University of Kwazulu-Natal

Generative Ai And Journalism Mapping The Risk Landscape

Bonisiwe Shabane
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generative ai and journalism mapping the risk landscape

This research report explores how the growing use of generative AI across society poses risks for journalism as an industry, an institution, a practice and a product; for the wider information commons to which... Qualitative insights from interviews and a survey with a range of experts suggest that GenAI is amplifying many pre-existing long-standing challenges (e.g. business model and disruption, problematic business practices, information disorder, trust destabilisation). There was a strong appetite for intervention to mitigate such risks (e.g. by demanding greater transparency from AI companies, developing new professional and public literacies, devising new standards and strengthening public policy responses and funding). Generative AI tools are reshaping the information environment in ways most audiences never see.

From the data that trains them to the labour that maintains them, their inner workings raise urgent questions for journalism and democratic accountability. Our world is in the midst of a disruption triggered by the development of Artificial Intelligence (AI). Companies selling AI tools have become the most valuable corporations in modern times, worth trillions of dollars – more than the GDPs of most countries. They are becoming a pervasive influence on social, commercial, and political life, and shaking up industries. The media industry is among those facing new kinds of challenges due to the rise of AI. The practice and delivery of journalism, which is a vital component for functioning and healthy democracies, is changing in ways that are not obvious to its consumers.

To understand the impact of AI on our information environment and its political consequences requires a basic understanding of what Generative AI is and how it works. We need to “lift the bonnet” on what will increasingly power the information we receive and consume. READ I IT Rules 2025 test balance between safety and rights arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.

Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs. Jones, B. (Co-investigator), Luger, E. (Principal Investigator) & Elsden, C. (Co-investigator)

Research output: Book/Report › Other report N1 - This work has been supported by the PETRAS National Centre of Excellence for IoT Systems Cybersecurity, which has been funded by the UK EPSRC under grant number EP/S035362/1 N2 - This rapid review outlines a range of existing and potential risks generative AI poses if incorporated into journalism, written with newsroom leaders and journalists in mind. It is intended as a quick entry point into live and rapidly evolving discussions of the issues, with links and references out to useful resources – some academic and peer-reviewed, some journalistic. It is not a comprehensive analysis or an exploration of applications or benefits (of which there are a growing number of resources. For ease of navigation, the document is structured into three broad risk categories: editorial, legal, and societal.

The report was created as an output of collaboration between the University of Edinburgh and the BBC R&D Responsible Innovation team, as part of the PETRAS Building Public Value via Intelligible AI project. The work underpinning it includes: a review of existing research and grey literature, expert workshops with BBC staff, interviews and focus groups with BBC journalists. Why have we produced this? Generative AI is a branch of general purpose AI (also referred to as foundation models) that can create media content of varied types, including text, images, audio and code. Generative AI systems such as Large Language Models (LLMs) have pushed the boundaries of what is possible in content generation and created new challenges and risks for society. They will likely have significant impacts on news organisations and journalists as well as audience members/news users, impacting how news is gathered, produced, distributed and consumed.

However, the news media industry currently lacks an advanced understanding of exactly how they work, when and how they fail, and what mitigations are required to ensure they work in the public interest. AB - This rapid review outlines a range of existing and potential risks generative AI poses if incorporated into journalism, written with newsroom leaders and journalists in mind. It is intended as a quick entry point into live and rapidly evolving discussions of the issues, with links and references out to useful resources – some academic and peer-reviewed, some journalistic. It is not a comprehensive analysis or an exploration of applications or benefits (of which there are a growing number of resources. For ease of navigation, the document is structured into three broad risk categories: editorial, legal, and societal. The report was created as an output of collaboration between the University of Edinburgh and the BBC R&D Responsible Innovation team, as part of the PETRAS Building Public Value via Intelligible AI project.

The work underpinning it includes: a review of existing research and grey literature, expert workshops with BBC staff, interviews and focus groups with BBC journalists. Why have we produced this? Generative AI is a branch of general purpose AI (also referred to as foundation models) that can create media content of varied types, including text, images, audio and code. Generative AI systems such as Large Language Models (LLMs) have pushed the boundaries of what is possible in content generation and created new challenges and risks for society. They will likely have significant impacts on news organisations and journalists as well as audience members/news users, impacting how news is gathered, produced, distributed and consumed. However, the news media industry currently lacks an advanced understanding of exactly how they work, when and how they fail, and what mitigations are required to ensure they work in the public interest.

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