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.
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.
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 labeled as being created or assisted by these models.
Robot reporters are built to produce large quantities of information at quicker speeds. The Associated Press announced that their use of automation has increased the volume of earnings reports from customers by more than ten times. 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.
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.
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.
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.
Among the concerns about automation is the loss of employment for journalists as publishers switch to using AIs. The use of automation has become a near necessity in newsrooms nowadays, in order to keep up with the ever-increasing demand for news stories, which in turn has affected the very nature of the journalistic profession. In 2014, an annual census from The American Society of News Editors announced that the newspaper industry lost 3,800 full-time, professional editors. Falling by more than 10% within a year, this is the biggest drop since the industry cut over 10,000 jobs in 2007 and 2008.
There has been a significant amount of recent scholarship on the relationship between platform companies, such as Google and Facebook, and the news industry with researchers examining the impact of these platforms on the distribution and monetization of news content, as well as the implications for journalism and democracy. Some scholars have extended this line of thinking to automated journalism and the use of AI in the news. A 2022 paper by the Oxford University academic Felix Simon, for example, argues that the concentration of AI tools and infrastructure in the hands of a few major technology companies, such as Google, Microsoft, and Amazon Web Services, is a significant issue for the news industry, as it risks shifting more control to these companies and increasing the industry's dependence on them. Simon argues that this could lead to vendor lock-in, where news organizations become structurally dependent on AI provided by these companies and are unable to switch to another vendor without incurring significant costs. The companies also possess artefactual and contractual control over their AI infrastructure and services, which could expose news organizations to the risk of unforeseen changes or the stopping of their AI solutions entirely. Additionally, the author argues the reliance on these companies for AI can make it more difficult for news organizations to understand the decisions or predictions made by the systems and can limit their ability to protect sources or proprietary business information.
Other outlets that have published articles whose content or byline have been confirmed or suspected to be created by generative AI models – often with false content, errors, or non-disclosure of generative AI use – include:
In May 2024, Futurism noted that a content management system video by AdVon Commerce, who had used generative AI to produce articles for many of the aforementioned outlets, appeared to show that they "had produced tens of thousands of articles for more than 150 publishers."
In response to potential pitfalls around the use and misuse of generative AI in journalism and worries about declining audience trust, outlets around the world, including publications such as Wired, Associated Press, The Quint, Rappler or The Guardian have published guidelines around how they plan to use and not use AI and generative AI in their work.
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