AI in the newsroom
Each newsroom has a unique set of ways it uses AI. For the first session, participants were asked to reflect on the role that artificial intelligence currently plays in their newsrooms, and the issues they are confronting. Chase Davis, editor of interactive news at The New York Times, highlighted the ongoing promises around the melding of technology and journalism to help reporters find and tell stories that were previously out of reach or impractical.
Areas where AI is particularly helpful can be classified into three categories:
1.Finding needles in haystacks:
In those outlying or special cases that might elude human identification because of the scale or complexity of the data, AI can be a breakthrough tool. This role fits neatly into standard newsroom processes, because even if it discovers cases the human eye could not, the findings can be fact-checked via standard human investigative techniques.
2.Identifying trends ( or departures from trends):
The massive computing power AI can help provide characterizations of aggregates of data, perhaps grouped in time or by geography or demographics. Alternatively, it can quickly identify outlier data.
3.Examining an application of AI or computation as the subject of the story itself:
Because they are built by humans, algorithms harbor human bias—and by examining them, we can discover previously unseen bias. How are these complex truths being found through these tools? What happens when these tools are applied to the operation of our neighborhoods or cities or nation?
As suggested by Mark Hansen, we are also starting to see situations in which more advanced journalists are creating analysis methods that essentially cultivate computational sources on a given topic. In those cases, we have new hybrid forms of investigation and writing where the story and the mechanistic technique share the spotlight.
A Spectrum of Autonomy
The incorporation of AI into the newsroom has led to a significant breakthrough in the abilities of reporters to act as amateur data scientist. AI can augment the human reporter in several ways:
i)helping to classify and categorize documents,
ii)identify outliers in data worthy of closer examination
iii)find needles in the haystacks of data.
iv)keeping an experienced human in the loop with real news judgment was frequently referenced during our discussion as an essential part of working with AI in the newsroom. One panelist pointed out there is a “spectrum of autonomy” with respect to AI: at one end of the spectrum is full autonomy, where no human is in the loop at all, and at the other end, AI can work alongside humans in a much more limited way.
AI can help free writers from having to constantly re-write the same stories over and over to work on more original reporting, as long as humans are helming the operation andverifying outcomes.
Major examples include Los Angeles Times reporters using classifiers to detect instances of the LAPD downgrading crime classifications; The Atlanta Journal-Constitution investigation of sexual abuse by doctors; Reuters’topic modeling to find centers of power among petitioners of the Supreme Court; ProPublica demonstrating how machines learn to be racist; and The New York Times interrogating campaign finance data and using facial recognition to determine who was sitting in the audience at President Trump’s inauguration.
How does technology fit in the news pipeline? As mentioned earlier, AI increasingly assists in reporting, content creation, distribution, and audience interaction, to name a few examples. Recently, crowdsourcing, brainstorming, and fact-checking tools are being developed to aid data information gathering and, particularly, to structure relevant data. Among contemporary newsrooms, automation is a keytool in competing not just against each other for customer attention, but also against large platforms such as Netflix, Facebook, and Amazon. The number of articles written by robots is growing rapidly. Just input the facts and let the algorithm write the paper. Associated press creates more than 3,000 financial reports per quarter. Some estimate that 90 percent of news could be algorithmically generated by mid-2020s
The above chart describes how software and journalists are perceived. As you can see, software totally dominates over the journalists.
An example of the use of artificial intelligence in journalism
Used by the Washington post after it was acquired by Jeff Bezos. The editors there started using Heliograf.
For an area of interest:
i)Hook heliograf up to source of structured data eg election data from votesmart.org
ii)Create narrative templates for stories, including key phrases that account for different potential outcomes
iii)Heliograf identifies relevant data, matches it to corresponding phrases in template, and then publishes different versions across different platforms.
RESULTS (FROM USE OF HELIOGRAF)
In November 2012,
It took four employees 25 hours to compile and post just a fraction of election results manually
In November 2016,
Heliograf created more than 500 articles, with little human intervention, that had more than 500,000 clicks
Use Heliograf to keep data in stories up-to-date
E.g., someone shares a Tuesday story on Thursday, and the facts have changed, Heliograf automatically updates story with most recent facts