Katalin Feher

How do elite US news outlets frame AI within accounts of hype, disappointment and tempered expectation? This study finds that rather than fueling hype, coverage tends to adopt a cautious tone – emphasizing everyday uses and current impacts over speculative futures. A content analysis of three high-profile US dailies shows more positive tones when only developers or vendors discuss their own AI tools. The findings underscore journalists’ important role in contextualizing and balancing biased narratives and will be a valuable contribution for academics, journalists and policymakers seeking to understand hype, disappointment and expectation dynamics in technology discourse and their broader social implications.

Generative AI is not just a technological leap -- it is a societal stress test, reshaping trust, identity, equity, and authorship. This exploratory PhD seminar examined emerging academic trends in AI-driven synthetic media and worlds, emphasizing ethical risks and societal implications. In Part One, students explored core concepts such as generative AI, fake media, and synthetic knowledge production. In Part Two, they critically engaged with these challenges, producing actionable insights. The two-part format enabled deep reflection on power, responsibility, and education in AI-augmented communication. Outcomes offer practical guidance for educators, researchers, and institutions committed to fostering more responsible, human-centered AI use in media and society.

Generative AI transforms knowledge production, validation, and dissemination, raising academic integrity and credibility concerns. This study examines 53 academic influencer videos that reached 5.3 million viewers to identify an emerging, structured, implementation-ready pipeline balancing originality, ethical compliance, and human-AI collaboration despite the disruptive impacts. Findings highlight generative AI's potential to automate publication workflows and democratize participation in knowledge production while challenging traditional scientific norms. Academic influencers emerge as key intermediaries in this paradigm shift, connecting bottom-up practices with institutional policies to improve adaptability. Accordingly, the study proposes a generative publication production pipeline and a policy framework for co-intelligence adaptation and reinforcing credibility-centered standards in AI-powered research. These insights support scholars, educators, and policymakers in understanding AI's transformative impact by advocating responsible and innovation-driven knowledge production. Additionally, they reveal pathways for automating best practices, optimizing scholarly workflows, and fostering creativity in academic research and publication.

This groundbreaking book demystifies generative AI’s transformative impact on media, socio-cultural dynamics, ethics, and policy. Defining generative AI as an evolutionary leap in the development of artificial intelligence, the author examines intricate human-machine interactions and socio-technical dynamics, advocating robust, proactive AI governance to address emerging uncertainties. The book is clearly structured into six key chapters, each exploring distinct aspects of the relationship between artificial intelligence, media, and society. The chapter on "Transformation" examines how machine behavior is reshaping our datafied society, questioning whether data is the new oil, or digital manure. "Generative AI" investigates the models and future impacts of generative AI as a co-intelligence, revisiting the Turing Test and analyzing societal-business impacts. "AI Media" explores the convergence of media and AI, highlighting robot journalism, synthetic content, and the disinformation era and discussing the trend toward high-risk optimism. "Uncertainties" addresses inherent unpredictability vs. strategic foresight, focusing on challenged business models, sustainability concerns, and emotional intelligence factors. "Ethics" analyzes generative morality and dual-use technology, covering trusted AI principles—from misuse to integrative solutions. Finally, "Policy" discusses governance, labor market impacts, the importance of human rights and power dynamics in generative AI. Each chapter also provides summaries of impact projects, reflective art, scholarly questions, and strategic takeaways—extended with a comprehensive glossary. This is an essential resource for scholars, students, policymakers, technologists, ethicists, and AI industry leaders seeking to rapidly understand and address the challenges and opportunities of generative AI and AI media in a cohesive framework.

Will generative AI empower society or lead us into a world of synthetic balance? This chapter explores generative AI, a technology that creates synthetic outputs—content, designs, and models—from learned data patterns, fundamentally transforming creativity, business processes, and societal structures. Key concepts explored include the Turing Trap, stochastic parrots, and the creator economy; alongside critical issues such as the impact of AI-driven mis/disinformation and poisoned data. Subsections also investigate the mechanics of generative models and their effects on industries such as entertainment and NewsTech. Reflective artworks illustrate generative AI’s ability to mirror and augment human creativity, potentially redefining contemporary art and media. The chapter also features an impact project centered on the FBI’s AI-detected generative AI fraud case. In the final section, the chapter summarizes critical debates and strategic takeaways for academia, industry, and policy, providing insights into the transformative potential of generative AI.

Are AI generators transforming our ethical boundaries, or can traditional frameworks adapt to emerging challenges? This chapter explores the evolving ethics of generative technologies, from the dual-use dilemma to trusted AI principles. Key topics include responsible and explainable generative AI, considering the challenges of harm, misuse, and generative colonization. Subsections also highlight the role of agent behavior and integrative ethics. This chapter concludes with the Data School at Utrecht University’s innovative tools for evaluating AI’s societal impact. The artwork references highlight AI’s potential to constrain human identity, distort historical truth, and blur the boundaries of privacy. In the concluding segment of the chapter, scholarly debates and strategic takeaways offer key focus areas for academia, industry, and policy.

Will AI media expand human imagination or limit us to synthetic realities? This chapter explores the fusion of AI and media, from AI-driven metamedia to the convergence of generative mediatization. Key concepts like robot journalism, synthetic-hyper-immersive media experiences, and deepfakes are examined alongside concerns about recommendation systems and their potential for mind control. Subsections cover how AI transforms media creation, consumption, and manipulation with a critical look at dangerous optimism. Bloomberg’s AI-generated financial journalism illustrates the chapter, and reflective art projects showcase the synergy between technology and creativity. In the closing section, scholarly questions and strategic takeaways offer focal areas for academia, industry, and policy, addressing the challenges and potential of AI in media.

Are current regulatory frameworks agile enough to manage the emergence of generative AI, or do they need a proactive change? This chapter explores the governance of generative AI, focusing on human-centered design and the shifting labor, political economy, power dynamics, and geopolitics. Key themes include the impact of AI on creativity and social innovation, and the contrasting policies shaping generative AI. Subsections also address proactive strategies, rights-based governance, and co-regulation while examining AI’s potential for social good. The chapter illustrates connected efforts through the European Digital Media Observatory (EDMO), which aims to improve Europe’s response to online disinformation. The summary of the related artworks highlights the societal consequences of AI-driven hyperproduction and data monopolies. In the closing section, critical debates and strategic takeaways offer insights for academia, industry, and policy on shaping a fair and sustainable AI future.

Is the change brought by AI creating real progress in society or trapping us in technosolutionism? This chapter examines transformation technologies, from the evolution of AI to a datafied society in which data serves as oil and manure. Key concepts include the attention economy, socio-technical systems, and machine behavior in the transformation era. The chapter concludes with insights into the United Nations’ Open-Source Initiative and reflective artworks that illustrate AI’s impact on creativity, human-machine relations, and cultural shifts. The final section presents key scholarly debates and strategic takeaways for academia, industry, and policy, highlighting the profound effects of AI transformation on society.