Community-powered AI: Enhancing regional development through dataset diversity and ethical governance

Zeyu Lin, Hongtao Dou and Shanlang Lin

Technovation, 2025, vol. 147, issue C

Abstract: This study examines the transformative impact of AI on promoting equitable regional development in China, with a particular focus on dataset diversity, community-driven data collection, and ethical governance frameworks. Employing a quantitative cross-sectional methodology, primary survey data from AI developers, policymakers, and regional planners across 31 provinces were integrated with secondary economic and technological indicators. The results reveal that higher AI dataset diversity is strongly associated with reduced income inequality, enhanced GDP growth, and improved access to education and healthcare services. Community-driven data initiatives significantly enhance dataset representativeness, improving the accuracy, fairness, and policy relevance of AI models. Furthermore, adopting ethical AI governance frameworks positively influences public trust, AI adoption rates, perceived fairness, and stakeholder engagement. Structural Equation Modeling validates the interrelationships among dataset diversity, community involvement, ethical governance, and regional development outcomes. This study highlights actionable strategies for the responsible integration of AI, offering a holistic socio-technical model to drive equitable and sustainable regional development in China.

Keywords: Artificial intelligence; Dataset diversity; Regional development; Ethical AI governance; Community-driven data; Public trust; China; Socio-technical systems (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0166497225001476
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:techno:v:147:y:2025:i:c:s0166497225001476

DOI: 10.1016/j.technovation.2025.103315

Access Statistics for this article

Technovation is currently edited by Jonathan Linton

More articles in Technovation from Elsevier
Bibliographic data for series maintained by Catherine Liu ().