but also analyse if the same may be of service in the judicial decision-making process itself. The authors will attempt to explore if such technologies may be helpful in relieving the Motor Accident Claims Tribunals of the herculean burden of processing lakhs of claims every year. Firstly, a brief overview of the present law on Motor Vehicle accidents will be requisite for a comprehensive understanding. Secondly, the authors shall delve into the recently introduced AI systems for insurance and road traffic accident claims/disputes, as well as court management over the globe. Lastly, learnings and challenges from the same will be analysed and their practicability will be discussed in the Indian context.

A Brief Overview of Motor Vehicles Accident Law in India

The Motor Vehicles Act, 2019, and the Central Motor Vehicles Rules, 1989 (last amended in 2022) are the primary legislations governing motor vehicle accidents in India. The most pressing developments vis-à-vis these laws have been the Special Scheme, the Disbursement Scheme, and the fast DAR Scheme devised by the Delhi High Court over the last decade. While the former two were approved and notified to be followed by all courts by the Supreme Court, the latter has been incorporated into the Motor Vehicles Act, 2019, and the Central Motor Vehicles Rules, 2022.

As per a report published by the Ministry of Road Transport and Highways (MoRTH) for the year 2022, a total number of 4,61,000 road accidents were reported in the country, claiming 1,68,000 lives and causing injuries to 4,43,000 persons. In a country such as India, the lives lost to motor vehicle accidents are often those of the sole breadwinners of the family, therefore magnifying the emotional and financial strain. In light of the nature and frequency of such accidents and their long-drawn and tedious fate in the Indian judicial system, the Delhi High Court devised the abovementioned schemes.

The Special Scheme revolutionized the Motor Accident Compensation Law in as much as the claimants received the compensation within 120 days of the accident, by laying out a comprehensive procedure for the same. The Disbursement Scheme further streamlined the procedure by mandating the compensation award amount be kept in monthly FDRs, to be transferred to the claimant’s bank account. The Supreme Court directed the National Legal Service Authority (NALSA) to monitor the compliance of the schemes by Claims tribunals in coordination and cooperation with the High Courts.

The Fast DAR scheme, now incorporated within the legislation, was also developed by the Delhi High Court which ensured a speedy compensation disbursement within ten days of the accident in certain specified motor accident deaths. While these schemes have enhanced the convalescence of the ailing accident claims system, complete recuperation is yet quite far down the road as mounting pendencies, compliance issues, and inherent red-tapism continue to prolong and exacerbate the existing systemic issues. Therein comes the need to explore new-age developments in the judicial context for faster resolution of accident disputes and distribution of compensation to the victims.

Lessons from AI application in Insurance Claims Abroad

In recent years, AI applications in insurance claims in a plethora of foreign jurisdictions have sparked new discourse surrounding its inclusion in vehicle accident claims in the legal context. AI’s general advances in the auto vehicle and insurance sectors have been observed by way of efficient data collection, collation, protection, and analysis, the verification of information crucial to the case, and risk assessment. Using this as a foundational argument, the authors believe that these developments are the predecessors to more efficient claim validation and verification using AI applications. Since the abovementioned advances have proven to be helpful in validating such claims by insurance companies, it is imperative to explore a similar use in the legal and judicial contexts as well.

If the courts were to have access to such data reflecting the risk assessment, information, and damage caused in each individual case, not only would it speed up the litigation process, but it would also contribute towards a more objectively fair legal decision-making process. When the court has access to effectively analysed data pertaining to the case, it opens up possibilities for reducing the number of contentious facts and any scope for legal practitioners to misrepresent the facts due to subjectivity or errors in data collection and analysis. It would streamline the litigation proceedings from a nascent stage by establishing important facts at the centre of the case and highlighting any inconsistencies, enabling the court to identify the precise nature of the facts, the risk and damage assessment, and consecutively nip frivolous litigation in the bud. Judges’ legal researchers and clerks working towards verifying and researching the arguments, claims, and facts presented in the court would highly benefit from such AI applications in motor vehicle accident claims, and provide the judges with a more comprehensive and objective understanding of the case, its verifiability, and quantifications in terms of damages and compensation.

It has a multitude of fruitful applications not just in a purely litigious sphere, but also in mediation and negotiation proceedings. Analyses, observations, and predictions made by such AI applications would further contribute to smoother, faster, and more efficient proceedings in alternate dispute redressal mechanisms. Primarily, with the precise calculations pertaining to the nature of the damage sustained and the risks involved, negotiating the quantum of compensation would be greatly enhanced and streamlined.

AI applications have also been developed to extract information from medical/injury reports, specifics of the vehicle, etc. to accurately identify injury characteristics. A comprehensive text analysis framework is developed on the basis of the same to pave the way for more levelled and straightforward proceedings. A fieldwork study in the UK has shown promising results in civil litigation by application of such AI techniques and software.

These AI-based systems are developed to identify and evaluate patterns and inconsistencies across various vehicle components, including structural elements and surface features. They leverage machine learning and computer vision to perform a full-scale analysis, encompassing damage assessment, image and data analysis, defect identification, severity evaluation, integration with diagnostic tools, and support in determining liability. In the Indian context, the parameters and formulae as laid down by recent amendments and the Apex Court’s guidelines applied by using the framework model discussed would tremendously lighten the courts’ burden in resolving such disputes.

