From Nieman Lab:
Uncertainty in the news industry, hype around AI, and hope for better business models and new revenue streams have all helped to drive news organizations to adopt AI technology, a new report from the Tow Center of Digital Journalism at Columbia University finds.
Felix Simon, a researcher and doctoral student at the Oxford Internet Institute, interviewed more than 130 journalists and news executives from 35 outlets in the United States, United Kingdom, and Germany. His findings suggest that as AI-powered search engines gain prominence and more newsrooms embrace the technology internally, “a familiar power imbalance” is emerging between news publishers and tech companies.
Newsrooms have become more open to AI not just because the technology has improved and become more publicly accessible, but also because it’s become acceptable and widely used in other industries. (“News organizations often anxiously watch their competitors, plagued by concerns that their own innovations have historically lagged behind those of their peers,” Simon noted.) Hard times in the journalism business have also pushed some news organizations to experiment with AI.
“I think one of the truths about the media industry is that it is an industry that is under a certain obvious strain for cash, for new business models, figuring out what their future is,” one Germany-based audience editor told Simon. “Basically this ‘What’s going to save us?’ question is all out there.”
If outlets are being pushed by news industry dynamics and market pressures, they’re also being pulled by promises of increased efficiency.
As one U.S.-based news executive put it: “The strategic question is: With the limited amount of time and resources, how could we make the most use of our journalistic talent?”
AI-generated news articles with incorrect information, made-up links, and baldly bad writing have grabbed headlines. But that’s far from the most common way newsrooms use AI. Nearly all of the interviewees reported using AI to help with transcription. (Many of us here at Nieman Lab use the AI-powered Otter.) Dynamic paywalls that use data points to predict how likely it is a certain user will pay for a subscription — and alter when (and if) that user sees a paywall based on those predictions — have been around for years.
Link to the rest at Nieman Lab
I think A.I. has its uses, but if I see something has been generated by A.I., I see it as a copout for doing the hard work.