Risk Analysis and Countermeasures of Generative Artificial Intelligence in Research Project Management
Year: 2025
Type: article
Abstract: In recent years，the wave of generative artificial intelligence has swept across the globe，profoundly transforming the development trajectory of multiple industries. However，as generative artificial intelligence technology rapidly develops and finds widespread applications，its potential risks and challenges have gradually emerged，particularly in science fund project management. To comprehensively understand the application orientation of generative artificial intelligence in science fund project management，this paper conducts a systematic review of policies from related global institutions and expert viewpoints，providing a basis and reference for the decision-making of the National Natural Science Foundation of China. Furthermore，by employing the “Motivation-Opportunity-Ability” and “Technology-Organization-Environment” frameworks，this paper conducts an in-depth analysis of the various risks that generative artificial intelligence might introduce to science fund project management. These risks include privacy and security risks，content and decision-making risks，ownership and infringement risks，ethical and integrity risks，diversity and innovation risks，as well as cultural and ethical risks. This analysis encompasses perspectives on artificial intelligence technology，legal regulations，and ethical considerations，with the aim of ensuring the healthy and orderly progression of science fund project management and promoting the standardized application of generative artificial intelligence in research management. To deal with the challenges and risks brought about by generative AI in research project management，this paper further proposes corresponding risk mitigation measures from the perspectives of policy，management，and technology to ensure the healthy and orderly conduct of research project management，thereby promoting the standardized application of generative AI in the field of research management.
Source: 中国科学基金
Authors: Chang Yao, Chen Gong, Guangyu Li, Yuting Deng, Sheng Wan, Peng Wei, Junjie Huang
Language: English
Subfield: Information Systems and Management
Field: Decision Sciences
Domain: Social Sciences
Open Access status: bronze
OpenAlex ID: W4413912570