The AI Democratization Paradox: Large-scale Evidence from Decentralized Knowledge Platforms
COM3 Level 1
SR12, COM3 01-21
Abstract:
Does AI democratize knowledge production or amplify existing disparities? We investigate this tension by studying the deployment of neural machine translation across more than 100 Wikipedia language communities. Leveraging rich, fine-grained data and exogenous variation from a natural experiment, we uncover the "AI Democratization Paradox," where the technology simultaneously drives democratizing and concentrating forces. AI lowered barriers, leading to a substantial increase in content creation across diverse target languages without sacrificing quality or readership. However, the benefits were concentrated: well-resourced communities captured disproportionate gains—3-4 times larger than mid-tier editions. While editors actively leveraged AI to address representation gaps, translating female biographies at twice the expected rate, structural constraints limited the impact in high-need areas. We conclude that technological solutions alone cannot overcome structural inequalities; AI's distributional impact is contingent on the interplay between technological capabilities and existing social structures.
Bio:
Kai Zhu is an Assistant Professor at Bocconi University in Milan, Italy. He earned his PhD in Information Systems from Boston University and his MA in Economics from Indiana University. His research examines the economic and societal implications of data-driven technologies, with a particular focus on how digital platforms, large-scale information ecosystems, and artificial intelligence reshape market structure, productivity dynamics, inequality, and regulatory frameworks. His work has been published in leading journals such as Information Systems Research, Production and Operations Management, and Management Science.