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What News-Writing Bots Mean for the Future of Journalism

17 February 2017

From Wired:

When Republican Steve King beat back Democratic challenger Kim Weaver in the race for Iowa’s 4th congressional district seat in November, The Washington Post snapped into action, covering both the win and the wider electoral trend. “Republicans retained control of the House and lost only a handful of seats from their commanding majority,” the article read, “a stunning reversal of fortune after many GOP leaders feared double-digit losses.” The dispatch came with the clarity and verve for which Post reporters are known, with one key difference: It was generated by Heliograf, a bot that made its debut on the Post’s website last year and marked the most sophisticated use of artificial intelligence in journalism to date.

When Jeff Bezos bought the Post back in 2013, AI-powered journalism was in its infancy. A handful of companies with automated content-generating systems, like Narrative Science and Automated Insights, were capable of producing the bare-bones, data-heavy news items familiar to sports fans and stock analysts. But strategists at the Post saw the potential for an AI system that could generate explanatory, insightful articles. What’s more, they wanted a system that could foster “a seamless interaction” between human and machine, says Jeremy Gilbert, who joined the Post as director of strategic initiatives in 2014. “What we were interested in doing is looking at whether we can evolve stories over time,” he says.

. . . .

It works like this: Editors create narrative templates for the stories, including key phrases that account for a variety of potential outcomes (from “Republicans retained control of the House” to “Democrats regained control of the House”), and then they hook Heliograf up to any source of structured data—in the case of the election, the data clearinghouse VoteSmart.org. The Heliograf software identifies the relevant data, matches it with the corresponding phrases in the template, merges them, and then publishes different versions across different platforms. The system can also alert reporters via Slack of any anomalies it finds in the data—for instance, wider margins than predicted—so they can investigate. “It’s just one more way to get a tip” on a potential scoop, Gilbert says.

The Post’s main goal with the project at this point is twofold. First: Grow its audience. Instead of targeting a big audience with a small number of labor-intensive human-written stories, Heliograf can target many small audiences with a huge number of automated stories about niche or local topics. There may not be a wide audience for stories about the race for the Iowa 4th, but there is some audience, and, with local news outlets floundering, the Post can tap it. “It’s the Bezos concept of the Everything Store,” says Shailesh Prakash, CIO and VP of digital product development at the Post. “But growing is where you need a machine to help you, because we can’t have that many humans. We’d go bankrupt.”

Link to the rest at Wired

Disruptive Innovation, Non-Fiction

4 Comments to “What News-Writing Bots Mean for the Future of Journalism”

  1. It’s bad enough when robots will take our jobs away, but now even liberal arts workers (when they have a job) are threatened by Bots. What’s this world coming to?

  2. I’ll tell you what news-writing bots mean for the future of journalism:


    In every case I’ve seen, the article is merely a string of boilerplate phrases and sentences strung together because they fit a particular set of quantitative data. The data are available to the public now; we don’t need a special priesthood of professional journalists to interpret them into paragraph form for us.

    The first bits of the newspaper (archaic term!) to fall to bot reporters were the stock report and the sports pages, specifically the post-game reports. Instead of a bored newswire person cranking out virtually identical stories day after day, the news service simply uses a template into which the appropriate company names and stock ticker symbols, or team names and scores, can be dropped as required. Add a few conditional statements: if the stock hit an all-time high, add sentence X; if a team was shut out, add sentence Y; plus as many more complications as you require for verisimilitude. In effect, you have one master article for a given kind of news, from which, each time it is reused, the inapplicable passages are left out. But the bot did not write any of it; it merely automates the leaving-out process.

    All this is nothing new. The game industry has been doing this stuff for decades. The SimCity games, for instance, began including faked-up news stories of this type in the 1990s.

    To actually comprehend an event and write new coverage from scratch – that is far beyond the capacity of any computer program now existing, or foreseeable from the present state of the art. Unfortunately for the news business, it is also beyond the capacity of a lot of hack journalists.

    When AIs gain the ability to find primary sources, interview eyewitnesses, and tell an interesting or important fact from a dull or trivial one, they can get back to me. In the meantime, all that has been proved is that bad journalism, being imitative, is itself easy to imitate.

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