The AI Democratization Paradox: Evidence from Decentralized Knowledge Communities

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—three to four times larger than mid-tier editions. Whereas editors actively leveraged AI to address representation gaps, translating female biographies at twice the expected rate, structural constraints still 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. This paper was accepted by Anindya Ghose, information systems. Funding: This work was supported by the Wikimedia Research & Technology Fund and European Union – NextGenerationEU funds, Component M4.C2, Investment 1.1, PRIN 2022 PNRR, CUP: J53D23015440001. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2024.04717 .

• 저널Management Science
• 토픽Wikis in Education and Collaboration · Communication
• DOI10.1287/mnsc.2024.04717
• 저자Kai Zhu, D. Walker