{"title":"Rage against the machine? Framing societal threat and efficacy in YouTube videos about artificial intelligence.","authors":"Andreas Schwarz, Janina Jacqueline Unselt","doi":"10.1111/risa.14299","DOIUrl":null,"url":null,"abstract":"<p><p>Artificial intelligence (AI) has become a part of the mainstream public discourse beyond expert communities about its risks, benefits, and need for regulation. In particular, since 2014, the news media have intensified their coverage of this emerging technology and its potential impact on most domains of society. Although many studies have analyzed traditional media coverage of AI, analyses of social media, especially video-sharing platforms, are rare. In addition, research from a risk communication perspective remains scarce, despite the widely recognized potential threats to society from many AI applications. This study aims to detect recurring patterns of societal threat/efficacy in YouTube videos, analyze their main sources, and compare detected frames in terms of reach and response. Using a theoretical framework combining framing and risk communication, the study analyzed the societal threat/efficacy attributed to AI in easily accessible YouTube videos published in a year when public attention to AI temporarily peaked (2018). Four dominant AI frames were identified: the balanced frame, the high-efficacy frame, the high-threat frame, and the no-threat frame. The balanced and no-threat frames were the most prevalent, with predominantly positive and neutral AI narratives that neither adequately address the risks nor the necessary societal response from a normative risk communication perspective. The results revealed the specific risks and benefits of AI that are most frequently addressed. Video views and user engagement with AI videos were analyzed. Recommendations for effective AI risk communication and implications for risk governance were derived from the results.</p>","PeriodicalId":21472,"journal":{"name":"Risk Analysis","volume":" ","pages":"2377-2395"},"PeriodicalIF":3.0000,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Risk Analysis","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1111/risa.14299","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2024/3/16 0:00:00","PubModel":"Epub","JCR":"Q1","JCRName":"MATHEMATICS, INTERDISCIPLINARY APPLICATIONS","Score":null,"Total":0}
引用次数: 0
Abstract
Artificial intelligence (AI) has become a part of the mainstream public discourse beyond expert communities about its risks, benefits, and need for regulation. In particular, since 2014, the news media have intensified their coverage of this emerging technology and its potential impact on most domains of society. Although many studies have analyzed traditional media coverage of AI, analyses of social media, especially video-sharing platforms, are rare. In addition, research from a risk communication perspective remains scarce, despite the widely recognized potential threats to society from many AI applications. This study aims to detect recurring patterns of societal threat/efficacy in YouTube videos, analyze their main sources, and compare detected frames in terms of reach and response. Using a theoretical framework combining framing and risk communication, the study analyzed the societal threat/efficacy attributed to AI in easily accessible YouTube videos published in a year when public attention to AI temporarily peaked (2018). Four dominant AI frames were identified: the balanced frame, the high-efficacy frame, the high-threat frame, and the no-threat frame. The balanced and no-threat frames were the most prevalent, with predominantly positive and neutral AI narratives that neither adequately address the risks nor the necessary societal response from a normative risk communication perspective. The results revealed the specific risks and benefits of AI that are most frequently addressed. Video views and user engagement with AI videos were analyzed. Recommendations for effective AI risk communication and implications for risk governance were derived from the results.
期刊介绍:
Published on behalf of the Society for Risk Analysis, Risk Analysis is ranked among the top 10 journals in the ISI Journal Citation Reports under the social sciences, mathematical methods category, and provides a focal point for new developments in the field of risk analysis. This international peer-reviewed journal is committed to publishing critical empirical research and commentaries dealing with risk issues. The topics covered include:
• Human health and safety risks
• Microbial risks
• Engineering
• Mathematical modeling
• Risk characterization
• Risk communication
• Risk management and decision-making
• Risk perception, acceptability, and ethics
• Laws and regulatory policy
• Ecological risks.