According to a report by Tata Consultancy Services, 68% of Indian companies use artificial intelligence (AI) for IT functions, but 70% believe AI’s greatest impact will be in functions outside of IT such as marketing, customer service, finance and HR by 2020. Also, the majority of companies see AI as transformative and consider it crucial to remaining competitive in future. The primary goal of all AI-enabled innovation is to minimise human labour and augment human capability to the maximum extent possible.
Machine learning (ML), a subset of AI, has been growing since the last 20 years but has hit an inflection point and evolved into the intersection of AI techniques and business intelligence (BI) analytics. Primarily due to acceleration in hardware and software capabilities, AI systems can now learn faster, predict with more accuracy and perform tasks that they haven’t tried yet.
Tech giant Google has bet its next wave of innovation to come in the AI space with Sundar Pichai unveiling the company’s new AI computer chip, TensorFlow processing unit, a GPU-accelerated chip with pre-loaded AI-software that can be used for a variety of AI and ML applications. TensorFlow applications range from information retrieval or search to summarisation, image and sentiment classification and predictive analytics.
AI and ML is a natural solution for any knowledge-driven industry wherein a large amount of new data is repeatedly produced. New data requires standardisation, classification, summarisation and storage, all of which are tasks best suited for AI/ML implementation. The legal field exhibits all of these salient features and is therefore a prime contender for an industry that will be going through a transformative change through the application and use of AI.
AI for judiciary
As per the National Judicial Data Grid, over 2.6 cases are pending across local, district and high courts and the Supreme Court and close to 9% of these cases have been pending for 10 years or more. On an average, 30,000 cases are filed everyday and roughly 28,000 cases are adjudicated daily. This means that there is a shortfall of 2,000 cases that are undecided, leading to a backlog of 7.3 lakh cases being added to the total cumulative backlog every year.
The backlog of cases falls within the purview of the administrative function of the judiciary. The solution to this seemingly perennial problem also involves an exponential increase in executive funding for judicial infrastructure and court expansion. For retaining the faith and promise of justice, it is imperative that the executive branch and judiciary’s administrative branch act in good faith to provide legal resolution to the cases, especially the ones pending for more than 5-10 years.
The applications of AI for the judiciary fall within three categories:
1. Prioritisation of pending cases: A criminal trial for heinous crimes would naturally assume significance over civil trials involving monetary claims below a certain threshold. Matters of security or state significance might be more significant to personal disputes. Each prioritisation rule could be a simple hyper-parameter in the ML model of optimisation. Each judge’s day would consist of only the most important and urgent matters at hand, and the time of the entire judiciary would be better used and properly prioritised.
2. Summarisation of plaints, affidavits, etc: ML has evolved substantially enabling more accurate summarisation. In 2016, Google announced the use of its AI tool library—TensorFlow—to summarise the news headlines for its news product. Though the feature was announced to be accurate for shorter text, research in TensorFlow is now open-sourced and is ready to tackle the challenge of summarising court documents.
3. Case-law research: Judges in common law jurisdictions (India, UK, Canada, US, etc.) use the decisions of high courts/Supreme Court in past cases of identical or similar circumstances as binding precedent in their own decisions. As a rule of judicial responsibility, judges must follow the binding decisions of superior or the same court.
AI for lawyers, law firms
AI has immediate application for legal practitioners and judges in three aspects:
1. Legal research: Legal research is an essential service for the smooth functioning of the legal services market, which was worth $6.1 billion in 2011-12. Lawyers, while arguing cases, need to delve deep into legal research of hundreds of relevant cases and peruse thousands of pages of decisions to deduce the right cases that are in favour of their client’s motion or application. Conversely, lawyers also need to know the opposing view and the supporting case-law justification, so they can prepare a defensive mitigation strategy.
2. Predictive analytics and visualisation: AI and machine learning-based platforms in consumer internet products such as smart assistants (Alexa, Siri, Ello, etc.) are slowly taking over traditional and static-digital modes of engaging consumers. Even top legal firms such as Cyril Amarchand Mangaldas are now leveraging the power of AI for contract analysis and review.
The startup scene in the legal space started to heat up with US investors turning their attention to startups such as RavelLaw and RossIntelligence. The new-age legal research startups in the US are leveraging analytics to visualise data and ascertain if they lead to a positive or adverse verdict. Other predictive insights from the data include the probability of winning the case, sentiment analysis of judgment text to find the historical reasoning of judges and the identical or similar judgments on that topic.
Nearer to home, in India, startups such as NearLaw.com are providing AI-enabled case-law research tools driven by summarisation algorithms coupled with machine learning to rank the cases using CaseRanking. Such tools help to inform lawyers on which cases are better suited to be cited in the courts over others and also provide analytics on how the network of cases are inter-related. ‘Lawyer knowledge’ repositories with original content such as NearLaw.com are also popular.
3. AI law office management: Particularly for law firms, the concept of an AI assistant to gather standardised requirements from clients is a scalable idea. The rise of robo-advisers (or automated bots) advising high-net-worth individuals and ultra-HNIs has shaken up the European wealth management industry, once considered conservative and old-fashioned. If wealthy Europeans would entrust their monetary investment decisions to an automated bot, surely legal AI-enabled chatbots are not a far-fetched idea, though more research on attitudes and client preferences needs to be conducted.
Most administrative operations of law firms such as payroll management, resource management, meeting schedules and client billing are akin to those of companies from other industries. The fact that 54% of manager time was spent in administrative/coordination and control tasks, is a huge motivation for management to invest in AI to automate such tasks.
Boon or bane?
My hope is that the use of NLP/AI would start from what is traditionally known as the ‘bar’ (lawyers) and then extend itself to the ‘bench’ (judges) wherein even judges could use the power of NLP summarisation to gather the sum of the contentions of both sides, the appellant (petitioner) and the defendant (respondent).
Besides spending less time on legal research and more time with clients, lawyers and law firms could present arguments and offer evidence digitally, get it processed, validated and submitted faster. Judges could quickly deduce which part contains merit as per the acts/statutes and the latest case-law on the subject of law pertaining to the dispute. While AI/NLP would be tools, the discretion, experience and knowledge of the human mind would be essential in adjudicating disputes; so judges would remain an integral part of the system.
The common misplaced notion that many legal industry executives, lawyers and law firms have is that AI or ML is a threat to their existence, or put simply, that AI is going to replace lawyers. The evidence, from other industries and verticals such as ecommerce, healthcare and accounting is that AI/ML will only enable judges, lawyers and law firms to do more with less, to become way more productive than their predecessors.
Lawyers, law firm partners and associates would do well to view AI as a kind of super-smart colleague who is there to help them focus on higher-order tasks requiring creative skill and fine judgement while relegating the repetitive and standardised tasks to the AI machine.
That said, a caution to be heeded: the rapid pace of AI’s acceleration is faster than most legal professionals realise or fully comprehend. Those lawyers, firms and professionals who assess the situation and plan for hiring and training the right skill-set of future lawyers and professionals will be much better prepared for the AI age.
Vikas Sahita is founder of NearLaw.com. Views are personal.
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