Innovation Policy Pluralism in Practice: A Critical Review and Reflection on Current Incentive Policies in the AI Industry

Abstract

The rapid development of artificial intelligence (AI) has prompted the need for effective innovation policies to support and guide the industry's growth. This study aims to critically review and reflect on the current landscape of innovation incentive policies in the AI industry, focusing on the concept of innovation policy pluralism and its implications for balancing incentives, addressing ethical concerns, and fostering international cooperation. The study employs a method of literature review and case analysis, and a comparative analysis of AI innovation policies across countries is conducted, along with case studies of successful and failed policy initiatives. The study also explores emerging trends and best practices in AI innovation policy design. The study underscores the importance of optimizing innovation policy pluralism in the AI industry, leveraging potential synergies between different incentive mechanisms, and developing adaptive and responsive policies. The findings have significant implications for policymakers, researchers, and AI industry stakeholders, emphasizing the need for strategic and collaborative approaches to foster sustainable and equitable AI innovation.