Citation: Sejin Paik, Sarah Bonna, Ekaterina Novozhilova, Ge Gao, Jongin Kim, Derry Wijaya, and Margrit Betke. The Affective Nature of AI-Generated News Images: Impact on Visual Journalism. The International Conference on Affective Computing and Intelligent Interaction, September 10-13, MIT Media Lab, Cambridge, MA. 10 pages.
Abstract: This study explores the affective responses and newsworthiness perceptions of generative AI for visual journalism. While generative AI offers advantages for newsrooms in terms of producing unique images and cutting costs, the potential misuse of AI-generated news images is a cause for concern. For our study, we designed a 3-part news image codebook for affect-labeling news images based on journalism ethics and photography guidelines. We collected 200 news headlines and images retrieved from a variety of U.S. news sources on the topics of gun violence and climate change, generated corresponding news images from DALL-E 2 and asked study participants to annotate their emotional responses to the human-selected and AIgenerated news images following the codebook. We also examined the impact of modality on emotions by measuring the effects of visual and textual modalities on emotional responses. The findings of this study provide insights into the quality and emotional impact of generative news images produced by humans and AI. Further, results of this work can be useful in developing technical guidelines as well as policy measures for the ethical use of generative AI systems in journalistic production. The codebook, images and annotations are made publicly available to facilitate future research in affective computing, specifically tailored to civic and public-interest journalism.
Codebook used in this work
Image data and annotations used in this work (link will work soon)
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