Research on Algorithmic Risks and Resilient Blocking Mechanisms of AIGC Embedded in Political and Legal Public Opinion Governance

DOI: https://doi.org/10.62517/jnme.202610416

Author(s)

Peipei Liang

Affiliation(s)

School of Media and Communication, Shandong University of Political Science and Law, Jinan, Shandong, China

Abstract

Objective: The integration of AIGC into the governance of political and legal public opinion has given rise to new risks, such as algorithmic bias and deepfake technologies. This study aims to clarify the underlying technical logic and risk mechanisms associated with these developments and to explore resilient governance pathways. Methods: Using resilience theory as a framework and combining literature analysis with theoretical deduction, this study constructs a risk transmission model characterized by technological trigger-scenario amplification-social diffusion, and derives a resilient mitigation mechanism across four dimensions-technology, institutions, actors, and ecology. Results: The integration of AIGC follows a logic of technological empowerment-scenario adaptation-power restructuring, giving rise to four types of risks-cognitive manipulation, algorithmic bias, information cocooning, and security erosion-which exhibit characteristics of chain-like transmission and phased evolution. Based on these findings, this study establishes a four-dimensional mitigation mechanism encompassing technological resilience, institutional resilience, actor resilience, and ecological resilience. Conclusion: The prevention and control of algorithmic risks in political and legal public opinion governance should move beyond rigid, reactive responses toward the proactive construction of resilience, achieving a paradigm shift from rigid stability maintenance to resilient governance, thereby providing an analytical framework and institutional solutions for addressing complex governance crises.

Keywords

AIGC; Public Opinion Governance; Algorithmic Risks; Resilient Mitigation Mechanism; Algorithmic Governance

References

[1] Wu, C., Lee, M., Lin, C., et al., A Longitudinal Study on Psychological Reactions and Resilience Among Young Survivors of a Burn Disaster in Taiwan 2015-2018, Journal of Advanced Nursing, 2020, (4), p.113. [2] Zeng, Y., Shi, J. Generative Artificial Intelligence in Government Governance: Logical Frameworks and Risk Regulation. China’s Administrative Management, 2023, 39 (09):90. [3] Shen, Y. (2025). Research on a new method of generating artificial intelligence to empower ideological and political education. Journal of Sociology and Education. vol.2 [4] Tsarouhas, P., & Grigoriadis, K. (2025). A socio-technical framework for AI trust in public administration. Transforming Government: People, Process and Policy. Advance online publication. [5] Cai, C. Promote the establishment of a new global governance paradigm for artificial intelligence. National Governance, 2025, (14):57. [6] Mandal, U., Setua, S. K., & Sarma, S. S. (2026). Deepfakes and beyond in the era of AI-generated disinformation: A systematic literature review of emerging technologies, challenges, and policy recommendations. Journal of Visual Communication and Image Representation, (98), Article 104748. [7] Zhou, J. (2025). Intelligent Recommendation Algorithm User Information Cocoon Room Effect Breakthrough Strategy. Advances in Computer and Communication, vol.6. [8] Gonzalez-Argote, J., Maldonado, E., & Maldonado, K. (2025). Algorithmic Bias and Data Justice: Ethical Challenges in Artificial Intelligence Systems. EthAIca, 159. [9] Zhan, G., Chen, Y. A conceptual model and generative mechanism for deepfake risks from a public safety perspective. Academic Monthly, 2025, 57(08):84. [10] Liu, X., Liu J. The Implications of Ecological Resilience Governance and an Analytical Approach to Its Implementation Pathways. Journal of Southwest University (Social Sciences Edition), 2024, 50(05):103-115.