Automated journalism, also known as algorithmic journalism or robot journalism, describes modern technological processes that are now in use in the journalistic profession, such as news articles and videos generated by computer programs. There are four main fields of application for automated journalism, namely automated content production, data mining, news dissemination and content optimization. Through generative AI, stories are produced automatically by computers rather than human reporters. In the 2020s, generative pre-trained transformers have enabled the generation and summarization of large amounts of information at scale. Increasingly, artificial intelligence (AI) is being used to optimize workflows across newsrooms, including drafting, editing, summarization, research and content optimization. Recent research has focused on expanding the topic of automated journalism to include AI-assisted newsroom workflows that go beyond the idea of content creation.
While automated journalism is seen by advocates as efficient and an opportunity to free journalists from routine reporting in sectors including finance, sports, and election coverage, critics raise concerns about what its use means for transparency, accountability, employment and editorial responsibility in the profession. As AI-assisted systems integrate further into newsroom operations, scholars are focused on how their use will impact journalistic ethics, governance, platform dependency, and public trust.
History Historically, the process involved an algorithm that scanned large amounts of provided data, selected from an assortment of pre-programmed article structures, ordered key points, and inserted details such as names, places, amounts, rankings, statistics, and other figures. These programs interpret, organize, and present data in human-readable ways. The output can also be customized to fit a certain voice, tone, or style. Early implementations were mainly used for stories based on statistics and numerical figures. Common topics include sports recaps, weather, financial reports, real estate analysis, and earnings reviews.
Data science and AI companies such as Automated Insights, Narrative Science, United Robots and Monok develop and provide these algorithms to news outlets. In 2016, early adopters included news providers such as the Associated Press, Forbes, ProPublica, and the Los Angeles Times.
StatSheet, an online platform covering college basketball, runs entirely on an automated program. In 2006, Thomson Reuters announced their switch to automation to generate financial news stories on its online news platform. Reuters used a tool called Tracer. An algorithm called Quakebot published a story about a 2014 California earthquake on The Los Angeles Times website within three minutes after the shaking had stopped.
The Associated Press began using automation to cover 10,000 minor baseball leagues games annually, using a program from Automated Insights and statistics from MLB Advanced Media. Outside of sports, the Associated Press also uses automation to produce stories on corporate earnings. Since 2014, Associated Press has been publishing quarterly financial stories with help from Automated Insights.
In May 2020, Microsoft announced that a number of its MSN contract journalists would be replaced by robot journalism. On 8 September 2020, The Guardian published an article entirely written by the neural network GPT-3, although the published fragments were manually picked by a human editor. Agentic Tribune produces all of its news articles automatically using AI.
News broadcasters in Kuwait, Greece, South Korea, India, China and Taiwan have presented news with anchors based on generative AI models, prompting concerns about job losses for human anchors and audience trust in news that has historically been influenced by parasocial relationships with broadcasters, content creators or social media influencers. Algorithmically generated anchors have also been used by allies of ISIS for their broadcasts.
In 2023, Google reportedly pitched a tool to news outlets that claimed to "produce news stories" based on input data provided, such as "details of current events". Some news company executives who viewed the pitch described it as "[taking] for granted the effort that went into producing accurate and artful news stories."
In February 2024, Google launched a program to pay small publishers to write three articles per day using a beta generative AI model. The program does not require the knowledge or consent of the websites that the publishers are using as sources, nor does it require the published articles to be labelled as being created or assisted by these models.
Meta AI, a chatbot based on Llama 3 which summarizes news stories, was noted by The Washington Post to copy sentences from those stories without direct attribution and to potentially further decrease the traffic of online news outlets.
The growing presence of generative AI in the 2020s has caused automated journalism to evolve from its origins in rule-based content generation. Recent studies describe a broader and more comprehensive transformation of newsroom workflows, with AI tools increasingly embedded into research, editing and content production processes.
Generative AI and AI-assisted journalism Recent developments in Generative AI have expanded the concept of automated journalism beyond the idea of news stories being generated from structured data. While earlier systems were designed to create routine news reports from structured datasets, newer AI tools are embedded within, and designed to assist, a wide range of newsroom activities.
