Repetitive journalism will soon get automated, leading to both increased productivity and job losses
Nov 3, 2019
The news industry has haemorrhaged jobs in the last decade. In the United States, the number of people employed in newsrooms declined by 25% from 2008 to 2018 — according to Pew’s analysis of the Bureau of Labor Statistics OES survey.
A potential reason for this could be automation. According to the New York Times, “Roughly a third of the content published by Bloomberg News uses some form of automated technology”.
Journalism professor Dan Kennedy recently tweeted, “Hearing news of more layoffs at the @BostonHerald. Meanwhile, the amazing @grlreporter writes that Herald parent @MediaNewsGroup is moving ... covering high school sports with AI. There is no bottom.”
The three paragraphs above were (mostly) written by a bot. They were unearthed and generated by AI that we created at Loki.ai. Given an input of “journalism layoffs automation”, the bot whipped these up in roughly 10 seconds. I did have to manually select a tweet from the 10 it identified (4 of which were unrelated), though.
There will always be demand for humans that do great investigative reporting, nurture relationships with sources, or write well-argued analysis and opinion pieces. But–outside of elite institutions–this represents a tiny fraction of the work done by reporters. As Waseem Zakir of the BBC noted:
You get copy coming in on the wires and reporters churn it out, processing stuff and maybe adding the odd local quote. It’s affecting every newsroom in the country and reporters are becoming churnalists.”
Churnalism is not always a bad thing. When done well, churnalism involves repackaging facts into digestible graphics, adding relevant quotes, and presenting historical information to put facts in context. When done poorly, it reads like a 200-word article with little context and no informational value outside of the headline.
Here’s the deal — machines are infinitely better churnalists than humans are. And when replacing you, they don’t care if you are passionate about your craft, or if you’re a disgruntled intern on her last day of work.
We have spent most of the last 5 years in print, digital, and TV newsrooms in India. Last year, we created auto-updated dashboards and feeds for monitoring issues like industrial growth, and pollution.
More recently, we have created automated articles about the elections, economic indicators, and sports. By next year, we expect to be creating automated news videos (with human-sounding voiceovers).
This is hardly ground-breaking work. Automated Insights and Narrative Science, for instance, are companies that have been doing the same for nearly a decade. We are simply addressing a geographical market (India) where automation has not been widely applied. Soon, we’re sure that others will attempt to do the same.
As barriers to Artificial Intelligence adoption get lower, we believe that automation will continue to come to even more media arenas. Cricinfo currently has some fantastic articles describing the bat vs ball battles in Test cricket. But it’s not inconceivable to train an AI to take in the video feed of a cricket game and create similar articles.
Similarly, it’s not difficult to train an AI to listen to the live stream of a politician’s speech, analyze it for keywords, and summarize the speech. The AI can also put the speech in historical context faster than any human can — for instance, by tracking how mentions of a keyword have changed over time (as we did in the image below).