The Robots are Coming, Part 132
First, some background, via Kevin Roose at New York Magazine:
Earlier this week, one of my business-beat colleagues got assigned to recap the quarterly earnings of Alcoa, the giant metals company, for the Associated Press. The reporter’s story began: “Alcoa Inc. (AA) on Tuesday reported a second-quarter profit of $138 million, reversing a year-ago loss, and the results beat analysts’ expectation. The company reported strong results in its engineered-products business, which makes parts for industrial customers, while looking to cut costs in its aluminum-smelting segment.”
It may not have been the most artful start to a story, but it got the point across, with just enough background information for a casual reader to make sense of it. Not bad. The most impressive part, though, was how long the story took to produce: less than a second.
If you’re into robots and algorithms writing the news, the article’s worth the read. It’s optimistic, asserting that in contexts like earnings reports, sports roundups and the like, the automation frees journalists for more mindful work such as analyzing what those earning reports actually mean
With 300 million robot-driven stories produced last year – more than all media outlets in the world combined, according to Roose – and an estimated billion stories in store for 2014, that’s a lot of freed up time to cast our minds elsewhere.
Besides, as Roose explains, “The stories that today’s robots can write are, frankly, the kinds of stories that humans hate writing anyway.”
More interesting, and more troubling, are the ethics behind algorithmically driven articles. Slate’s Nicholas Diakopoulos tried to tackle this question in April when he asked how we can incorporate robots into our news gathering with a level of expected transparency needed in today’s media environment. Part of his solution is understanding what he calls the “tuning criteria,” or the inherent biases, that are used to make editorial decisions when algorithms direct the news.
Here’s something else to chew on. Back to Roose:
Robot-generated stories aren’t all fill-in-the-blank jobs; the more advanced algorithms use things like perspective, tone, and humor to tailor a story to its audience. A robot recapping a basketball game, for example, might be able to produce two versions of a story using the same data: one upbeat story that reads as if a fan of the winning team had written it; and another glum version written from the loser’s perspective.
Apply this concept to a holy grail of startups and legacy organizations alike: customizing and personalizing the news just for you. Will future robots feed us a feel-good, meat and potatoes partisan diet of news based on the same sort behavioral tracking the ad industry uses to deliver advertising. With the time and cost of producing multiple stories from the same data sets approaching zero, it’s not difficult to imagine a news site deciding that they’ll serve different versions of the same story based on perceived political affiliations.
That’s a conundrum. One more worth exploring than whether an algorithm can give us a few paragraphs on who’s nominated for the next awards show.
Want more robots? Visit our Robots Tag.
Image: Twitter post, via @hanelly.