As The Tow Center for Journalism’s newly released Guide to Automated Journalism points out, the use of algorithms to automatically generate news from structured data is increasingly impacting the journalistic industry. These algorithms can quickly and inexpensively create high volumes of news stories for a given topic. According to Tow, this has fueled journalists’ fears that automated content production will eventually eliminate newsroom jobs, while at the same time scholars and practitioners see the technology’s potential to improve news quality.
Tow’s guide summarizes recent research on the topic to provide overview of the current state of automated journalism, discusses key questions and potential implications of its adoption, and suggest avenues for future research.
For news consumers
- People rate automated news as more credible than human-written news but do not particularly enjoy reading automated content.
- Automated news is currently most suited for topics where providing facts in a quick and efficient way is more important than sophisticated narration, or where news did not exist previously and consumers thus have low expectations regarding the quality of the writing.
- Little is known about news consumers’ demand for algorithmic transparency, such as whether they need (or want) to understand how algorithms work.
For news organizations
- Since algorithms cannot be held accountable for errors, liability for automated content will rest with a natural person (e.g., the journalist or the publisher).
- Algorithmic transparency and accountability will become critical when errors occur, in particular when covering controversial topics and/or personalizing news.
- Apart from basic guidelines that news organizations should follow when automatically generating news, little is known about which information should be made transparent regarding how the algorithms work.