A Critical Study On Corporate Accountability For Algorithmic Decision-Making In The Healthcare Sector In India: Legal Challenges And Regulatory Perspectives
- IJLLR Journal
- 3 hours ago
- 2 min read
Harshitha. R & Dr. Jyotirmoy Banerjee
ABSTRACT
The increasing integration of Artificial Intelligence (AI) and algorithmic decision-making systems into India’s healthcare sector has significantly transformed diagnostic accuracy, treatment planning, disease prediction, patient monitoring, and healthcare administration. The launch of the Ayushman Bharat Digital Mission (ABDM) in 2021 accelerated the digitization of healthcare services, with over 780 million Ayushman Bharat Health Accounts (ABHA IDs) and more than 550 million digital health records generated by 2025. Simultaneously, the Indian healthcare AI market, valued at approximately USD 950 million in 2023, is projected to exceed USD 6 billion by 2032, reflecting the growing adoption of AI-enabled technologies across hospitals, telemedicine platforms, diagnostic centres, and health-tech corporations. While these advancements promise greater efficiency, accessibility, and personalized healthcare, they also raise serious concerns regarding corporate accountability when algorithmic systems produce biased, opaque, inaccurate, or harmful outcomes affecting patient welfare. This dissertation critically examines the legal and regulatory challenges associated with corporate accountability for algorithmic decision- making in India’s healthcare ecosystem. It investigates issues relating to liability attribution among AI developers, healthcare providers, and corporate entities; algorithmic opacity arising from proprietary “black box” models; discriminatory outcomes affecting vulnerable populations; inadequate informed consent mechanisms; and the increasing commodification of patient data. The study evaluates the adequacy of India’s existing legal framework, including the IT Act, 2000, the DPDP Act, 2023, consumer protection laws, and medical ethics regulations, highlighting their limitations in addressing algorithmic harms and emerging healthcare AI risks. Through doctrinal and comparative analysis of regulatory developments such as the European Union Artificial Intelligence Act, 2024, and the United States’ sector-specific governance approach, the research identifies global best practices for AI accountability and risk management. The study further explores contemporary trends including algorithmic impact assessments, explainable AI, independent auditing mechanisms, and enhanced corporate governance obligations. It proposes a comprehensive regulatory framework incorporating mandatory transparency requirements, differentiated liability standards, board-level AI oversight, and strict accountability mechanisms for high-risk healthcare AI applications. By addressing the intersection of technology, healthcare, and law, the dissertation contributes to the evolving discourse on AI governance and seeks to balance innovation with patient safety, ethical responsibility, and corporate accountability in India’s rapidly expanding digital health landscape.
Keywords: Corporate accountability, algorithmic decision-making, AI in healthcare, medical liability, regulatory framework, algorithmic bias, India, digital health governance, AI accountability, corporate governance.
