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Abstract
With the continuous development of artificial intelligence (AI), algorithmic discrimination and discriminatory and misleading content (DMC) generated by AI have given rise to many negative effects in cyberspace, such as racial and gender discrimination, misinformation, and so on. The growing concern in society over AI governance urgently necessitates the establishment of an effective mechanism to supervise and govern AI-generated DMC. In this article, the discriminatory and misleading contents of AIGC (Artificial Intelligence Generated Content) were extracted according to Text Classification Model and then classified by Naive Bayesian algorithm. The results showed that under the Global Digital Compact (GDC), countries differed in their degrees of discrimination related to race, gender, religion, and age. The racial discrimination accounted for the highest proportion in the United States, with a score of 0.15; that in Britain and France took up a share of 0.06 and 0.07, respectively; and merely 0.03 in Germany. Discriminatory content of racial discrimination (M1) and gender discrimination (M2) in science and technology industry was relatively low, accounting for 0.05 and 0.08, respectively. Analyzing data within the Global Digital Compact (GDC) illuminates the disparities and trends in DMC generation across various countries, cities, industries, and individual users. This analysis provides valuable references for subsequent research and problem-solving initiatives under the compact. Furthermore, GDC plays a pivotal role in addressing issues related to AI-generated DMC, contributing significantly to the creation of a secure, reliable, and equitable cyberspace.
Author contributions
Zhi Li is mainly responsible for designing the framework of the article, designing research models, analyzing data, and writing and proofreading the article. Wenyi Zhang is mainly responsible for organizing literature and writing articles. Hengtian Zhang is mainly responsible for language proofreading and correction. Ran Gao is mainly responsible for language proofreading, data inspection, and liaison and implementation of research projects. Xingdong Fang is mainly responsible for the liaison and implementation of articles.
Disclosure statement
No potential conflict of interest was reported by the author(s).
Data availability statement
The data used to support the findings of this study are available from the corresponding author upon request.
Additional information
Notes on contributors
Zhi Li
Zhi Li is an associate professor in School of Media and Law, NingboTech University. He was born in Hubei, China. He graduated with a PhD from Macau University of Science and Technology. His research interest falls on cyberspace governance, international communication, and advertising strategy study.
Wenyi Zhang
Wenyi Zhang is studying in School of Humanities, University of Chinese Academy of Sciences. She was born in Shandong, China. Her current research interest is science communication.
Hengtian Zhang
Hengtian Zhang is studying in School of Foreign Languages, Renmin University of China. He was born in Hubei, China. His current research interest is cognitive translation and interpreting studies.
Ran Gao
Ran Gao works in School of Journalism and Communication, Nankai University. She was born in Tianjin, China. She graduated with a PhD from Communication University of China. Her research interest is ethics in AI communication and global and international communication.
Xingdong Fang
Xingdong Fang is a professor at Faculty of Arts and Humanities, Zhejiang University. He was born in Zhejiang, China. He graduated with a PhD from Tsinghua University. His research interest falls on new media and Internet communication, digital governance, and the history of Internet.