人工智能对社会科学研究影响深远

Group 1 - Artificial intelligence is recognized as a strategic technology that leads a new round of technological revolution and industrial transformation, significantly changing human production and lifestyle [1] - The introduction of large language models into social science research methods is reducing research costs and expanding methodological boundaries [3][4] - Large language models excel in text analysis, outperforming traditional methods in tasks such as sentiment recognition and stance judgment, with accuracy approaching human coding levels [3] Group 2 - Large language models can generate synthetic survey data by simulating responses based on specific demographic characteristics, providing a cost-effective alternative to traditional surveys [4] - Generative AI offers new avenues for simulating complex human interactions, aiding in the understanding of the natural evolution of social norms [5] - The application of generative agents in social simulations allows researchers to observe behavior interactions in controlled virtual environments, revealing the logic behind collective actions [5] Group 3 - Despite the promising applications of large language models, their actual use in social science is still in its infancy, lacking widespread and standardized research practices [6] - The complexity and opacity of these models present challenges to the transparency and replicability required in social science research [6] - There is a need for systematic research and normative development regarding the "black box" mechanisms of AI technologies [6] Group 4 - The governance of artificial intelligence has become a central topic in public policy and global governance discussions, focusing on institutional design, ethical boundaries, and accountability mechanisms [8] - The deployment of AI technologies is impacting international relations, domestic social structures, and economic development models, becoming significant topics in social science research [9] - Current research on AI's impact is primarily focused on short-term effects, with long-term implications and underlying mechanisms still requiring further empirical investigation [9] Group 5 - The biases and cognitive imbalances revealed by large language models highlight the need for social science to reflect on human biases and the structural imbalances in training data [10] - The findings indicate that mainstream models struggle to accurately predict perceptions in low-education groups and developing countries, suggesting a need for more inclusive training data [10] - Overall, AI serves as both a research tool and a research topic, profoundly influencing social science, with China's leading position in AI development presenting opportunities for advancing social science research [10]