As the influence of transformer-based approaches in general and generative artificial intelligence (AI) in particular continues to expand across various domains, concerns regarding authenticity and explainability are on the rise. Here, we share our perspective on the necessity of implementing effective detection, verification, and explainability mechanisms to counteract the potential harms arising from the proliferation of AI-generated inauthentic content and science. We recognize the transformative potential of generative AI, exemplified by ChatGPT, in the scientific landscape. However, we also emphasize the urgency of addressing associated challenges, particularly in light of the risks posed by disinformation, misinformation, and unreproducible science. This perspective serves as a response to the call for concerted efforts to safeguard the authenticity of information in the age of AI. By prioritizing detection, fact-checking, and explainability policies, we aim to foster a climate of trust, uphold ethical standards, and harness the full potential of AI for the betterment of science and society.
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http://dx.doi.org/10.1016/j.isci.2024.108782 | DOI Listing |
PeerJ Comput Sci
October 2024
Chair of Cyber Security, Department of Computer Science, College of Computer and Information Sciences, King Saud University, Riyadh, Saudi Arabia.
The proliferation of fake news on social media platforms necessitates the development of reliable datasets for effective fake news detection and veracity analysis. In this article, we introduce a veracity dataset of Arabic tweets called "VERA-ARAB", a pioneering large-scale dataset designed to enhance fake news detection in Arabic tweets. VERA-ARAB is a balanced, multi-domain, and multi-dialectal dataset, containing both fake and true news, meticulously verified by fact-checking experts from Misbar.
View Article and Find Full Text PDFOpen Res Eur
September 2024
Grupo Ciberimaginario, XR COM LAB, Faculty of Communication Sciences, Universidad Rey Juan Carlos (ROR 01v5cv687), Madrid, Community of Madrid, 28943, Spain.
Background: This study examines the scientific misinformation about climate change, in particular obstructionist strategies. The study aims to understand their impact on public perception and climate policy and emphasises the need for a systemic understanding that includes the financial, economic and political roots.
Methods: A systematic literature review (SLR) was conducted using the PRISMA 2020 model.
JMIR Form Res
October 2024
Department of Media and Information, Michigan State University, East Lansing, MI, United States.
Background: Older adults, a population particularly susceptible to misinformation, may experience attempts at health-related scams or defrauding, and they may unknowingly spread misinformation. Previous research has investigated managing misinformation through media literacy education or supporting users by fact-checking information and cautioning for potential misinformation content, yet studies focusing on older adults are limited. Chatbots have the potential to educate and support older adults in misinformation management.
View Article and Find Full Text PDFNat Hum Behav
December 2024
Department of Psychology, University of California, Berkeley, Berkeley, CA, USA.
Teach Learn Med
September 2024
Department of Pediatrics, University of Minnesota, Minneapolis, Minnesota, USA.
: Misleading health information is detrimental to public health. Even physicians can be misled by biased health information; however, medical students and physicians are not taught some of the most effective techniques for identifying bias and misinformation online. : Using the stages of Kolb's experiential learning cycle as a framework, we aimed to teach 117 third-year students at a United States medical school to apply a fact-checking technique for identifying bias and misinformation called "lateral reading" through a 50-minute learning cycle in a 90-minute class.
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