From Dharma to Data

From Dharma to Data: Judicial Impact Assessment and the Constitutional Morality of Justice in Post-Digital India Can justice be truly delivered if the impact of the laws we enact is never measured? From Dharma to Data addresses this fundamental question by exploring Judicial Impact Assessment (JIA) as a missing link between law-making and effective justice delivery in India. Moving beyond the conventional understanding of judicial reform as a question merely of increasing courts, judges or technology, the book argues for an evidence-based system capable of anticipating and evaluating the consequences that legislation, procedural reforms and technological interventions impose upon the justice-delivery system. The work is distinctive in bringing together two seemingly different worlds: India's civilizational philosophy of dharma, nyāya and yukti, and the contemporary requirements of constitutional morality, empirical governance and data-driven judicial administration. It demonstrates that reasoned and measurable justice is not alien to Indian jurisprudence; rather, it represents a modern institutional expression of an enduring Indian legal philosophy. Against the backdrop of enormous judicial pendency, increasing legislative complexity and the rapid digitisation of courts, the book examines comparative international approaches to impact assessment and considers their relevance for India. It further explores the constitutional foundations of JIA through Articles 14, 21 and 39-A, access to justice, human dignity, equality and institutional accountability. Going beyond diagnosis, the authors propose an institutional roadmap, including a Judicial Impact Assessment framework and a National Judicial Impact Assessment Authority capable of integrating legislative foresight, judicial data, budgeting, manpower planning and technological evaluation. At its core, From Dharma to Data makes a simple yet powerful proposition: justice cannot remain merely a constitutional promise; its institutional consequences must also be anticipated, measured and continuously improved.