"This report presents findings from the third wave of the Worlds of Journalism Study (WJS3), conducted between 2021 and 2025. In this iteration, we focused on journalists’ perceptions of risk and uncertainty in their profession and sought to identify key factors that shape how journalists navigate
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journalism’s changing environment. These risks and uncertainties arise from four partially overlapping domains: politics, economy, technology, and news consumption. Accordingly, the WJS3 questionnaire addressed journalists’ safety, editorial freedom, professional roles, news influences, and labor conditions. Our survey confirms that journalism is under pressure. Journalists worldwide are often undercompensated, and more than one-third engage in secondary employment. Economic pressures on news organizations have intensified in most countries. Nearly half of journalists have been targeted with hate speech, while psychological, physical, and digital threats are more prevalent in the Global South than in the Global North. More than 300 researchers from 75 countries participated in WJS3. This report provides a concise overview of key global findings. Subsequent publications will analyze specific topics in greater depth; please visit worldsofjournalism.org for more information." (Foreword, page 4)
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"As artificial intelligence (AI) becomes more seamlessly integrated into our social life, the unfair outcomes and ethical issues associated with AI and its subtechnologies have been widely discussed in scholarly work across disciplines in recent years. This study provides an overview of the conceptu
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alization, empirical scholarship, and ethical concerns related to algorithmic bias across diverse disciplines. In doing so, the study relies on the framework of AI-mediated communication and human-AI communication, as well as topic modeling and semantic network analysis to examine the conceptualization and major thematic areas of AI bias literature. The study reveals the complexity of the concept of algorithmic bias, which extends beyond the algorithm itself. Empirical scholarship on AI and algorithmic bias revolves around conceptualizations, human perceptions, algorithm optimization, practical applications, and ethics and policy implications. Understanding and addressing the ethical challenges require a multilevel examination from the perspectives of different stakeholders. Theoretical and practical implications are further discussed in the context of AI and algorithmic justice." (Abstract)
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