A Mathematical Framework for Assessing Generative Artificial Intelligence in Media Using Fuzzy MCDM

This study examines the key ethical and professional challenges associated with the application of generative artificial intelligence (GenAI) in journalism, their relationship with public attitudes, and the potential role of GenAI as an auxiliary tool rather than a replacement for journalists. The empirical research is based on data collected from 273 public respondents and assessments provided by an expert panel of three experts. An integrated methodological framework combining principal component analysis (PCA), linear regression, Analytic Hierarchy Process (AHP), and the Fuzzy Analytic Hierarchy Process (FAHP) was applied. PCA was used to identify the underlying dimensions of the challenges associated with GenAI in journalism, linear regression to examine the relationships between relevant predictors and respondents’ attitudes, and FAHP to rank the identified factors according to their relative importance based on expert judgments. The findings identify lack of human control, copyright, and fake news as major ethical concerns in the application of GenAI to media content creation, while inadequate professional education also emerges as a prominent challenge. The results further support the view that GenAI is expected to serve as an auxiliary tool in journalism rather than completely replace journalists in newsrooms. A comparison of the rankings derived from regression analysis and FAHP revealed a very strong positive correlation in one comparison (0.949) and a perfect positive correlation in the other (1.000), supporting the hypothesis that the two ranking approaches produce highly consistent results. Overall, the findings highlight the importance of maintaining human oversight, strengthening professional education, protecting intellectual property, and safeguarding the integrity, reliability, and credibility of media content. By integrating public responses with expert judgments, this study provides a comprehensive methodological approach to evaluating the challenges of GenAI in journalism and identifying priorities for its responsible integration into journalistic practice.