Artificial Intelligence Tools and Bias in Journalism-related Content Generation: Comparison Between Chat GPT-3.5, GPT-4 and Bing




media bias, NLP, natural language, computational communication, ChatGPT


This study explores the biases present in artificial intelligence (AI) tools, focusing on GPT-3.5, GPT-4, and Bing. The performance of the tools has been compared with a group of experts in linguistics, and journalists specialized in breaking news and international affairs. It reveals that GPT-3.5, widely accessible and free, exhibits a higher tendency rate in its word generation, suggesting an intrinsic bias within the tool itself rather than in the input data. Comparatively, GPT-4 and Bing demonstrate differing patterns in term generation and subjectivity, with GPT-4 aligning more closely with expert opinions and producing fewer opinative words. The research highlights the extensive use of generative AI in media and among the general populace, emphasizing the need for careful reliance on AI-generated content. The findings stress the risks of misinformation and biased reporting inherent in unexamined AI outputs. The challenge for journalists and information professionals is to ensure accuracy and ethical judgment in content creation to maintain the quality and diversity of content in journalistic practices.


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Author Biographies

Mar Castillo-Campos, Loyola Andalucia University

Mar Castillo-Campos has a degree in Communication, and a master's degree in Research Methods. She is currently working at Universidad Loyola Andalucía as a research assistant, integrating quantitative methodologies and data science in the field of journalism. 

David Varona-Aramburu, Universidad Complutense de Madrid

David Varona-Aramburu has a PhD in Journalism and currently works at the Journalism and New Media Department at Universidad Complutense de Madrid. David has worked for more than 20 years in the media,  and does research in Communication and Media. 


David Becerra-Alonso, Loyola Andalucia University

David Becerra-Alonso obtained his PhD in the School of Computing at the University of the West of Scotland, where he worked on dynamical chaotic systems. David currently holds a position as a lecturer at Universidad Loyola Andalucía. His research interests include dynamical systems, emergent collective behavior, and machine learning techniques and heuristics. 


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How to Cite

Castillo-Campos, M., Varona-Aramburu, D., & Becerra-Alonso, D. (2024). Artificial Intelligence Tools and Bias in Journalism-related Content Generation: Comparison Between Chat GPT-3.5, GPT-4 and Bing. Tripodos, (55), 99–115.

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