{"id":357796,"date":"2026-07-10T15:50:49","date_gmt":"2026-07-10T15:50:49","guid":{"rendered":"https:\/\/peraltafinancing.com\/uncategorized\/ai-in-the-indian-judiciary-augmenting-justice-without-automating-judgement\/"},"modified":"2026-07-10T15:50:49","modified_gmt":"2026-07-10T15:50:49","slug":"ai-in-the-indian-judiciary-augmenting-justice-without-automating-judgement","status":"publish","type":"post","link":"https:\/\/fivemor.com\/?p=357796","title":{"rendered":"AI In The Indian Judiciary: Augmenting Justice Without Automating Judgement"},"content":{"rendered":"


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The Indian judiciary is suffering from a backlog of 4.9 crore cases with 13 lakh cases still in the pre-litigation stage<\/a>. The judiciary is plagued by inefficiencies such as procedural delays, low judge to population ratio, legal complexity of cases and absence of prescribed timelines for case disposals. Artificial Intelligence (\u201cAI\u201d) has entered the courtrooms with a familiar promise of speed, efficiency and relief to reduce the backlog.<\/p>\n

AI tools promise to automate case management, translation, scheduling of hearings, filing court documents and assist judges in decision-making through predictive analytics. This would give judges more time to their responsibility of adjudicating disputes. However, though AI tools offer speed and efficiency the core reason for the backlog remains unaddressed. India has 15 judges per 10 lakh people which is well below the recommended 50 judges per 10 lakh people<\/a> by the 1987 law commission.<\/p>\n

The central thesis of this article is that AI tools would only be used in the capacity of assistants and not in a decision-making capacity. This decision by the Indian judiciary is not only one that is made in the interests of efficiency but also an institutional choice to preserve legitimacy, accountability and judicial discretion. Furthermore, the article analyzes the regulation of AI in the judiciary with a focus on the recent guidelines issued by the Kerela High Court.<\/p>\n

\"AI<\/p>\n

AI in the Indian Judiciary<\/span> \u00a0<\/strong><\/h4>\n

AI in the judiciary has the potential to transform case management, legal research, translation of documents, hearing appointments and filing court documents. There are 3 key technologies that are being used in the Indian judiciary:<\/p>\n

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  1. Machine Learning (\u201cML\u201d)<\/strong> \u2013 <\/strong>ML is defined as a subset of AI that uses pattern matching based on its training data to make inferences on new data. This allows LLMs to make decisions without explicit instructions. ML is usually used to build tools for tasks like legal research, drafting and predictive analytics.<\/li>\n
  2. Natural Language Processing (\u201cNLP\u201d) \u2013 <\/strong>NLP is a subset of AI that uses ML to allow LLM models to understand and communicate in human language. NLP allows lawyers to prompt an LLM model for extracting case law, assisting in drafting and summarizing jurisprudence.<\/li>\n
  3. Optical Character Recognition (\u201cOCR\u201d) \u2013<\/strong> OCR allows the digitization of physical documents. In India, courts often have to translate document such as affidavits, bail petitions and witness transcripts into English. OCR technology is used for digitization and translation documents from district courts.<\/li>\n<\/ol>\n

    AI is being integrated into the judiciary as part of the e-courts project\u2019s Phase III. The Indian government has allocated a budget of Rs 7,210 crores with Rs 53.57 crores<\/a> earmarked for integration of future technological advancements such as AI and Blockchain across high courts up to 2027.<\/p>\n

    The Supreme Court of India has already started utilizing tools such as:<\/h4>\n