By Anabelle Nicoud
Two years into the generative AI boom, many newsrooms and publishers are rethinking their editorial roles to include an AI-centric mindset. The New York Times, Hearst, AFP, The United States today… Many news agencies of all sizes have recently created new editorial or product jobs, illustrating the importance that AI has quickly acquired in the media.
What are these AI leaders doing?
Anabelle Nicoud I spoke with leaders in the United States and Europe to find out.
See the first interview in this series: Meet AI Leaders: Cynthia Tu, Data Journalist and AI Specialist
Tyler Dukeslecturer at Duke University, has a long career as a journalist and data scientist.
In July, he started a new role at McClatchyone of the largest local news publishers in the United States.
As McClatchy’s editor-in-chief, AI innovation in journalism, he works with local newsrooms on mastering AI and developing new tools.
He currently works with a developer journalist and five colleagues from local editorial offices.
His goal? Create tools that would really make the lives of journalists easier.
Congratulations on your new role. How did you get into AI?
I was one of several journalists who participated in a master class that McClatchy hosted for the company last March, focused on AI. One of our tasks was to find smart ways to integrate AI into our workflows, as well as what tools we could implement to do our jobs better.
In the News division, the conversation focused on how we could improve our journalism and its value to our readers and audiences. Journalists and editors from across the country brainstormed ideas and we came up with some interesting projects and tools.
But our main idea was that we really needed a group of people who would dedicate themselves full time to studying this question, to following the evolution of the technology and to thinking critically about how it can have an impact on our journalism, where it can’t, and where it shouldn’t – essentially sorting the wheat from the chaff.
There is never a dull moment in AI: a week can see a lot of new models on the market.
There are many things ahead of us every day, not just in the field of generative AI, but in the broader AI sector. It can be hard to keep up with everything if you’re not watching it 24/7.
Even for me – and this is my job now – there are still days where I’m like, “Well, I’m not going to figure this out for two weeks.”
“Our idea was to create a response team totally immersed in the world of AI; test, troubleshoot and create important pieces of journalism, guided by the principles of our newsrooms. They actively seek to solve problems in journalism and serve as a resource to our newsrooms.
Does this require you to adopt a new mindset towards technology? How do you do this?
Well, check back with me next month because I feel like it changes every couple of weeks!
I really feel like the pace of technological change is accelerating.
My career has focused on computational journalism, data journalism, and investigative journalism, so I’ve always had to stay up to date with the latest and cutting-edge tools in the field.
We achieved this by creating a community where we share techniques, knowledge, read each other’s work and learn from each other.
“I approach it like I do a journalism project: reading, learning and consuming large amounts of information from companies and people; observing these companies, while mastering some of these things.
I’m trying to process this information with the goal of making 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 main goal: lots of learning, research, and experimentation with these tools.
What do you mean when you say that some of these tools don’t apply to journalism?
We must ensure that what is essentially human in journalism remains in the hands of humans. These tools have many limitations and different industries may have acceptable margins of error, but we know that these tools have significant, often unquantifiable, margins of error.
“In journalism, the margin for error is almost zero and we need to be clear about where our uncertainties lie.”
This is a really scary area when we talk about generative AI, and I think two key considerations are:
- What are the essentially human tasks, where journalists – especially those who know their beats and their communities – will always be better, faster and smarter than a big linguistic model?
- How can we ensure that what we get from these tools meets our journalistic standards and serves our community?
So, what is your mandate? How did McClatchy approach this position?
There is a lot of variability in my daily life. Being a journalist is quite common. I spend a lot of time keeping up to date with everything I read about new models.
My days are filled with meetings with our editorial staff and groups within our editorial staff. 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 part of my workload: building tools.
Are you referring to internal – or external – tools?
We focus on building tools for the newsroom, which include both user-facing tools and internal tools. At this stage, we are primarily focused on internal tools, while other teams across the company develop different solutions.
My main goal is to create useful tools for journalists, helping us address quality control by surfacing content that journalists should pay attention to.
This approach allows us to maintain the necessary layer of news judgment in our process. We are also exploring the possibility of making some of these tools accessible to users if there is a clear benefit to doing so.
What excites me 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 under development and testing. First, we want to equip people with a framework that highlights areas where generative AI is good, could be good, or how it could help.
From the beginning, our focus is on training and getting people to think critically about this technology.
Our goal is to improve journalism, and that can mean many things. In some cases, it involves creating multiple types of stories. In others, it’s about telling stories we’ve never been able to tell before or giving newsrooms features they’ve never had. So we’re approaching this problem from several different angles.
What is the general level of enthusiasm around Gen AI – for you personally and for the editorial teams you work with?
I think there are mixed opinions about Generation AI, not just in our newsrooms but across the industry as a whole, which seems fair to me. I would describe the general feeling as cautious optimism.
We try to be realistic about what these tools and technologies can do in their current state and to demystify their capabilities, so as not to assign values to them that don’t exist.
At the same time, we’re careful to maintain a healthy skepticism toward these tools, much like we do with everything else, especially in data journalism.
Just because the information is in a spreadsheet doesn’t mean it’s 100% accurate, and journalists should exercise the same skepticism here.
This approach will make us better consumers and users of technology, and will also improve our coverage of its impact on the world, which is another essential part of the conversation.
About the author, Anabelle Nicoud
An independent journalist and consultant based in San Francisco, Nicoud currently contributes to the newsletter The Audiencers and the Canadian monthly L’Actualité.
She worked with Apple News+ (2022-2024); supported the editorial teams of La Presse (2015-2019) and Le Devoir (2019-2022) in their digital transformation, while leading ambitious editorial projects that won prestigious journalism awards in Canada and Quebec.
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.
