For their part, however, the developers present seemed to welcome a move towards more story-driven news apps and visualizations.
“News apps are now edited, which is fairly new,” said Derek Willis, interactive developer at The New York Times. “I think we now hope to treat the editing process as seriously as you do with any story, including asking, ‘Does this work? Does it deserve to stay up?’ I think this has been the growth in the domain.”
Yet while many news app developers will agree that news apps need story, they also assert that journalism needs news apps, which Thibodeaux says do “the thing that a story can’t do, which is let you drill down.”
Rather than focusing only on individual, moment-in-time accounts, Chua says, journalistic publishing needs to include work that is both more focused and more incremental. “The real example of this is Homicide Watch: It updates in essentially real time, and you can drop in anytime and see what the trends are.” This sort of in-progress publishing, Chua believes, is essential, “if we want to get all the value of all the reporting we do every day, and also better serve these communities.”
Whether or not they agree on the need to diversify the way news is published, CAR reporters, data editors, and news app developers alike see new technologies changing the way that journalism is both conceptualized and executed.
As much was indicated by the strong impression made on many attendees by Jeff Larson and Chase Davis’s NICAR presentation, “Practical machine learning: Tips, tricks and real-world examples for using machine learning in the newsroom.”
“I’m pretty conservative on this stuff,” says Thibodeaux. “Source reporting leads to the best data reporting.” But after Larson and Davis’s presentation, he says, he can see how “the techniques start to act like sources. The tools let us ask questions that we couldn’t even conceive of before.”
Likewise, Cohen sees significant opportunities in algorithmic document analysis. “Our ability to make sense of messy original records has been revolutionized,” she says.
Whether the broader use of data science tools to do journalism will increase the acceptance of work like Silver’s remains to be seen, but his methods are more likely to be embraced than abandoned. If nothing else, the economic advantages of offloading more work to machines is hard to finesse:
“We don’t have the financial wherewithal to waste the kind of time we waste,” says Cohen. “If we spend a week doing document analysis that could be done by an algorithm, then we deserve to be replaced by machines.”
“We need to reserve the work for things that take human creativity and human insight.”