Companies worldwide are developing software solutions for generating automated news.
Leading media companies such as the Associated Press, Forbes, The New York Times, Los Angeles Times, and ProPublica have started to automate news content.
Although the technology is still in an early market phase, automated journalism has arrived in newsrooms and is likely here to stay.
Conditions and drivers
Automated journalism is most useful in generating routine news stories for repetitive topics for which clean, accurate, and structured data are available.
Automated journalism cannot be used to cover topics for which no structured data are available and is challenging when data quality is poor.
The key drivers of automated journalism are an ever-increasing availability of structured data, as well as news organizations’ aim to both cut costs and increase the quantity of news.
Algorithms are able to generate news faster, at a larger scale, and potentially with fewer errors than human journalists.
Algorithms can use the same data to tell stories in multiple languages and from different angles, thus personalizing them to an individual reader’s preferences.
Algorithms have the potential to generate news on demand by creating stories in response to users’ questions about the data.
Algorithms rely on data and assumptions, both of which are subject to biases and errors. As a result, algorithms could produce outcomes that were unexpected, unintended, and contain errors.
Algorithms cannot ask questions, explain new phenomena, or establish causality and are thus limited in their ability to observe society and to fulfill journalistic tasks, such as orientation and public opinion formation.
The writing quality of automated news is inferior to human writing but likely to improve, especially as natural language generation technology advances.