Category Archives: AI

Investigative journalism and ChatGPT: using generative AI for story ideas

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Applications of genAI in the journalism process Pyramid with the bottom 'pre-production' layer highlighted: Idea generation and stimulation: identify and map systems and rules, apply brainstorming frameworks (iceberg model, 5 whys, 8 angles of data journalism). Planning.
Generative AI can be used at all points in the journalism process: this post focuses on pre-production

Last week I delivered a session at the Centre for Investigative Journalism Summer School about using generative AI tools such as ChatGPT and Google Gemini for investigations. In the first of a series of posts from the talk, here are my tips on using those tools for idea generation.

Generative AI tools may not be entirely reliable, but that doesn’t mean that they’re not useful. Journalism, after all, is about more than just gathering information: reporters also need to generate story ideas, identify and approach potential sources, plan ahead, write and edit stories and solve a range of technical challenges. All of these are areas where genAI can help.

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VIDEO: AI in journalism: machine learning and natural language processing

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Machine learning and Natural Language Processing (NLP) are two forms of artificial intelligence that have been used for years within journalism. In this video, part of a series of video posts made for students on the MA in Data Journalism at Birmingham City University, I explain how both technologies have been used in journalism, the challenges that journalists face in using them, and the various concepts and jargon you will come across in the field.

PS: The MA courses at BCU have an open day this month: you can register here.

The examples mentioned in the video include:

Investigative journalism’s AI challenges: accuracy and bias, explainability and resources

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screenshots of guidelines on AI

Having outlined the range of ways in which artificial intelligence has been applied to journalistic investigations in a previous post, some clear challenges emerge. In this second part of a forthcoming book chapter, I look at those challenges and other themes: from accuracy and bias to resources and explainability.

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