THE EDUCATORS' PEN

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  CBS LINE

Volume 4(9)

AI in Indian Judiciary: Prospectus and Challenges

Akhil Shaji
HSST Political Science, St. John Nephumsians HSS Kozhuvanal
Ph No: 8078172520

The Indian justice system is currently undergoing the crisis of huge backlog in terms of disposal of cases both criminal and civil in nature in all levels of courts that is from Supreme Court to lower courts. In the top court, The Supreme Court of India has over 83,312 cases, of which 10,094 cases has been pending for 10 years, 558 cases for 20 years and 26 cases for 30 years. Roughly 80% of cases are civil in nature while around 21% of the cases are criminal in nature. The condition of high courts and lower courts are much worse with over 5.8 million cases to be disposed. Some experts argued that, even after 100 years the backlog faced by Indian Judiciary in all levels cannot be solved. So in this context the entry of Artificial Intelligence becomes an interesting solution to completely clear the backlog in the system. 

Deep learning, natural language processing, and neural networks are just a few of the many components that make up artificial intelligence. Face recognition software on Android smartphones frequently uses deep learning, a subfield of artificial intelligence that deals with data representation in a hierarchy employing many levels of neural networks. The capacity to read, comprehends, interpret, and generate human languages is referred to as natural language processing (NLP), and it is employed in voice control and smartphone command prompts.  Neural networks, on the other hand, are based on neural connections seen in the human brain that function as a systematic model. Algorithms used in artificial intelligence enable a machine to think, learn, and solve problems in a manner like to that of the human brain. 

China has implemented Artificial Intelligence in its legal system through the Smart Court Navigation System, which aids judges in managing caseloads by transcribing documents, reviewing evidence, and suggesting recommendations based on past cases. Brazil's Supreme Federal Court introduced MarlA, an AI system that summarizes lawsuits and generates procedural reports for judges. Additionally, Buenos Aires developed Prometea AI, capable of making decisions in 20 seconds with 96% accuracy and translating documents into multiple languages. The USA utilizes the COMPAS system to assess the risk of reoffending among criminals. In India, for translating English legal documents into nine regional languages, the Supreme Court developed an AI-based system named SUVAS, along with a multi-lingual app approved by the Supreme Court and National Informatics Centre. The system translates documents into six regional languages in addition to English and Hindi. Additionally, on April 6, 2021, Chief Justice S.A. Bobde launched the SUPACE system, aimed at reducing judges' workload by processing legal information necessary for case hearings. Currently, it is being tested at the Bombay and Delhi High Courts for criminal cases.

Benefits

     ·              Judges can easily locate pertinent legislation, case law summaries, and precedents with the use of SUPACE (Supreme Court Portal for Assistance in Court Efficiency).

    ·       By translating court rulings into regional languages, translation systems such as SUVAS (Supreme Court Vidhik Anuvaad Software) make legal information more accessible to a wider range of people.

      ·          Accurate digital records of witness depositions and court proceedings are produced by AI-driven real-time transcribing systems.  Litigants can structure their filings with the aid of chatbots and digital filling portals.

     ·                Farber (2025) evaluated three AI tools—ChatGPT-4, Claude, and Gemini—in analyzing crime scene photographs compared to ten forensic experts. The AI achieved an average reliability rating of 7.4 out of 10, significantly reducing analysis time to under two minutes versus 42.5 minutes for human experts. The study highlighted the AI tools' effectiveness in observational accuracy (7.6 out of 10) and logical inference (7.4 out of 10).

       Issues

    ·       The predictive policing system, such as COMPAS in the US, faces criticism for racial discrimination, particularly against the black community, which is often labeled as high-risk compared to their white counterparts.

     ·         Additionally, these systems raise concerns about potential biases against minorities and marginalized groups.

      ·            Privacy issues linked to mass surveillance and risks of exploitation for personal gain by political leaders and activists further complicate the situation.

        ·                The emergence of deep fakes adds another layer of complexity, undermining trust in legal proceedings as AI can generate misleading content, making verification of authenticity a challenging and resource-intensive process.

Conclusion

The role of Artificial Intelligence (AI) should be to assist judges, promoting transparency, fairness, and accountability. Its deployment requires clear government parameters to prevent misuse for personal gain. AI must enhance speed, efficiency, accuracy, and accessibility, particularly for marginalized groups in India. Transitioning from backlog to efficiency poses challenges that necessitate safeguards and a commitment to constitutional integrity. Successful AI implementation will rely on decisions by policymakers, judges, technologists, and civil society, as India seeks to wisely leverage AI's potential.

References


Brennan, T., Dieterich, W., & Ehret, B. (2008). Evaluating the predictive validity of the compas risk and needs assessment system. Criminal Justice and Behavior, 36(1), 21–40. https://doi.org/10.1177/0093854808326545

Beruashvili, M. (2025). The Capabilities and Challenges of Artificial Intelligence in the Justice System. Law and World, 11(3), 142–153. https://doi.org/10.36475/11.3.10

Carrasquilla-Díaz, L., De Luque-Pisciotti, A., & Lagos-González, E. (2024). AI Adoption in Colombian Legal Practice: Challenges and Opportunities. Procedia Computer Science, 241(6), 508–513. https://doi.org/10.1016/j.procs.2024.08.072

Dario, L. M., Morrow, W. J., Wooditch, A., & Vickovic, S. G. (2015). The point break effect: an examination of surf, crime, and transitory opportunities. Criminal Justice Studies, 28(3), 257–279. https://doi.org/10.1080/1478601x.2015.1032409

De Sanctis, F. M. (2021). Artificial Intelligence and Innovation in Brazilian Justice. International Annals of Criminology, 59(1), 1–10. https://doi.org/10.1017/cri.2021.4

Farber, S. (2025). AI as a decision support tool in forensic image analysis: A pilot study on integrating large language models into crime scene investigation workflows. Journal of Forensic Sciences, 70(3). https://doi.org/10.1111/1556-4029.70035

Ghosh, M., & Thirugnanam, A. (2021). Introduction to Artificial Intelligence. Studies in Big Data, 23–44. https://doi.org/10.1007/978-981-16-0415-7_2

Jain, P. (2018). Artificial Intelligence for Sustainable and Effective Justice Delivery in India. SSRN, 2018

 

 

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