Discussion on News Media and Generative AI: 'The Impact We Generate'

AI, Media & Democracy Lab

Part 2 of a series of events that explores and discusses the pitfalls and possibilities of Generative AI.

About this discussion seriesIn recent months, increased media attention has been given to AI-driven applications like Stable Diffusion, Dall-E, GPT-3, ChatGPT and Bard. These programmes are commonly referred to as Generative AI: technologies that learn from existing data in order to produce new content, including audio, (realistic) images and art, chat, text and code. Though these technological innovations have sparked renewed enthusiasm and interest for the field of AI, their potential to disrupt and transform has also been met with concern.In an effort to demystify the use of Generative AI, the AI, Media and Democracy Lab is organising a series of events that will explore and discuss the pitfalls and possibilities of the technology’s transformative impact on the media and journalism industries: How can the sector reap the benefits of artificially created content in line with the industries’ values, such as personal creativity, autonomy, user agency, transparency, objectivity, diversity, trust and authenticity?News Media and Generative AIFor our second session, we will explore the practical challenges of Generative AI for news media. How does the integration of Generative AI benefit and challenge the various phases of news reporting, including news gathering, production, distribution and interaction? Will the technology drastically alter the process as well as the product of news media and journalism, and if so, which technological characteristics play a central role as part of this transformation? The media landscape is not unfamiliar with digital change and disruption. The arrival of the Internet, the growth and popularity of crowd-sourcing websites and social media platforms and the emergence of data analytics have equally challenged the way news is provided and consumed. Even more so, the concerns news media face today (e.g., the need to verify the reliability of AI-generated information and the creative relationship between journalists and AI) sound awfully familiar to those experienced previously. Even in the case the issues raised by generative AI are truly distinctive (are they?), the industry may nonetheless draw from the lessons it has learned from the past. ModeratorNick DiakopoulosNicholas Diakopoulos is an Associate Professor in Communication Studies and Computer Science (by courtesy) at Northwestern University where he is Director of the Computational Journalism Lab (CJL) and Director of Graduate Studies for the Technology and Social Behavior (TSB) PhD program. He is also an Associate Professor II at the University of Bergen Department of Information Science and Media Studies and is on sabbatical as a visiting researcher at the University of Amsterdam IVIR during the 2022-23 academic year.PanellistsAimee RinehartAimee Rinehart is the program manager for the Associated Press (AP) Local News AI initiative, which aims to narrow the growing technology gap between national and local newsrooms. The program will distribute a national “scorecard” to assess AI readiness, provide free online courses to address knowledge gaps, and consult with newsrooms to integrate AI across their operations.Hannes CoolsHannes Cools is a post-doctoral researcher both at the AI, Media, and Democracy Lab and at the Digital Democracy Center. He holds a doctoral degree on AI and news from the Faculty of Social Sciences at the University of Leuven and a MA degree in International Politics of the University of Ghent in Belgium. His research interests include AI, computational journalism, and news recommender systems.David CaswellDavid is a former Executive Product Manager of News Labs. He previously managed data products at the Los Angeles Times Media group, and was Director of Product Management for content understanding at Yahoo!. He has also researched structured and automated journalism as an RJI Fellow at the Missouri School of Journalism, and has developed structured knowledge systems for intelligence applications. He is particularly interested in exploring sustainable models for news in the digital communications environment.Mathias Felipe de Lima SantosMathias-Felipe de Lima Santos is a postdoctoral researcher in the RPA Human(e) AI at the University of Amsterdam, Netherlands. Previously, he was a researcher at the University of Navarra, Spain, under the JOLT project, a Marie Skłodowska-Curie European Training Network funded by the European Commission’s Horizon 2020. He was also a Visiting Researcher at the Queensland University of Technology (QUT) in Brisbane, Australia. Mathias-Felipe is co-editor of the book “Journalism, Data and Technology in Latin America” published by Palgrave Macmillan in 2021. His research interests include the changing nature of communications driven by technological innovations, particularly in journalism, media, and online social networks.

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