Documentation, Informaiton & Knowledge ›› 2023, Vol. 40 ›› Issue (4): 32-40.doi: 10.13366/j.dik.2023.04.032

• Academic Focus(1):Artificial Intelligence Generated Content (AIGC)Governance • Previous Articles     Next Articles

Analysis on AIGC False Information Problem and Root Cause from the Perspective of Information Quality

MO Zuying PAN Daqing LIU Huan ZHAO Yueming   

  1. School of Information Management, Zhengzhou University of Aeronautics, Zhengzhou, 450046
  • Online:2023-07-10 Published:2023-08-16
  • Contact: Correspondence should be addressed to MO Zuying,, ORCID: 0000-0003-0661-9333
  • Supported by:
    This is an outcome of the project "Research on the Intervention of Online False Information Dissemination Behavior in the Context of Social Media "(21BTQ049)supported by National Social Science Foundation of China.

Abstract: [Purpose/Significance] This paper aims to analyze the types and characteristics of false information in AIGC,which has a positive role in understanding the root causes of false information and reducing its generation. [Design/Methodology] In this study, the method of data testing experiment was adopted. Based on the perspective of information quality, the types and characteristics of false information generated by AIGC were analyzed through collecting first-hand testing data of AI systems and second-hand false information of AIGC. Further, focusing on the information generation process of artificial intelligence language models, we explored the origins of false information generation in AIGC. [Findings/Conclusion] False information in AIGC mainly consists of two types: factual false information and hallucinatory false information. Factual false information is primarily focused on errors in five aspects: data errors, author and his works errors, errors in objective facts, programming code errors, and machine translation errors. On the other hand, hallucinatory false information is mainly concentrated in the areas of fake news events, false academic information, false health information, and bias and discrimination. The origins of false information in AIGC are related to three factors: large-scale language models, pre-training datasets, and human annotations. [Originality/Value] This study employes a data testing experimental approach, complemented by the collection of second-hand data, comprehensively analyzes various types of false information in AIGC, and divides false information into factual false information and hallucinatory false information based on the generation mechanisms and manifestations, which provides a theoretical foundation for further research on false information in AIGC.

Key words: Artificial intelligence generated content(AIGC), False information, Information quality, Factual false information, Hallucinatory false information, Root cause analysis