Sibadutta Dash,

Assistant Professor of Law, Bhubaneswar Law College, Bhubaneswar, Odisha

Dillip Kumar Mahapatra,

Principal, Bhubaneswar Law College, Bhubaneswar, Odisha

DoI: CJBES/ Vol.2.1/ June-2025…

Abstract:        

The rapid advancement of Artificial Intelligence (AI) and Machine Learning (ML) technologies presents a unique opportunity to address the significant challenges facing India’s legal system, including large case backlogs, judicial delays, and a shortage of legal professionals. This paper examines how AI and ML can enhance legal research, decision-making, and judicial efficiency in India. AI and ML can automate routine legal tasks such as document analysis, case law retrieval, and legal research, enabling legal professionals to process vast amounts of data more efficiently. These technologies assist judges in evaluating evidence, analyzing precedents, and predicting case outcomes, reducing administrative burdens and allowing for more complex legal analysis. Predictive analytics, a key application of AI, can forecast case outcomes, aiding judges in making informed and unbiased decisions. AI also promotes transparency by identifying trends and biases in judicial decisions. However, ethical concerns, particularly around algorithmic bias, must be addressed to ensure fairness, accountability, and transparency in AI-driven decisions. Establishing clear ethical guidelines and regulatory frameworks is crucial to the responsible use of AI in the legal domain. Additionally, improving digital infrastructure across India, particularly in rural areas, is necessary to ensure equitable access to AI tools in the judiciary. In conclusion, AI and ML hold immense potential to revolutionize India’s legal system by improving efficiency, reducing case backlogs, and supporting fairer, more transparent decision-making. With responsible adoption, these technologies can play a transformative role in shaping the future of legal practice in India.

Keywords:

Artificial Intelligence (AI); Machine Learning (ML); Judicial Efficiency; Predictive Analytics