How will journalists use ChatGPT? Clues from a newsroom that’s been using AI for years
For better or worse, journalism’s background information-gathering, idea-hunting, and fact-checking that were once done without the internet…were now done with the internet. Don’t ask how we used to do it, or how we learned to do it the new way. It just happened — seeping into the bones of our profession without training courses, or guidebooks, or any significant debate on standards and ethics. For better or worse.
In contrast, the public release of ChatGPT on November 30, 2022, was very much a watershed moment, and it has many of us asking (before the fact, this time!) how our jobs and industry could be changed with the advent of the natural language functions of artificial intelligence.
Not surprisingly, the huffing and puffing over the past three months about ChatGPT and other similar AI-powered platforms run the gamut from a rush to mock its shortcomings to doomsday warnings that the machines will take our jobs. What’s clear is that all the predictions and public experiments are provisional, as the technology is rapidly evolving and so many applications are still to be discovered.
My own particular contribution in these early days comes instead from looking backward. By no design of my own, I’ve found myself swimming in the AI waters for the past decade in my job as co-founder and editor of the online magazine Worldcrunch, which publishes English editions of top foreign-language journalism.
Even before we launched in 2011, we were regularly being questioned about AI, though nobody was yet calling it that. Instead, would-be investors and partners, editorial and tech colleagues all wanted to know: “…and what about Google Translate?”
Some saw burgeoning machine translation as a threat to our business model. Others saw it as an inevitable downward pull on the quality of our editorial product. My reflex back then was to dismiss the machine, pointing out some of its more outrageous errors and assuring that it could never be good enough to take the place of our 100%- human translation.
Still, in Worldcrunch’s first years, working largely with freelance translators, I would find myself occasionally “catching” those who I suspected of using the machine. Sometimes it was incoherent copy, sometimes it was blatant errors (names, for example, get translated as words: Marine the Pen, anyone?) And sometimes I would get suspicious simply because the copy came back so quickly.
The truth is some of our best translators were also already using the machine…and there was nothing to “catch.” It was a tool, and professionals knew how to use it to help them be as good (and, yes, as fast) as possible. Those who abused or misused it were rather quickly spotted, and didn’t last long working with us.
Irene Caselli, a veteran journalist and editor on our team who speaks five languages, has been leaning on machine translation tools for years — even before they were really any good. “When it first came out, I would sometimes use Google Translate just to have many of the basic words and structure laid out in the new language on the page,” she recalled. “But I would have to check word by word.”
In the years since, while the machine has gotten exponentially better (today Irene prefers DeepL), she is quite conscious about when and how to use it. The output is generally stronger on more straightforward political and informational journalism, and weaker on writerly work and stories that “change register” within the same piece. In the languages where pronouns are often omitted (Italian, Spanish, French, etc.), translation programs still tend to use the default “he,” though Caselli says they are improving and can sometimes figure out that it’s “she” based on context.
So while 10 years ago she used the technology “to be able to type less,” Caselli says now, “I can sometimes use it as a bonafide first draft where I can just go through and edit the copy, spotting any mistakes along the way.”
When Le Monde launched an English edition last year, it did so with the systematic integration of an AI-powered translation platform into the editorial process, with human oversight.
Here at Worldcrunch, one application that’s been revealed over time has been automated translation’s role in our work as editors. In many (though not all) of the languages we translate from, the machine has gotten good enough that I can refer to it when trying to rephrase a clunky sentence in a translated piece from a language I don’t know. Perhaps even more useful has been the ability to browse through entire newspapers in a variety of languages and feel confident assigning stories to translators who don’t necessarily know the kind of pieces we’re looking for. That can save a ton of time and mental energy.
Even with the advances of machine translation, plenty of translators — of all ages — still choose not to use it. And I’ll always prefer to get story pitches from seasoned journalists who speak the languages. I imagine that AI natural language tools will largely be used for purposes of speed and shortcuts, and that will differ down to the level of individual affinities and working habits.
With that said, the potential changes we’ve begun to imagine with ChatGPT for the production of news and journalism — and all across the creative industries — go beyond speed. It’s fundamentally different from the various digital bells and whistles that have been thrust upon us over the past decade or two.
The first round of public experiments have been interesting to watch. There are initial ethical questions for news companies vis-à-vis readers. Medium has established a first policy on “transparency, disclosure, and publication-level guidelines” for the use of AI language tools.
But ultimately, if we stay on the current trajectory, it’s utterly plausible that AI language tools will begin to blend into our daily workflows, similar to how Google and Google Translate have. That’s a very big deal.
These advanced automated language models get at the very essence of what we do — or at least half of it: the writing, synthesizing information, crafting stories that has always made us muttering hacks feel, well, human.
The Google-Facebook era put our earning power on the line. This runs deeper. It’s an ego thing. Will we be reduced to the machine’s fact-checker?
Yet there’s the other half of what news and journalism is about, which makes us feel human in another way — and that stands beyond the reach of the databases and algorithms. We are also doing our job (and feeling alive) when we find or figure something out first. Our digital world — and creativity itself — can be so derivative that we can forget that we’re here because every day new stuff happens. We see things, make connections, and occasionally, according to the famous dictum, do the only “real” journalism: Publish what someone else doesn’t want published.
To further soothe our fragile egos, we can borrow from another old industry dictum: If journalism is the first draft of history, the machine goes nowhere without us.
Here are some thoughts, culled from Worldcrunch’s experience with machine translation, that may be applicable to using AI natural language tools.
— Maximizing the utility of automation requires human reasoning/thinking/creativity before feeding it to the machine, and human oversight (which may include more reasoning/thinking/creativity) after it comes out the other side.
— Don’t manage down: Online tools are best left in the hands of individuals.
— Editors will have to rely on the same “red flag” instincts that catch sloppiness, laziness, plagiarism, etc. (though some tools to help keep up in the early days would be nice!)
— Include regular training and an open conversation about new ways to use it, and possible pitfalls (we haven’t done enough of this with other internet tools).
— If the tools are powerful and reliable enough to integrate into the editorial process, publicly labeling work as “Produced with AI,” etc., will ultimately be pointless.
— Factor in exponential improvement in quality and precision.
— Factor in that human oversight will always be necessary.
— Speed matters.
— Quality matters more.
—Accuracy matters most.
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