Can Algorithms Improve Judicial Infrastructure Allocation? A Justice-Based Framework for India

Abstract

Currently, India has more than 56 million pending cases and despite the constitutional guarantees under Articles 14 and 21, systemic judicial deficiencies, such as lack of courtrooms, judges, staff and limited digital capacity, continue to be a major cause of case pendency and unequal access to justice.1 In India, the number of judges per million population is now about 20, which is below the 50 per million suggested by the Law Commission.2 This is because the current allocation practice in the country is based on demographic projections, budgetary ceilings, and administrative discretion, thus a reactive rather than predictive planning model. A lot of research is dedicated to artificial intelligence for decision making and unlike predictive policing, there has been little research on algorithmic techniques for organizing the institutional framework of justice delivery.3 This study addresses that gap. Instead of prescribing the outcomes of judicial architecture, it argues that algorithmic systems should be constitutionally constrained by decision-support tools that augment human authorities rather than substitute for them. To operationalise this argument, the Justice-Based Judicial Infrastructure Allocation Framework (JJIAF), an innovative weighted-indicator model, is developed which generates a district-level Judicial Need Score from case pendency (25%), population (20%), accessibility (15%), rural need (15%), judicial vacancies (10%), digital infrastructure (10%) and disaster risk (5%).4 The framework is benchmarked against practices in the US, UK, Singapore and Kenya where data-driven court administration has enhanced efficiency without compromising judicial control.5 It is also tested against the constitutional values of equality, access to justice, and natural justice under Articles 14, 21 and 39A.6 The report concludes with six policy recommendations, including a National Judicial Infrastructure Dashboard, mandated explainability, and independent bias audits, to ensure that algorithmic allocation strengthens, rather than undermines, the constitutional oversight of the Indian judiciary.