Innovative GenAI uses cases you should know about

By Sonali Verma

INMA

Toronto, Ontario, Canada

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I thought it would be nice to start off the year with some positive, inspiring examples of how AI is actually helping news media businesses serve their staff and their audiences better.

• Here’s a neat approach to building trust through transparency with your audience from software company Every, which also writes about technology. It offers readers the option of seeing the source material, including what’s not in the article.

• Thinking about audio? The New York Times uses AI to match videos to audio for its Snippets experiment to help its audience discover programming by swiping through and listening to bits of different articles or podcasts. It also uses language models to help select those clips and for automated voices.

• AURA is a tool to help reporters undertake research before they write a story, built by The EconomistIndian ExpressDR (Danish Broadcasting Corporation), and Aftonbladet under the JournalismAI initiative. It lets journalists chat with a database to give them context about how big a story is.

They can upload an academic paper or a press release to get the process started. AURA generates storyline ideas as well as a headline and a pitch for editors, all of which can be further refined through prompting to suit the publication’s needs.

Then, a basic research report is generated, which features different personas appropriate for the story that answer questions based on publicly available information. The next step is to get AURA to recommend potential sources for interviews and future storylines as well.

• Checkmate, a collaboration between Germany’s Deutsche Presse-AgenturNews UK, Mexico’s DataCritica, and the British Broadcasting Corporation, is a real-time fact checker for broadcasts. It provides a transcript for videos and highlights in yellow any claims being made, then searches for sources to verify them and provides links to them. It was built as a tool to help journalists figure out what is accurate and what is not. 

• The Financial Times conducted an investigation into missing Ukrainian children who were being put up for adoption in Russia. It used AI to join tens of thousands of listings from different databases and then to narrow down potential abduction cases. Reporters then worked off this smaller list to confirm identities.

“Ultimately, it illustrates the importance and impact of taking a really big data set and taking algorithmic tools to expand the possibilities of what we’re able to do on our own and work very quickly,” said FT visual investigative journalist Peter Andringa.

• U.S.-based Hearst and Gannett, Canada’s The Globe and Mail, and E24 in Norway have built a tool that alerts journalists to interesting real-estate events, using clustering techniques for anomaly detection and LLMs to identify newsworthiness. The LLM prompts were designed after real estate reporters defined — and weighted — criteria for newsworthiness. 

• GMA Network in the Philippines and Finland’s Helsingin Sanomat have built a document-comparison tool to help journalists quickly compare large sets of documents.

For example, it can be used for comparing political parties’ election platforms against government coalition agreements to see whose messages prevail or analysing stakeholder comments on legislative proposals to identify supporters and opponents along with their reasoning. It works in English as well as the national and regional languages of the Philippines and Finland.

• Time Magazine lets you chat with an article, asking questions, both via voice and text. You can also listen to it in five languages or summarise it.

• The Washington Post is using GenAI to engage its subscribers in the comments section. Some features of its “Conversations” experience include:

    • Prompts: AI-generated questions will appear with most journalism to offer subscribers a starting point for a dialogue.
    • The Washington Post is using GenAI to engage its subscribers in the comments section: Some features of its “Conversations” experience include: Subscribers can immediately understand their peers’ dominant takeaways and reactions to the journalism through AI-generated summaries of the comments.
    • Sentiment reactions: A range of sentiments, including “thoughtful,” “clarifying,” “new to me,” and “provocative,” are available for subscribers to characterise their peers’ comments.

For the record, its subscribers did not immediately love the feature. There are 995 comments on the blog post about the feature, many of them negative.

My opinion?

When you try something new, sometimes your audience loves it and sometimes it doesn’t. Fortune favours the bold. Kudos to The Post for transparently trying something that hasn’t been done before and learning something about their audience in the process. As of writing, the feature was still being tested, and readers were interacting with it.

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About Sonali Verma

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