Artificial Intelligence and the Stakeholder Governance Paradigm: A Comparative Analysis of Corporate Accountability Frameworks in the Era of Algorithmic Decision-Making

Abstract

The rapid integration of artificial intelligence (AI) systems into corporate decision-making processes presents unprecedented challenges to traditional corporate governance frameworks. This article examines the intersection of AI governance and stakeholder capitalism across multiple jurisdictions, analyzing how different legal systems are adapting corporate accountability mechanisms to address algorithmic decision-making. Through a comparative analysis of regulatory approaches in the European Union, United States, United Kingdom, and India, this study reveals significant divergences in how jurisdictions balance technological innovation with stakeholder protection. The research demonstrates that jurisdictions with established stakeholder-oriented governance models are better positioned to integrate AI accountability frameworks, while shareholder primacy regimes face structural impediments in allocating responsibility for AI-driven decisions. This article proposes a hybrid governance model that reconciles technological capabilities with fiduciary duties, advocating for mandatory AI impact assessments, algorithmic transparency requirements, and expanded director liability frameworks. The findings contribute to ongoing debates about corporate purpose in the digital age and provide practical recommendations for policymakers seeking to develop effective AI governance regimes that protect diverse stakeholder interests while fostering innovation.