Governance of Artificial Intelligence

This work critically explores the governance of artificial intelligence and data through legal, ethical, and democratic lenses. It examines data protection, algorithmic bias, and emerging governance models while addressing the impact of AI on democracy, corporate power, and global regulation. Examines the challenges and opportunities of governing AI, with a focus on law, ethics, democracy, and global governance. The book outlines the legal foundations that underpin responsible data practices, including data protection frameworks, informed consent, and the principle of purpose limitation. It highlights the risks of algorithmic bias, representational harms, and systemic inequities that arise from flawed datasets. Different governance models—such as data trusts, data commons, and cooperatives—are explored as mechanisms to balance ownership and stewardship of data. The book also reviews cross-border data flows and localization mandates, reflecting the tension between digital sovereignty and global digital trade. It will interrogate the intersection of technological power and democratic values. It examines how surveillance capitalism erodes civil liberties by commodifying personal information and shaping behavior. Special attention is paid to the deployment of AI in law enforcement, the judiciary, and policymaking, raising concerns over fairness, accountability, and due process. Threats to deliberative democracy, including misinformation campaigns and microtargeted political advertising, are analyzed alongside the pressing need for algorithmic transparency to safeguard democratic accountability. Moving forward with the concept of corporate power and platform responsibility, the study explores the concentration of AI capabilities within Big Tech firms, whose dominance raises structural concerns for competition and democracy. The fiduciary responsibilities of platforms, their role in algorithmic governance, and the politics of content moderation are critically assessed.