The local news industry in the UK and globally has been in a state of decline for the past decade. Dwindling audiences, as their readers have switched to the internet and social media for their news, means advertisers now choose to spend their money elsewhere. This causes problems beyond local news journalists increasingly finding themselves out of work. Who will provide the service of holding local authorities to account while shining a spotlight on issues of local importance?

Well, it turns out, robots might. Leading UK news agency Press Association (PA) is betting that artificial intelligence can fill the gap left by redundant reporters and shuttered local newspaper offices. A new initiative sees them partnered with news automation specialists Urbs Media – and endorsed by a 706,000 Euro Google grant – to create 30,000 localised news reports every month.

The technology relies on natural language generation (NLG), a cornerstone of much of the progress which has been made in recent years thanks to artificial intelligence and automation. The principle is that humans and computers can work together much more effectively if we all speak the same language. And teaching ultra-fast, infallible machines to understand and communicate in our own human languages is more efficient than teaching slow, fallible humans to communicate with computers in their language (for example, by learning computer code).

It’s the technology behind iPhone’s Siri and Amazon’s Alexa as well as “chatbots” which are increasingly taking on customer service roles. Only here instead of answering questions their job is to write news stories based on the data it is fed.

The PA project is known as RADAR – Reporters and Data and Robots – and relies on open data sets from government, local authorities and public services. Urbs Media editor-in-chief Gary Rogers told me that they initially started looking at the possibilities of generating stories for national media using open data sources, but soon realised that its highly geographically-segmented nature meant it was very well suited for local stories.

“So instead of writing one story about a dataset – a national story – you could write 10 regional stories or 200 local authority-based stories.”

“Some will be more interesting than others – but local papers don’t have the staff to write those stories and no centralised operation – even at the scale of PA – is going to take on writing 250 localised stories.” 

“We realised if we can write this automation into the local news production process, we are not taking someone’s job, we are doing something that no one else is doing.”

In fact human journalists, for now, still play an essential part in the process, and Gary doesn’t see this changing in the immediate future.

Humans are still needed to make the initial decision on which data sets will be analysed for stories. The process of “defining” story templates is also still done by humans – making the decision, for example, that if an outlier variable in a particular geographic region is beyond a certain threshold, it means the data is newsworthy.

Urbs chose established NLG specialists Arria to provide the AI backbone of its service, becoming beta users of their Articulate Lite product. The result is a tool which allows a journalist to write one story and then produce potentially hundreds of localised variations, of interest to different regional audiences, at the touch of a button.

As proof of concept, they initially built urbs.london, “to see if we could do something with open data that looked like a news operation,” Rogers says.

Satisfied that they indeed could, work began on enabling stories to be nationalised for any region in the UK, and it is planned that stories produced with the system will be available to local news publishers in the near future, through PA.

The hope is that UK news providers, where they do still have journalistic staff dedicated to local issues, will use stories uncovered by the AI as building blocks for their own, deeper and ongoing investigations into local affairs.

“We would like to see local journalists engaging with the stories we produce to develop them into bigger stories. They can add a layer to it – but the baseline story, what the numbers show – we can give that immediately to their public. We think this will be of huge benefit to the local press and to news consumers who are currently not getting those stories because there’s no one there to produce them.”

Local news journalism has certainly suffered in the digital era. Simply put, the content – by its nature, of interest only to a limited number of people – is difficult to effectively monetise. It certainly seems to have been beyond the ability of local news groups, which have seen news gathering budgets and staff levels cut to the bone.

This means less transparency around issues of public importance and less avenues for holding those responsible to account. Obviously, there will be commentators out there who will see PA’s proposed solution as another example of “robots taking our jobs”. But if the jobs were actually taken away years ago thanks to the inability of big publishers to adapt to technological change, then these particular robots could turn out to be the saviours of local news, rather than its executioner.