mlsanityData to 14 Oct 2026

Journalists' Perceptions of Artificial Intelligence and Disinformation Risks

arXiv 2509.01824 ↗First posted 1 Sep 2025cs.CY cs.AIcited by 1

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Abstract (arXiv)

This study examines journalists' perceptions of the impact of artificial intelligence (AI) on disinformation, a growing concern in journalism due to the rapid expansion of generative AI and its influence on news production and media organizations. Using a quantitative approach, a structured survey was administered to 504 journalists in the Basque Country, identified through official media directories and with the support of the Basque Association of Journalists. This survey, conducted online and via telephone between May and June 2024, included questions on sociodemographic and professional variables, as well as attitudes toward AI's impact on journalism. The results indicate that a large majority of journalists (89.88%) believe AI will considerably or significantly increase the risks of disinformation, and this perception is consistent across genders and media types, but more pronounced among those with greater professional experience. Statistical analyses reveal a significant association between years of experience and perceived risk, and between AI use and risk perception. The main risks identified are the difficulty in detecting false content and deepfakes, and the risk of obtaining inaccurate or erroneous data. Co-occurrence analysis shows that these risks are often perceived as interconnected. These findings highlight the complex and multifaceted concerns of journalists regarding AI's role in the information ecosystem.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 5 of its 88 references that are in the map. First posted on arXiv 1 Sep 2025.

Builds on

5 of 88 references.

References · 5

  1. 2018The Malicious Use of Artificial Intelligence: Forecasting, Prevention, and Mitigationcited by 165
  2. 2023Generative Language Models and Automated Influence Operations: Emerging Threats and Potential Mitigationscited by 158
  3. 2017Fairness in Machine Learning: Lessons from Political Philosophycited by 149
  4. 2024How Do AI Companies "Fine-Tune" Policy? Examining Regulatory Capture in AI Governancecited by 7
  5. 2025Artificial Intelligence Training in Media: Addressing Technical and Ethical Challenges for Journalists and Media Professionalscited by 1

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Citing papers per year: 2025: 1.

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  1. 2025Generative Propagandacited by 0