AI has "enormous potential" for local newsrooms (if they know how to use it)

Credit: Photo by Shahadat Rahman on Unsplash

AI is all the rage in journalism at the moment. Large Language Models (LLM) like OpenAI's ChatGPT are the latest iteration in a fast-developing process. While this "generative AI" is receiving a lot of attention, the biggest impact of AI in the news industry is likely to be behind the scenes.

Enormous potential for local newsrooms

Up until now, machine learning tools have become handy for tasks such as analysing data leaks, transcribing interviews, fine-tuning paywalls, and serving up article recommendations.

Publishers like Buzzfeed and Reach plc are now starting to dabble in the realms of using AI to create news articles. Both announcements led to speculation over whether this was the final nail in the coffin for journalists.

Media Voices recently published the report Practical AI for Local Media in which they explore what AI can do for local news. One of the big upsides is product development.

American publisher McClatchy uses AI to analyse real estate sales and found this to be a big driver of new audiences, but too resource-intensive to scale meaningfully. So the publisher developed a bot to cover this beat instead.

This new product – regular real estate sales coverage at scale – has supported its growth and has provided the opportunity to drive new audiences to other parts of its site.

Nordic countries have for a while had news organisations which embrace AI to cover resource-intensive beats. This is helped significantly by the good availability of publicly accessible and well-organised data.

Read more: I am a local journalist: what can AI do for me?

The beats featured in the report, such as property prices, sports reporting, and weather updates, all rely on robust and structured data sources, which allows for potentially limitless granularity in reporting.

AI helps local newsrooms filter and identify interesting content from large volumes of data. It can surface stories that may have been missed, such as a bot focusing on junior hockey in the US able to pick up that one team had won for the first time in 40 games, says Cecilia Campbell, chief marketing officer at United Robots, quoted in the report.

Securing reliable and accurate data sources is crucial. Public data from government and health services are commonly used, along with data from large charities or commercial data suppliers. The source of the data, and its potential bias, also need to be seriously considered.

Elin Stueland, deputy news editor at Stavanger Aftenblad in Norway puts it bluntly: "Crap in, crap out. If you have good data, that just solves everything. But if the data is chaotic, then you will never succeed."

Misconceptions and misunderstandings

In March 2022, the Associated Press (AP) published a report on artificial intelligence in local news. It looked at the understanding and readiness of local US newsrooms, and how AI could help newsgathering, distribution, production and the business side. It found that local newsrooms were barely using existing tools, largely because of the lack of capacity of staff to learn how to use the technology, as well as issues related to an already complex and patchy use of technology.

The AP has been using AI to support news content creation for a few years now. A program manager had already pointed out in 2021 that the technology would "hopefully broker opportunities for journalists to do deeper, richer stories."

AI is more than technology, it is a problem-solver.

In the UK, PA Media’s RADAR, created in 2018, uses AI to produce national stories with local relevance by digging into data sets and creating specific versions for different areas.

Read more: Press Association is using artificial intelligence to help local news organisations produce more data-driven stories

It uses a combination of AI and traditional news reporting, according to the Media Voices report. The journalists use templates, sometimes a few different ones, to format a version of the story at a local level, making stories more interesting and relevant to local audiences.

Emilia Díaz-Struck, research editor at the International Consortium of Investigative Journalists (ICIJ), which has used machine learning in investigations for more than five years, told DataJournalism.com in 2021: "[Machine learning] has a big human component […] it isn’t magic, it takes considerable time and resources.

"Reporters, editors, software engineers, academics working together - that’s where the magic happens."

Therefore, "chasing the algorithm" or what could be a "quick win" for now, is one of the pitfalls for publishers to avoid.

Professor Charlie Beckett, founding director of JournalismAI told Journalism.co.uk: "In the short term, publications prioritising low-quality SEO content might see some gain, but in the long term other people will do that better.

"If you do clickbait you’re going to lose."

That is also what Jonathan Heawood, executive director of the Public Interest News Foundation (PINF) and its head of impact Joe Mitchell believe. They told Journalism.co.uk that whether cheaper, more processed "cookie-cutter" content will threaten the jobs of "free-range journalists" will depend on the "vision and motivation of the publisher."

Throughout all the case studies in the Media Voices report, the importance of knowing your audience's needs and thinking about the "why" appears central, be it for reducing churn or engaging readers more with journalism.

The goal is to free up journalists' time to focus on adding value and telling stories, while AI can handle the more mundane tasks.

It appears as a good opportunity for all media organisations, not just local ones, to think about their mission and how they are serving their audiences.

Mind the technology gap

Many local newsrooms are not using machine learning. Global initiatives such as the JournalismAI project could be key to supporting local, small, underfunded newsrooms to develop the AI tools they need.

Heawood and Mitchell from PINF conclude: "AI won’t solve the news deserts. Worst-case scenario, it will increase news inequality, and, best case scenario, small newsrooms will be able to achieve more.

"It relies upon the capacity for newsrooms to investigate these tools, and understand them."

Artificial intelligence is changing the way we approach journalism. Join our panel of experts at Newsrewired on 23 May to learn more about how it could impact your newsroom.

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