Algorithmic Bias: Identification of Algorithmic Bias, Its Interference in Corporate Governance, and Board‑Level Remedies – In Indian Boards

Algorithmic decision-making (ADM) systems increasingly shape Corporate Governance processes, from hiring and performance evaluation to Financial Risk Management. The rapid adoption of ADM systems in Indian Corporations introduces new risks of bias, potentially undermining principles of fairness, compliance, accountability, transparency and stakeholder trust. While these systems are efficient and data-driven, they can embed and perpetuate systemic biases. Such biases threaten to undermine corporate decision-making, distort risk assessments, and expose boards to regulatory and reputational risks. This paper undertakes a comprehensive exploration and analysis of algorithmic bias in Indian corporate settings and its potential interference with board-level responsibilities. The paper analyses a comprehensive study and analysis of the challenges and limitations through research methodology principles. It also proposes a robust illustrative Governance framework including Algorithmic Impact Assessments and Independent Audits, and proposes a multi-layered governance-oriented control framework to detect, mitigate, and manage bias in Indian Corporate Boards. The paper suggests a Data, Technology & intelligence driven possible prescriptive options and solutions.