In November, the news website Quartz unveiled a bold idea: a studio, funded by the Knight Foundation, dedicated to reporting the news using machine learning techniques.
Today, the Quartz AI Studio’s first story dropped — and it’s an intriguing peek at how advancements in artificial intelligence could provide journalists with new tools for digging into public documents.
For the story, Quartz reporters trained an algorithm to examine the section of ride-hailing app Lyft’s Initial Public Offering (IPO) that lists risks the company anticipates — and to identify the most “distinctive,” or unusual, things that rattle Lyft’s executives.
The resulting list of Lyft’s unusual concerns range from the fairly obvious to the moderately surprising. In addition to having concerns about “public perception,” the company’s leaders are also worried about how healthcare privacy laws will affect customers who use its service to catch rides to medical appointments. They’re also sweating whether cyberattacks could affect Amazon Web Services, which runs its platform.
Quartz’s Lyft story isn’t the most groundbreaking work of journalism in the world, but it’s an interesting proof of concept about how reporters can leverage new tools to pull interesting takeaways from otherwise dry public records — and, perhaps, a preview of things to come.
“This is taking [data journalism] to the next level where we’re trying to get journalists comfortable using computers to do some of this pattern matching, sorting, grouping, anomaly detection — really working with especially large data sets,” John Keefe, Quartz’s technical architect for bots and machine learning, told Digiday back when the Quartz AI Studio first launched.
READ MORE: Here’s what Lyft talks about as risk factors that other companies don’t [Quartz]
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