It didn’t take long for the downsides of a generative AI-empowered newsroom to make themselves obvious, between CNet’s secret chatbot reviews editor last November and Buzzfeed’s subsequent mass layoffs of human staff in favor of AI-generated “content” creators. The specter of being replaced by a “good enough AI” looms large in many a journalist’s mind these days with as many as a third of the nation’s newsrooms expected to shutter by the middle of the decade.
But AI doesn’t have to necessarily be an existential threat to the field. As six research teams showed at NYU Media Lab’s AI & Local News Initiative demo day in late June, the technology may also be the key to foundationally transforming the way local news is gathered and produced.
Now in its second year, the initiative is tasked with helping local news organizations to “harness the power of artificial intelligence to drive success.” It’s backed as part of a larger $3 million grant from the Knight Foundation which is funding four such programs in total in partnership with the Associated Press, Brown Institute’s Local News Lab, NYC Media Lab and the Partnership on AI.
This year’s cohort included a mix of teams from academia and private industry, coming together over the course of the 12-week development course to build “AI applications for local news to empower journalists, support the sustainability of news organizations and provide quality information for local news audiences,” NYU Tandon’s news service reported.
“There’s value in being able to bring together people who are working on these problems from a lot of different angles,” Matt Macvey, Community and Project Lead for the initiative, told Engadget, “and that that’s what we’ve tried to facilitate.”
“It also creates an opportunity because … if these news organizations that are out there doing good work are able to keep communicating their value and maintain trust with their readers,” he continued. “I think we could get an information ecosystem where a trusted news source becomes even more valued when it becomes easier [for anyone] to make low-quality [AI generated] content.”
The six teams include Bangla AI, which is developing a web platform that surfaces and translates relevant news stories into the Bengali language for journalists and New York City’s sizable Bangladeshi immigrant community.
“More than 200,000 legal Bangladeshi immigrants live in the United States, half of them in New York City,” Bangla team member, MD Ashraful Goni, told reporters during the demo day. “Only half of the population are fluent in English,” depriving the other half of easy access to the day’s news through mainstream media outlets like the New York Times or the Associated Press.
“Bangla AI will search for information relevant to the people of the Bengali community that has been published in mainstream media … then it will translate for them. So when journalists use Bangla AI, they will see the information in Bengali rather than in English.” The system will also generate summaries of mainstream media posts both in English and Bengali, freeing up local journalists to cover more important news than rewriting wire copy.
Similarly, the team from Chequeado, a non-profit organization fighting disinformation in the public discourse showed off the latest developments of its Chequeabot platform, Monitorio. It leverages AI and natural language processing capabilities to streamline fact-checking efforts in Spanish-language media. Its dashboard continually monitors social media in search of trending misinformation and alerts fact checkers so they can blunt the piece’s virality.
“One of the greatest promises of things like this and Bangla AI,” Chequeado team member Marcos Barroso said during the demo, “is the ability for this kind of technology to go to an under-resourced newsroom and improve their capacity, and allow them to be more efficient.”
The Newsroom AI team from Cornell University hope that their writing assistant platform will help do for journalists what Copilot did for coders – eliminate drudge work. Newsroom can automate a number of common tasks including transcription and information organization, image and headline generation, and SEO implementation. The system will reportedly even write articles in a journalist’s personal style if fed enough training examples.
On the audio side, New York public radio WNYC’s team spent its time developing and prototyping a speech-to-text model that will generate real-time captioning and transcription for its live broadcasts. WNYC is the largest public media station in New York, reaching 2 million visitors monthly through its news website.
“Our live broadcast doesn’t have a meaningful entry point right now for deaf or hard of hearing audiences,” WNYC team member, Sam Guzik, said during the demo. “So, we really want to think about as we’re looking to the future is, ‘how can we make our audio more accessible to those folks who can’t hear?’”
Utilizing AI to perform the speech-to-text transformation alleviates one of the biggest sticking points of modern closed-captioning: that it’s expensive and resource-intensive to turn around quickly when you have humans do it. “Speech-to-text models are relatively low cost,” Guzik continued. “They can operate at scale and they support an API driven architecture that would tie into our experiences.”
The result is a proof-of-concept audio player for the WNYC website that generates accurate closed captioning of whatever clip is currently being played. The system can go a step further by summarizing the contents of that clip in a few bullet points, simply by clicking a button on the audio player.
“This is a meaningful improvement, both for folks who can’t hear,” Guznik said. “But also for folks who are just not in the space where they can listen, and this is a really great tool if you’re in a place where you don’t have headphones and you want to follow along with what’s being said.“
On the back end, NOBL Media has developed an ad tech product that, “allows programmatic advertisers to reach publishers’ content in service of smaller audiences that can be targeted by geography or demography,” while the Graham Media Group created an automated natural language text prompter to nudge the comments sections of local news articles closer towards civility.
“The comment-bot posts the first comment on stories to guide conversations and hopefully grow participation and drive users deeper into our engagement funnels,” GMG team member Dustin Block said during the demo. This solves two significant challenges that human comment moderation faces: preventing the loudest voices from dominating the discussion and providing form and structure to the conversation, he explained.
”The bot scans and understands news articles using the GPT 3.5 Turbo API. It generates thought-provoking starters and then it encourages discussions,” he continued. “It’s crafted to be friendly.”
Whether the AI revolution remains friendly to the journalists it’s presumably augmenting remains to be seen, though Macvey isn’t worried. “Most news organizations, especially local news organizations, are so tight on resources and staff that there’s more happening out there than they can cover,” he said. “So I think tools like AI and [the automations seen during the demo day] enable the journalists and editorial staff more bandwidth.”
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