Artificial Intelligence To Help Deliver Justice In India

AI and ML use is present in many sectors like healthcare, machines manufacturing, service sector, etc. The effectiveness of these technologies for the eCourts project is expected to increase the efficiency of the current justice delivery process. The government of India said that while implementing Phase 2 of the eCourts projects, under operation since 2015, a need was felt to adopt new, cutting-edge technologies of Machine Learning (ML) and Artificial Intelligence (AI) to increase efficiency of the justice delivery system.

Artificial Intelligence To Help Deliver Justice In India

Key-Points

To explore the use of AI in the judicial domain, the Supreme Court of India has constituted the Artificial Intelligence Committee which has mainly identified the application of AI technology in the translation of judicial documents, legal research assistance, and process automation.

Several law firms are now keen on trying out new technologies for a quick reference on judicial precedents and pronouncements on cases with similar legal issues at stake.

The Mumbai-based Riverus, a “legal tech” firm, has developed ML applications that peruse troves of cases, “understand” them, and parse cases that are similar in content — very much like a human expert would do — in a fraction of the time.

Present status in India

Over the course of the COVID-19 pandemic, the use of technology for e-filing, and virtual hearings has seen a dramatic rise.

From the beginning of the lockdown in 2020 until January 8 this year, the Supreme Court of India emerged as a global leader by conducting 1,81,909 virtual hearings.

But the use of ML in India’s legal sphere has so far been restricted to automating back-end work and is still a very long way from being used as a decision-making tool for the judiciary.

SUVAS is a language-learning application being used to translate judgments, and SUPACE, which can draft a legal brief, comprises the initiatives being undertaken in the Indian judiciary as a part of incorporating ML-based applications.

Digital Justice Delivery System

The e-Committee of the Supreme Court of India recently released its draft vision document for Phase III of the e-Courts project. Phases I and II had dealt with the digitization of the judiciary, i.e., e-filing, tracking cases online, uploading judgments online, etc. This has helped in easing justice delivery procedures. However, Phase III aims to go beyond simple digitization to incorporate the use of artificial intelligence (AI) and blockchain technology in the realm of law.

However, many expect AI to replace humans entirely when it comes to justice delivery in the future. A lot of research is happening in this area, but currently, there isn’t enough evidence that would point at one clear winner when it comes to AI replacing lawyers or judges. Here are some pros and cons:

Pros:

  1. It is faster and more efficient than humans. This will help in clearing backlogs and quickening processes.
  2. It can maintain an audit trail easily and keep copious notes during legal discussions and arguments.
  3. It is not susceptible to fatigue like humans are over long periods of time.
  4. There is no scope for bias or arrival at an incorrect conclusion due to personal beliefs because AI does not have emotions like humans do.

Cons:

  1. It can lead to false accusations and malicious trials, as it will be harder to prove innocence in absence of physical evidence.
  2. Lack of physical evidence could hamper effective and credible investigations.
  3. As a number of people without any legal knowledge will be able to launch a trial, it may give rise to frivolous cases.
  4. It may not be easy for everyone to access the internet (and in turn, the justice delivery system). There may be some technical glitches that could hinder access.
  5. Not everyone is comfortable with using technology or using it for legal purposes. It may take time for the people to adapt this system.

What is AI?

Artificial intelligence (AI) is a broad term that covers everything from robotic process automation (i.e., making a computer perform a task without human intervention) to advanced algorithms designed to mimic human decision-making.

The goals of Artificial Intelligence (AI) research include reasoning, knowledge representation, planning, learning, natural language processing (communication), perception, and the ability to move and manipulate objects.

General intelligence is still among the field’s long-term goals. Currently, popular approaches include statistical methods, computational intelligence and traditional symbolic AI.

Many tools are used in AI, including versions of search and mathematical optimization, logic, methods based on probability and economics. The AI field draws upon computer science, information engineering, mathematics, psychology, linguistics, philosophy, etc.

What is ML?

Machine learning is a field of artificial intelligence that uses statistical techniques to give computer systems the ability to “learn” (e.g., progressively improve performance on a specific task) with data, without being explicitly programmed. The name machine learning was coined in 1959 by Arthur Samuel.

Machine learning tasks are typically classified into three broad categories, depending on the nature of the learning “signal” or “feedback” available to a learning system:

Supervised learning: The computer is presented with example inputs and their desired outputs, given by a “teacher”, and the goal is to learn a general rule that maps inputs to outputs.

Unsupervised learning: No labels are given to the learning algorithm, leaving it on its own to find structure in its input. Unsupervised learning can be a goal in itself (discovering hidden patterns in data) or a means towards an end (feature learning).

Reinforcement learning: A computer program interacts with a dynamic environment in which it must perform a certain goal (such as driving a vehicle or playing a game against an opponent). The program is provided feedback in terms of rewards and punishments as it navigates its problem space.

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