Integration of AI in Indian Court Management

The Indian courts, despite serving a noble cause, are often criticized for their inefficiencies, delayed justice delivery system, and human fallibility. This is particularly true in the case of motor accident claims which account for the highest number of pending cases in any category. In order to counter the same, AI is seen as a potential transformative ally to overcome these defects. AI has proved to be fruitful in addressing these aspects as seen in the case of insurance claims. Further, the integration of AI techniques in the day-to-day functioning of the court can streamline the settlement of motor accident claims, as is seen in the case of insurance claims.

One of the most important aspects of AI which can be used in motor accident claims is its predictive justice feature. As observed in many cases, there are often a lot of similarities between claims of motor accidents which exhausts a major chunk of the judiciary’s time. Here, AI can assist in predicting case outcomes by analyzing case data, which helps speed up the resolution process. Additionally, it ensures consistency and fairness in judgments. For the same purpose, legaltech softwares like “Premonition” and "LexMachina" have proved to be handy giving in-depth details about the judicial process. In India, the implementation of predictive justice features can follow a hierarchical approach, beginning with their application in the Apex Court. Given its authoritative position, the Supreme Court would be best suited to assess the efficacy of such technologies. Upon successful evaluation, these features can be progressively integrated into the lower judiciary, taking into account the administrative and financial constraints that may pose challenges at that level.  Secondly, the Machine learning (ML) methods of online legal databases have helped in easing the task of the legal professionals which has ultimately resulted in effective and timely delivery of justice. For instance, AMICUS, CaseMine's GPT-powered solution, has significantly helped in simplifying the tasks of legal research and document drafting process. The process of legal research and drafting have proved to be a tedious task particularly in the motor accident claims due to lack of legal databases on the same owing to the niche nature of the industry. Therefore, such legal chatbots can be particularly used by the court researchers and other research scholars in their legal research and drafting work. However, while using these AI tools it must be kept in mind that the efficiency of these tools is still uncertain, which leaves room for errors and inaccuracies in their usage and hence, the output from them must only be used as a guide and should be cross-checked before putting them into final use. 

Additionally, during the Covid 19 pandemic, the amalgamation of AI in the scheduling of cases, file management, and other administrative work was made possible through the e-Courts Mission of the government, which can contribute to increasing the judicial proficiency in motor accident claims. Recently, the Chief Justice of India unveiled an AI assistance project, where AI-powered tools like SUPACE (Supreme Court Portal for Assistance in Court's Efficiency) have come to the rescue of the courts. With the help of these tools, real-time transcription of court proceedings has been made possible and new data extraction techniques have also been introduced with the help of tools like SUVAS (Supreme Court Vidhik Anuvaad Software). 

Further, AI-powered tools have also proved to be particularly useful in assisting judges in the process of risk evaluation. This phenomenon can be particularly observed in  foreign jurisprudence where chatbots like COMPAS (Correctional Offender Management Profiling for Alternative Sanctions) of the US and the advanced digital case system of the UK have helped in lowering the accumulation of cases. The Indian judiciary has not lagged behind in this aspect as observed in the case of Md Zakir Hussain v. State of Manipur, where a precedent was set by engaging ChatGPT for research in a service matter.

Lastly, some of the international techniques like the Civil Resolution Tribunal (CRT) found in Columbia, Canada, etc. are established in order to deal with issues of subsidised housing, and collision cases. CRT uses an AI Solution Explorer approach and helps in guiding the dispute resolution process using various calculation aids and processing of public legal information. Another example of the integration of AI in court management can be ‘e-Discovery’, an automated investigation device from the US. It is an AI algorithmic programme which extracts the relevant parts from a large amount of information. The most glaring feature of this programme is that the parties agree to the terms of the coding that this programme will use before deploying it, therefore giving party autonomy the requisite importance. Therefore, such AI-based digital databases could be incorporated in India, so as further streamline the resolution of cases.

Paving the Way Forward

However, it will not be an easy road to adopt and implement the use of such AI-application across MACT tribunals in India. As a developing nation, it might be quite difficult to fund the installation of such software, as well as the provision of training to use them appropriately in the legal system. Excessive energy consumption and the environmental costs that come hand in hand present more hurdles in the adoption of such AI applications.

Nevertheless, hierarchical, wise, and limited application as suggested above by the authors, would certainly pave the way for a more streamlined resolution of such cases that do not involve a meticulous application of judicial mind and yet pose as a herculean task to be resolved. Better claim validation from injury reports and vehicle specifics data, cost and time savings, accurate and objective quantifications in terms of damage and compensation, effective court management, legal research and drafting, and all complimentary assistance AI application would provide in motor vehicle accident claims in the Indian judicial context are worth deliberating upon in today’s legal discourse.

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This article has been authored by Pranav Gupta, Associate Editor and Jahnvi Thanvi, Junior Editor at RSRR. This blog is a part of the RSRR Editor’s Column Series.