In research, identified use cases for modern artificial intelligence systems have included newsroom tasks like drafting articles, summarizing documents, generating headlines, conducting research and being used to optimize publication schedules. In many of these cases, AI tools are used alongside human journalists and not as fully autonomous systems.
A 2024 bibliometric analysis of artificial intelligence and journalism research has identified that scholarship on the topic has increasingly shifted to include topics like: transparency, accountability, ethics, misinformation, newsroom transformation and human-AI collaboration.
Benefits
Speed Robot reporters are built to produce large quantities of information more quickly. The Associated Press announced that their use of automation has increased earnings reported customers more than tenfold. With software from Automated Insights and data from other companies, they can produce 150 to 300-word articles in the same time it takes journalists to crunch numbers and prepare information. By automating routine stories and tasks, journalists are promised more time for complex jobs such as investigative reporting and in-depth analysis of events.
Francesco Marconi of the Associated Press stated that, through automation, the news agency freed up 20 percent of reporters’ time to focus on higher-impact projects. This has also been stated by a spokesperson at Gannett, who stated "By leveraging AI, we are able to expand coverage and enable our journalists to focus on more in-depth reporting."
GBH reports that AI tools help increase the reach of news publishers. Mike Carragi, a product manager at Patch, stated that they were able to increase their reach from 1200 communities to 7000 communities in just a few months without the need for new employees solely through the adoption of generative AI. In fact, many communities are served solely by AI-generated content, which creates summaries of existing information within the community.
Cost Automated journalism is cheaper because more content can be produced within less time. It also lowers labour costs for news organizations. Reduced human input means less expenses on wages or salaries, paid leaves, vacations, and employment insurance. Automation serves as a cost-cutting tool for news outlets struggling with tight budgets but still wish to maintain the scope and quality of their coverage.
Quality and accuracy Some research argues that artificial intelligence improves journalistic quality through better verification, fact checking and transparency support mechanisms.
Emerging developments Some experimental journalism models incorporate community validation processes in conjunction with AI-generated content. This allows members of the public to review, verify and contribute to the findings or original content generated by these models before publication.
Concerns
Authorship In an automated story, there is often confusion about who should be credited as the author. Several participants of a study on algorithmic authorship attributed the credit to the programmer; others perceived the news organization as the author, emphasizing the collaborative nature of the work. There is also no way for the reader to verify whether an article was written by a robot or human, which raises issues of transparency although such issues also arise with respect to authorship attribution between human authors too.
Research has increasingly focused on the importance of human oversight in AI-assisted journalism. While AI systems contribute to content creation at scale, responsibility and editorial decision-making is still generally considered to remain with journalists or the news organizations.
Credibility and quality Concerns about the perceived credibility of automated news is similar to concerns about the perceived credibility of news in general. Critics doubt if algorithms are "fair and accurate, free from subjectivity, error, or attempted influence." Again, these issues about fairness, accuracy, subjectivity, error, and attempts at influence or propaganda has also been present in articles written by humans over thousands of years. A common criticism is that machines do not replace human capabilities such as creativity, humour, and critical-thinking. However, as the technology evolves, the aim is to mimic human characteristics. When the UK's Guardian newspaper used an AI to write an entire article in September 2020, commentators pointed out that the AI still relied on human editorial content. Austin Tanney, the head of AI at Kainos said: "The Guardian got three or four different articles and spliced them together. They also gave it the opening paragraph. It doesn’t belittle what it is. It was written by AI, but there was human editorial on that."
The largest single study of readers' evaluations of news articles produced with and without the help of automation exposed 3,135 online news consumers to 24 articles. It found articles that had been automated were significantly less comprehensible, in part because they were considered to contain too many numbers. However, the automated articles were evaluated equally on other criteria including tone, narrative flow, and narrative structure.
Beyond human evaluation, there are now numerous algorithmic methods to identify machine written articles although some articles may still contain errors that are obvious for a human to identify, they can at times score better with these automatic identifiers than human-written articles.
A 2017 Nieman Reports article by Nicola Bruno discusses whether or not machines will replace journalists and addresses concerns around the concept of automated journalism practices. Ultimately, Bruno came to the conclusion that AI would assist journalists, not replace them. "No automated software or amateur reporter will ever replace a good journalist", he said.