--- title: "Journalists Reported a News Story Using Machine Learning" description: "An experiment about reporting news stories with machine learning just released its first effort — a deep dive into Lyft's IPO." date: "2019-03-02" modified: "2019-03-02" authors: - name: "Jon Christian" job_title: "Executive Editor" link: "https://futurism.com/authors/jonc" url: "https://futurism.com/the-byte/quartz-machine-learning-report-story" categories: - "Artificial Intelligence" - "Machine Learning" tags: - "lyft" - "machine learning" - "quartz" - "the digest" --- # Journalists Reported a News Story Using Machine Learning ![An experiment about reporting news stories with machine learning just released its first effort — a deep dive into Lyft's IPO.]() *\Image: Conger Design/Xresch/Tag Hartman-Simkins\* ## Machine Journalism **I**n 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. ## Face Lyft 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. ## Data Journalism *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]() \[*Quartz*\] ***More on machine learning:*** [Statistician: Machine Learning Is Causing A “Crisis in Science”]() ## Author I'm responsible for editing, assigning, and scouting at Futurism, as well as occasionally writing for the site. That work involves keeping an eye on a wide range of narratives and issues, but over the past few years I've become increasingly interested in how AI is shaping the future of media, the web, and information itself. My day-to-day often involves collaborating on reporting and commentary projects bylined by my colleagues, but I try to find time to do my own reporting as well; stories I've broken for Futurism have been cited by publications including the New York Times, the Washington Post, the New Yorker, Wired, Ars Technica, New York Magazine, the Columbia Journalism Review, Bloomberg, and more. I grew up in Southern Vermont and attended Vanderbilt University. Prior to Futurism, I contributed to outlets including the Boston Globe, Vice, Wired, Slate, the Atlantic, the Outline, Ars Technica and more, and did stints in farming and food service. I currently live in Brooklyn, New York, and in my free time I enjoy pinball, word games, cycling, running, and music production. ### Author social links [Bluesky]()