Ten things I learnt about journalism and AI in 2020

It has been a year since we published the results of our global survey on what news organisations are doing with and thinking about AI technologies. The big themes that stuck out for me from that research were:

Augmentation: Most use cases were designed to improve the effectiveness and efficiency of the work of human journalists, not to replace them. AI-powered technologies were used to connect content better to the public, rather than to replicate the editorial process.

Knowledge gap: There was a shortage of people skilled in AI technologies and, just as important, a lack of knowledge across news organisations about the potential and pitfalls of those technologies.

Strategy: In a fast-moving, highly-pressurised industry, there was a lack of strategic thinking about a set of technologies that can have a systemic impact on all aspects of journalism.

I don’t think the report missed much or got things wrong, but since then we’ve had a year of intense and wide-ranging research and activities with journalists using AI around the world. We’ve created online training courses and innovation workshops to address some of the issues that the report raised. It’s been an exciting and educational time. In December we’ll showcase at our JournalismAI Festival the most interesting results, and discuss the issues that have been raised during the year. But here are some of the things I have learnt since we published the report, or things that were small in the report but now feel much bigger:

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One of the Journalism AI Collab team meetings

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This post was written by Professor Charlie Beckett, Director of POLIS and of the JournalismAI project, on the 1-year anniversary of the publication of the JournalismAI report: New Powers, New Responsibilities.

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JournalismAI is a project of POLIS, supported by the Google News Initiative.

Journalist, LSE media professor, Polis think-tank director. Writes about journalism, UK & global politics

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