Algorithmic Bias, Economic Efficiency, and Social Welfare

Algorithmic efficiency is essential for the thriving of digital platforms and the AI-driven economy. However, with the growing public concerns about the detrimental and distributional impacts of algorithmic bias, there is an urgent need of building a regulatory framework under which information and AI technology is used for social good. In this proposed project, the team combines economic analysis, statistical methods, and computer science tools to analyze the economic and social impact of algorithmic decision-making and design social-welfare- enhancing algorithms in a wide range of economic and societal settings. Under this overarching goal, the team aims to achieve the intellectual, policy, and educational objectives through four work packages: (1) social media addiction and cognitive bias induced by recommendation algorithms, (2) decision bias in AI-human interactions, (3) the impact of platform promotion on grassroots innovation and consumer welfare, and (4) algorithmic design with social goals.