The Quartz AI Studio published its first story on Thursday, using a machine-learning algorithm to identify the most distinctive risk factors in Lyft's initial public offering filing. The studio, funded by the Knight Foundation and announced in November, aims to apply artificial intelligence to news reporting.
For the Lyft story, Quartz reporters trained an algorithm to examine the risk factors section of the ride-hailing company's IPO filing and flag concerns that stood out from typical corporate disclosures. The resulting list included both predictable and less common worries. Lyft's executives cited public perception, but also expressed concern about how healthcare privacy laws might affect customers using the service for medical appointments. The filing also noted potential cyberattacks on Amazon Web Services, which hosts Lyft's platform.
John Keefe, Quartz's technical architect for bots and machine learning, described the project as an effort to get journalists comfortable with using computers for pattern matching, sorting, grouping, and anomaly detection in large datasets. He spoke to Digiday when the studio launched.
The story is not a major investigative coup, but it serves as a proof of concept for how reporters can use machine learning to extract insights from dense public records. Quartz's approach suggests a potential shift in data journalism, where algorithms help surface unusual details that might otherwise be buried in lengthy filings.
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