An Empirical Study of the Contemporary Use and the Applicability of Artificial Intelligence in Judicial Systems

1. An Empirical Study of the
Contemporary Use and the Applicability
of Artificial Intelligence in Judicial
Systems
Artificial intelligence in the judicial
systems by select countries and also to
explore the applicability of Artificial
intelligence in the judicial system, so
that the legal fraternity as well as other
stakeholders could be benefitted, in the
process of seeking justice.

2. What is the Need of Technology in Judiciary?
• Pendency of Cases: The recent National
Judicial Data Grid (NJDG) shows that
3,89,41,148 cases are pending at the District
and Taluka levels and 58,43,113 are still
unresolved at the high courts.
• Such pendency has a spin-off
effect that takes a toll on the efficiency
of the judiciary, and ultimately
reduces peoples’ access to justice.

3. What are Examples of Use of Technology in Judiciary?
•Virtual Hearing: Over the course of
the Covid-19 pandemic, the use of
technology for e-filing, and virtual hearings
has seen a dramatic rise.
•SUVAS (Supreme Court Vidhik Anuvaad
Software): It is an AI system that can assist
in the translation of judgments into regional
languages.
• This is another landmark effort to
increase access to justice.

4. What are Examples of Use of Technology in Judiciary?
eCourts:
• It was conceptualized with a vision to
transform the Indian Judiciary by
ICT (Information and Communication
Technology) enablement of Courts.
• It is a pan-India Project, monitored and
funded by the Department of Justice,
Ministry of Law and Justice, for the
District Courts across the country.
eCourts portal

5. Use and applicability of artificial intelligence (AI) in
judicial systems
Some key points on how AI is transforming the judicial system:
• Legal Research: AI-powered tools can quickly and accurately
analyse vast amounts of legal data to assist judges and lawyers in
their research.
• Document Review: AI algorithms can analyse and classify legal
documents, such as contracts and briefs, to help lawyers identify
relevant information and potential risks.
• Predictive Analytics: Machine learning algorithms can analyse
past court cases and predict the likelihood of a certain outcome,
which can assist lawyers in their decision-making process.
• Sentiment Analysis: AI tools can analyse the sentiment of court
transcripts and predict the emotional state of jurors or witnesses,
which can provide valuable insights to lawyers.

6. The use and applicability of artificial intelligence (AI) in
judicial systems
• Case Management: AI-powered systems can manage and
organize court cases, including scheduling, case status updates,
and communication with parties involved.
• Access to Justice: AI can help increase access to justice by
providing low-cost or free legal assistance to people who cannot
afford traditional legal services.
• Bias Reduction: AI tools can help reduce bias in the judicial
system by removing human prejudices and ensuring that decisions
are based on objective data.
• Security: AI-powered systems can improve the security of court
documents and prevent unauthorized access or alteration.

7. • Increased efficiency: AI can automate time-consuming tasks, such as
legal research and document analysis, saving lawyers and judges time and
effort.
• Improved accuracy: AI can analyse vast amounts of data and identify
patterns that humans may miss, leading to more accurate predictions and
decisions.
• Enhanced fairness: AI can remove human biases from decision making,
leading to more objective and fair outcomes.

8. • Lack of transparency: The decision-making process of AI can be
difficult to understand and may not be transparent, leading to concerns
about accountability and fairness.
• Limited context awareness: AI systems may not be able to fully
understand the nuances of legal proceedings, which could lead to incorrect
predictions or decisions.
• Ethical concerns: There are ethical concerns about the use of AI in legal
proceedings, such as privacy and data protection.

9. • Research design: The study used a mixed-methods approach, including
surveys and interviews.
• Data collection methods: Data was collected from lawyers, judges, and
legal professionals who have experience with AI in judicial systems.
• Sampling techniques: The sample size was 100 legal professionals from
different parts of the world.