Case Study

Lenfest AI Fellow spotlight: Mark Chonofsky

Chicago Public Media

October 19, 2026

This profile is part of the Lenfest AI Collaborative and Fellowship Program’s 2026 Impact Report.

As a Lenfest Institute AI Fellow at Chicago Public Media, Mark Chonofsky is exploring how AI can expand access to local news, improve archival audio, and surface journalism more effectively. We spoke with Mark about the lessons he’s learned so far, what excites him most about AI, and where he believes the industry is headed next.

About his role and the problem he is trying to solve

Mark started at Chicago Public Media almost two years ago, after working on Kamala Harris’s presidential campaign and going to the U.S. Army Officer Candidate School in preparation for commission as Signal Officer in the Illinois Army National Guard. He has a DPhil from Oxford, along with two degrees in physics from Cambridge.

At Chicago Public Media, his first major focus area was translation. His goal: To expand access to news in Spanish and dramatically improve turnaround times for Spanish-language coverage.

He and his colleagues built workflows that allow Spanish-language journalists and editors to edit in Spanish, instead of starting translations from scratch. They have also used AI and code-generation tools to translate entire interactive features by modularizing text into structures like JSON, translating those, and then dropping them back into the interactive.

Mark is particularly proud of one example of AI translation: Chicago Public Media’s coverage of the Chicago-born Pope being appointed.

“Chicago Pope was a big thing for our Spanish audiences, who are largely Catholic,” Mark said. “Previously, we would have taken 12-plus hours to translate that news out into Spanish, but we had a Pope headline out in two hours, because we have this much improved process for doing it.”

The site’s traffic was five times higher than that on a normal day, as a result.

Alongside translation, Mark is also working on archival-quality transcription and smarter content discovery tools that can surface both articles and transcripts to staff, building on the Philadelphia Inquirer team’s fellowship product, Dewey. “We have been exploring different ways of surfacing our content internally.”

He also worked on building a propensity to donate model that Chicago Public Media hopes to share with other not-for-profit news organizations, once it has been thoroughly tested.

The biggest surprise during the fellowship

“People are rightly worried about what kinds of things are possible and what kinds of things may happen as a result of overuse or poor use of AI, and I think there are all sorts of news organizations around the world that are doing a bad job of this or an exploitative job of this,” Mark said.

“It has taken me some time to really understand how to conceptualize the role… I’m not here to tell anybody what to do, and I’m certainly not here to replace anybody’s job. I think organizationally, the issue is, we know that there are some incredibly powerful capabilities that are here or are coming, and we need somebody who knows about them, and then can help the organization make informed choices about to what extent they are useful for our work or deleterious to our mission.”

He also points out that AI itself is rarely the hardest part of any project. The real challenge lies in teaching, trust, and organizational adoption.

This emphasis on teaching and judgment, rather than hype, has been a defining lesson of his fellowship.

One thing the industry gets wrong about AI

“We put a lot of thought into the concerns about AI replacing the journalists, and I understand why people are scared by that, but I think that anyone who spends 15 seconds with a journalist understands why that’s not just unfeasible now, but unhelpful, always,” Mark said.

“I really think that what this moment spurs us towards is to consider our essential humanity as news storytellers. I think that that’s actually a good thing for us. I think we’ve lost too much of that, and we should do more of it.”

Lessons that other newsrooms could apply tomorrow

Mark is skeptical of the notion that AI is moving so fast that newsrooms can’t possibly keep up. He thinks that fear of being left behind is overstated.

His lesson for other newsrooms: Treat AI as a technology domain you can learn, like any other, and recognize that journalists already possess many of the skills – such as critical thinking and erudition – that are needed to understand it.

What he is paying attention to right now

Mark is particularly interested in the rise of powerful local models, systems that can be run and controlled more closely by news organizations.

What he is most optimistic about

Mark is most optimistic about AI’s ability to tackle long‑standing resource‑intensive problems.

“AI used well has the possibility to straighten out a number of product engineering problems that have just bedeviled us for some time because we haven’t been able to devote the engineering resources to fixing them,” he said.

From centralizing data to fixing messy datasets and enabling smoother transitions between systems, he sees AI as a way to do crucial but unglamorous work “much faster, much cheaper.”

The Fast Four

A book, article, or newsletter you’d recommend?
If you let AI do your writing, I will come to your house and kill you by Sam Kriss.

What’s one thing you’re learning that has nothing to do with AI?
Working with a medium-format camera and enjoying the fully mechanical, film‑based process.

What local journalism challenge deserves more attention?
Consolidation.

One word that describes the next year of AI in news?
“Similar… I think there’s a lot of cool stuff that people are doing, but I am not forecasting a huge amount of disruption.”