Automated Decision-Making and the Rule of Algorithm: Challenges to Accountability in Indian Governance

The increasing use of automated decision-making (ADM) systems in public governance marks a shift in how administrative and judicial decisions are made and implemented. ADM systems offer unmatched efficiency and consistency in handling large volumes of data, but they also raise concerns regarding transparency, accountability, and fairness. The opaque nature of algorithmic processes, often described as the “black box” problem, creates a “deficient scrutiny problem,” making it difficult to understand, challenge, or review decisions that affect individual rights and liabilities. This raises another issue of a transition from the “rule of law” to a “rule of algorithm,” where decision-making is shaped by systems that are not easily understandable or challengeable. This paper examines the rift between ADM and constitutional principles, particularly non-arbitrariness, speaking order, and procedural fairness. It also highlights the risks of algorithmic bias in the Indian context, including concerns of “digital casteism,” where systems trained on historical data may reproduce existing social inequalities. The paper analyses India’s response through the DPDP Act, 2023, the DPDP Rules, 2025, the Reserve Bank of India’s approach to responsible AI, and the India AI Governance Guidelines. While these developments indicate a move towards integrating legal and technical safeguards, they remain insufficient to address the challenges posed by ADM. The paper proposes a techno-legal approach combining legal standards with design architecture, continuous auditing, and institutional oversight. It concludes that the legitimacy of ADM in governance depends on the development of effective mechanisms for scrutiny and accountability.