By Vikas Sahita
Vikas Sahita is the Head of Product at https://NearLaw.com and an internet entrepreneur.
Current judicial system needs a revamp
According to the National Judicial Data Grid, over 2.6 crore cases are pending across all the local, district and high courts—including the Supreme Court—and close to 9% of these cases have been pending for over 10 years. On average 30,000 cases are filed and 28,000 cases adjudicated daily. This means that there is a shortfall of 2,000 cases that are left undecided each day, leading to a backlog of 7.3 lakh cases being added to the total cumulative backlog each year.
The backlog of cases falls within the purview of the administrative function of the judiciary. Yet the solution to this perennial problem would seem to involve an exponential increase in the executive funding for judicial infrastructure and court expansion. For retaining trust in the judicial system, it is imperative that the executive and judiciary act in good faith to provide a legal resolution to these cases, especially the ones that have been pending for more than 10 years.
Relevance of judgements in legal research
Lawyers in common law jurisdictions (India, the UK, Canada, US, etc) use case-law from the higher courts as precedent in subsequent cases that involve similar or identical circumstances. As a rule of judicial responsibility, judges are bound to follow the decisions of superior courts or previous decisions from the same court. Judgements which are frequently cited are known as landmark decisions and take on a disproportionate importance. Judges mark their pronouncements as either ‘reportable’ or ‘unreportable’, depending on the relevance and applicability of the legal principles involved, for future cases.
In this system, lawyers, when arguing cases, need to delve deep into hundreds of relevant cases and peruse thousands of pages of previous decisions in order to identify the precedents that exist in favour of their client. Lawyers also need to identify the opposing arguments based on case-law so they are prepared to argue their case, which doubles the research work required.
Evolution of the legal research industry
Legal research is an essential service for the smooth functioning of the legal services market and equalled $6.1 billion in 2011-12. However, legal research has been languishing in the era of ASP and .NET driven software for lawyers. Traditionally, law journals in printed form would condense the ratio decidendi or legal ratio (summary) of a judgement and present it in a headnote with corresponding paragraphs where the principle at law would be presented and the verdict noted. Analysing and reporting on these journals is a laborious task and requires the attention of an experienced legal expert, well versed in legal proofreading, content comprehension and abstraction.
In the 1990’s and early 2000’s the electronic storage of data moved to CDs/DVD-ROM’s. The law journal moved their electronic media and pay-for-view database access onto a software system that could be run locally on subscribers’ machines. The software was static, required online updates through a manual process and did not have automatic analysis or use any artificial intelligence or take advantage of machine learning techniques.
The idea behind the system was to offer the Google experience and simply apply it to the legal database. This approach more attuned to the idea of an e-book library, transferring material via encrypted files to a host computer. However, the user interface and software was not attuned to the future needs of legal practitioners and has been left behind by the changing dynamics of the technological landscape.
Nowadays, legal research tasks and summarization processes have been delegated to computer programs and software, such as Natural Language Processing (NLP) tools. Canadian researchers at the University of Montreal released an academic paper in 2004 where they described a new methodology to create labelled data from legal judgments and then developed a system that automatically produced summaries of the legal abstracts. According to other extrinsic tests, the summarization methodology has an accuracy rate of roughly 90%. Google has now released the source code of their NLP tool, called TensorFlow, which they use for generating Google news headlines based on indexed text from various news sites.
New startups and innovation using AI/ML
Consumer internet products that use AI/ML technology such as smart assistants (Alexa, Siri, Ello, etc) are slowly overtaking traditional platforms. According to a report by Tata Consultancy Services (TCS)—the largest software services company in India—68% of Indian companies use AI for IT functions. Indeed, 70% believe that AI’s greatest impact by 2020 will be in non-IT areas such as marketing, customer services, finance, and HR. Also, a majority of companies believe that adopting AI technologies will be a significant part of remaining competitive. The primary goal of all AI-enabled innovation is to minimise human labour and augment human capability to the maximum extent possible.
With the pace of innovation in NLP increasing, AI has become a potentially disruptive force in the legal services sector. Top legal firms, such as Cyril Amarchand Mangaldas, are now leveraging the power of AI for contract analysis and review. The start-up scene in the legal services sector has started to heat up, with US investors already turning their attention to legal services start-ups such as RavelLaw and RossIntelligence. These new-age legal research start-ups are using big-data analytics to give advice on whether a case is winnable, and give analysis on the history of judges’ verdicts in order to predict how a judge might rule.
In India, start-ups such as NearLaw.com are providing AI-enabled case-law research tools, driven by summarization algorithms and machine-learning, to analyse the possible outcome of cases. Legal repositories with original content, such as kanoon.nearlaw.com, are also popular. Such tools help to provide lawyers with information about which cases are better suited to be cited in court, and also provide analytics on how precedent-setting cases are interrelated.
AI’s contribution to productivity: A boon or bane?
A common belief that many lawyers and law firms have is that AI/ML is a threat to their existence, or put simply, that AI is going to replace lawyers. However, the evidence from other industries such as eCommerce is that AI/ML will only enable lawyers and law firms to do more with less and to become much more productive. An associate or partner with a law firm who spends roughly 30-40 per cent of her time on non-client activities would, with the use of NLP/AI systems, spend only 5-10 per cent of her time in non-client activities. This advantage would deliver cost savings of roughly 25-30 per cent for law firms.
The best scenario is that the use of NLP technology would start from the Bar (the lawyers) and eventually extend to the Bench (the judges). While AI/ML are tools, the discretion, experience and knowledge of the human mind will still be essential in adjudicating disputes. However, the question is not whether AI/ML will replace professionals in a wide range of industries rather it is how are we going to use AI/ML to make ourselves more productive at work.
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