The IPPR recommended standardised labels for AI-generated news, showing what information had been used to create those answers. The IPPR recommended standardised labels for AI-generated news, showing what information had been used to create those answers. AI-generated news should carry'nutrition' labels, thinktank says AI-generated news should carry "nutrition" labels and tech companies must pay publishers for the content they use, according to a left-of-centre thinktank, amid rising use of the technology as a source for current affairs . The Institute for Public Policy Research (IPPR) said AI firms were rapidly emerging as the new "gatekeepers" of the internet and intervention was needed to create a healthy AI news environment. It recommended standardised labels for AI-generated news, showing what information had been used to create those answers, including peer-reviewed studies and articles from professional news organisations.

The advancement of artificial intelligence (AI) has led to its application in many areas, including journalism. One key issue is the public's perception of AI-generated content. This preregistered study investigates (i) the perceived quality of AI-assisted and AI-generated versus human-generated news articles, (ii) whether disclosure of AI's involvement in generating these news articles influences engagement with them, and (iii) whether such awareness affects the willingness to read AI-generated articles in the future. We employed a between-subjects survey experiment with 599 participants from the German-speaking part of Switzerland, who evaluated the credibility, readability, and expertise of news articles. These articles were either written by journalists (control group), rewritten by AI (AI-assisted group), or entirely generated by AI (AI-generated group). Our results indicate that all news articles, regardless of whether they were written by journalists or AI, were perceived to be of equal quality. When participants in the treatment groups were subsequently made aware of AI's involvement in generating the articles, they expressed a higher willingness to engage with (i.e., continue reading) the articles than participants in the control group. However, they were not more willing to read AI-generated news in the future. These results suggest that aversion to AI usage in news media is not primarily rooted in a perceived lack of quality, and that by disclosing using AI, journalists could attract more immediate engagement with their content, at least in the short term.

The rapid proliferation of AI-generated text online is profoundly reshaping the information landscape. Among various types of AI-generated text, AI-generated news presents a significant threat as it can be a prominent source of misinformation online. While several recent efforts have focused on detecting AI-generated text in general, these methods require enhanced reliability, given concerns about their vulnerability to simple adversarial attacks. Furthermore, due to the eccentricities of news writing, applying these detection methods for AI-generated news can produce false positives, potentially damaging the reputation of news organizations. To address these challenges, we leverage the expertise of an interdisciplinary team to develop a framework, J-Guard, capable of steering existing supervised AI text detectors for detecting AI-generated news while boosting adversarial robustness. By incorporating stylistic cues inspired by the unique journalistic attributes, J-Guard effectively distinguishes between real-world journalism and AI-generated news articles. Our experiments on news articles generated by a vast array of AI models, including ChatGPT (GPT3.5), demonstrate the effectiveness of J-Guard in enhancing detection capabilities while maintaining an average performance decrease of as low as 7% when faced with adversarial attacks.