By Anabelle Nicoud
Two years into the generative AI boom, many newsrooms and publishers are rethinking their editorial roles to include an AI-focused mentality. The New York Times, Hearst, AFP, USA Today… many news organisations of all sizes have recently created new roles working in editorial or product, an illustration of the importance AI has rapidly acquired in media.
What do these heads of AI do?
Anabelle Nicoud talked to leaders in the United States and Europe to find out.
See the first interview in this series: Meet the AI leaders: Data reporter and AI specialist Cynthia Tu
Tyler Dukes, an adjunct lecturer at Duke University, has a long career as a reporter and data expert.
In July, he started a new role with McClatchy, one of the largest local news publishers in the United States.
As McClatchy’s Lead Editor, AI innovation in journalism, he works with local newsrooms on AI literacy and developing new tools.
He currently works with one journalist developer and five fellows from local newsrooms.
His goal? Building tools that would truly make journalists’ lives easier.
Congrats on your new role. How did you get involved with AI?
I was one of several journalists who participated in a master class held by McClatchy for the company back in March, focusing on AI. One of our tasks was to figure out smart ways to integrate AI into our workflows, and the tools we might implement to do our jobs better.
In the news division, the conversation centered around how we could enhance our journalism and its value to our readers and audiences. Reporters and editors from across the country brainstormed ideas, and we came up with a few interesting projects and tools.
But our top idea was that we really need a group of people who are dedicated full-time to looking at this issue, tracking the evolution of the technology, and thinking critically about how it can impact our journalism, where it can’t, and where it shouldn’t – essentially, sorting the wheat from the chaff.
There’s never a dull moment in AI: one week can see a lot of new models on the market.
There’s so much coming at us every day – not just in the generative AI space, but in the broader AI sector. It can be challenging to keep up with everything if you’re not looking at it 24/7.
Even for me – and this is my job now – there are still days where I feel like, ‘Well, I’m not going to understand that for another two weeks.’
‘Our idea was to create a strike team that would be fully immersed in the world of AI; testing, troubleshooting, and building things that mattered for journalism, driven by the principles of our newsrooms. They actively look to solve journalism problems and serve as a resource for our newsrooms.’
Does this require that you have to adopt a new mindset towards tech? How do you do that?
Well, check with me next month because it feels like it’s changing every couple of weeks!
I definitely feel that the pace of technological change is speeding up.
My career has been focused on computational journalism, data journalism, and investigative journalism, so I’ve always had to keep up with the latest, cutting-edge tools in the space.
We’ve done that by building a community where we’re sharing techniques, knowledge, reading each other’s work, and learning from one another.
‘I’m approaching it as I do a journalism project: reading, learning, and consuming vast amounts of information from companies and people; observing those companies, while also getting hands-on with some of this stuff.’
I’m trying to process this information with the goal of turning it into something useful for people, because a lot of this technology isn’t necessarily useful for journalism.
So, figuring out where to focus our attention is an ongoing conversation, but that’s the focus: a lot of learning, researching, and experimenting with these tools.
What do you mean when you say that some of these tools don’t apply to journalism?
We need to make sure that what’s essentially human about journalism stays in the hands of humans. There are a lot of limitations with these tools, and different industries might have acceptable margins of error, but we know that these tools have large, often unquantifiable margins of error.
‘In journalism, the margin of error is almost zero, and we need to be clear about where our uncertainties lie.’
That’s a really scary space when we’re talking about generative AI, and I think two key considerations are:
What are the tasks that are essentially human, where journalists – especially those who know their beats and communities – will always be better, faster, and smarter than a large language model?
How do we ensure that what we’re getting from these tools meets our journalistic standards and serves our community?
So, what’s your mandate – how did McClatchy approach this position?
There’s a lot of variability in my day to day. Being a journalist, that’s pretty common. I spend a lot of time catching up on all the things that I’m reading about new models.
My days are filled with meetings with our newsrooms and with groups within our newsrooms. I’m also building the logistics of this team and what it will look like. And then there’s what I hope will become a bigger piece of my workload: building tools.
Are you referring to internal facing tools – or external?
We’re focused on building tools for the newsroom, which includes both user-facing and internal tools. At this stage, our primary emphasis is on internal tools, while other teams in the company are developing different solutions.
My main goal is to create tools that are useful for journalists, helping us address quality control by surfacing content that journalists need to pay attention to.
This approach allows us to maintain the necessary layer of news judgment in our process. We’re also exploring the possibility of making some of these tools user-facing if there’s a clear benefit.
What excites me the most is how we can deconstruct the journalist’s workflow to identify tedious and time-consuming tasks. By targeting these areas, we can make processes more efficient, easier, and even more enjoyable, ultimately serving as a force multiplier for our work.
Can you tell me about one of the tools?
We’re definitely still in the early stages, so a lot of what we’re doing is still being built out and tested. First, we want to equip people with a framework that highlights what generative AI is good at, could be good at, or how it could help.
Early on, we’re focusing on training and getting people to think critically about this technology.
Our goal is better journalism, and that can mean a lot of things. In some cases, it’s about doing more kinds of stories. In others, it’s about telling stories we’ve never been able to tell before or giving newsrooms capabilities they’ve never had. So, we’re tackling this from several different directions.
What’s the general level of enthusiasm around Gen AI – for you personally, and the editorial teams you work with?
I think there are mixed opinions about Gen AI, not just in our newsrooms but across the industry, which I believe is fair. I’d describe the general sentiment as cautious optimism.
We’re trying to be realistic about what these tools and technologies can do in their current state and demystify their capabilities, so we’re not attributing values to them that don’t exist.
At the same time, we’re ensuring we maintain a healthy skepticism about these tools, much like we do with anything else—especially in data journalism.
Just because information is in a spreadsheet doesn’t make it 100% accurate, and journalists should apply the same skepticism here.
This approach will make us better consumers and users of the technology, and also improve our coverage of how it’s impacting the world, which is another critical part of the conversation.
About the author, Anabelle Nicoud
A freelance journalist and consultant based in San Francisco, Nicoud currently collaborates with The Audiencers newsletter and the Canadian monthly L’actualité.
She has worked with Apple News+ (2022-2024); helped the editorial teams at La Presse (2015-2019) and Le Devoir (2019-2022) with their digital transformation, while leading ambitious editorial projects that have won prestigious journalism awards in Canada and Quebec.
A former journalist for La Presse and correspondent for Libération in Canada, Nicoud is passionate about the impact of technology on the media, she closely follows issues related to the use of artificial intelligence.
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