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Final Assignment for JOUR 3751 at UMN
Introduction Before the rise of digital media in journalism, humans determined what was newsworthy using their prior knowledge and investigative research, constrained by their physical and mental capacity to write articles before a deadline, and based on their experience during their lifetime or the shared experiences and values of a newsroom. As digital media became pervasive during the 20th and 21st centuries, news has benefited from the inputs of computer algorithms that can write, research, and moderate at least as efficiently as the human journalists on which the computer scientists have based their designs. Three aspects of news production have changed: article writing has been enhanced by machine learning algorithms, computer science has expanded the capacity of news organizations to leverage research, and finally, technology has improved the efficiency and civility of audience-provided feedback to the news organization. The importance of these technological changes has been measured economically through standard metrics like unique visitors, illustrated by a case study of Le Monde, below. Though the value of other improvements, such as greater gender inclusivity and civility, have been difficult to measure they are equally important to society. Finally, optimization of basic journalistic processes by digital technology led to more efficient newsrooms that produce more content so that they can be relevant to a wider audience. News production before digital media Before digital media, local beat writers would cover one sports team or one sport in their area, speaking to athletes and taking notes on a pad or recording device that required transcription, both of which required additional time to reorganize before someone could begin to write a story. News organizations were constrained by their human resources, for example, “AI tools can help journalists tell new kinds of stories that were previously too resource-impractical or technically out of reach” (Hansen et al., 2017). Physical proximity to the subjects of their stories also constrained the organizations. The financial costs of travel, in addition to the travel time that was not conducive to productive writing, have been mitigated by technology. Once someone had written a story during the era before digital media, it would be given to another person in the newsroom who focused on production tasks, such as layout, bylines, a headline, and images. These copy editors were experts at writing headlines based on a formula of engagement so that people would buy and subscribe to newspapers. They had a knack for selecting captivating images, too. Unfortunately, “Copy editors have been sacrificed more than any other newsroom category” (Beaujon, 2013). Algorithms are most effective when replacing human activities that are based on a “knack” because those human skills tend to use factors that can be captured and built into a computer model. While much of the attention on artificial intelligence in the newsroom has been focused on the bias within the algorithms, it is important to understand the context of bias in newsrooms before digital media, which continues today. News production has been overwhelmingly white, and predominantly male, and remains this way today according to a Women’s Media Center analysis of the “Five Big Sunday Shows” in 2020 in which “findings confirm many years of research… as ample evidence shows, the news is fundamentally male” (Byerly, 2021). This article also refers to a study by Gaye Tuchman from over 40 years ago that quantified gender representation in news organizations as overwhelmingly white male. Perspectives offered by the news before digital media tended to follow the ideology of the writers and production staff, who were white, and though the statistics indicate that minority and female representation has not changed with the demographic changes in the United States, technology is beginning to offer insights about inclusivity and more in-depth research from more points of view. I believe that the first step toward being inclusive is being open to feedback. We are imperfect beings who benefit from listening to others’ viewpoints. In the past, the audience of journalism wrote letters to the newspaper. Large newsrooms invested in mailroom headcount to receive and sort, then route the opinions and feedback of their readers to the parts of the organization that produced a particular section. I experienced a world that required a visit to the post office or a visit from the postman, plus a few days for a letter to travel to the news organization, then a day or more for the letter to be open, read, and sorted. The feedback loop between audience and news producer required a week or longer. Reacting to feedback about a story was a slow process in most circumstances. Also, rude or nasty letters could be sorted into a separate basket that was routed differently, to avoid offending the writer and/or the producers. The feedback loop has been one of the biggest changes in digital media. Effect of technology in the age of digital media Many major news organizations maintain a robot writer on staff. “Cyborg… accounts for an estimated one-third of the content published by Bloomberg News” (Contributor, 2019) and “Bertie is part of a broader focus on using artificial intelligence to make publishing more efficient for Forbes staff” (Willens, 2019). Those are just two examples of many algorithms that are writing the content for news publishers. These companies maintain a robot writer, not a staff of robot writers, to supplement the production of their human writers. “The AP estimated that it’s freed up 20 percent of reporters’ time spent covering corporate earnings and that AI is also moving the needle on accuracy” (Moses, 2017). I think the coolest news writing algorithm is GPT-3 that, or who (depending on bias toward machine learning algorithms), writes for The Guardian. Following a recent article that GPT-3 titled “A robot wrote this entire article. Are you scared yet, human?” the editor provided a brief recap of the process they used: “prompts were written by The Guardian… GPT-3 produced eight different outputs… Each was unique, interesting, and advanced a different argument.” (Editor, 2020) Like they would have done for a human writer, the editor picked “the best parts of each… Editing GPT-3’s op-end was no different to editing a human op-ed. Overall, it took less time to edit than many human op-eds.” Efficiency has been a huge benefit of algorithms, as well as several others that will be discussed later. These Robot Writers reduce the time needed to write the article, as the AP noted, and they are more accurate because computers are parameter-based technology that struggles with intuitive decision-making. They stay on topic. The second writing technology reduces the time to write headlines and choose images, “reinforcement learning can also be applied to optimize publishing; for example, to help choose the best headlines or thumbnails for a particular story.” (Marconi, 2020). The methodology of good headlines was based on the experience of copy editors, which has been recreated in machine learning algorithms that are reactive to feedback and produce more options, more quickly (Clark, 2019). A case study of the UK press association reported, “journalists have developed templates for particular topics and use automation to create multiple variations” (Marconi, 2020) which are presented to editors as suggested headlines, images, and entire stories, just like GPT-3. After discussing two good examples of technological efficiency, let’s examine how algorithms can affect inclusivity and gender bias. In a predominantly white male news organization, connecting stories to female and minority points of view can be difficult. Algorithms are being used by the Financial Times to track personal pronoun usage. “As reporters write their piece, the bot will alert them of any imbalance in gender ratios” and the same technology is being used to monitor images to ensure there is gender equity in coverage, “based on ‘research showing a positive correlation between stories including quotes of women and higher rates of engagement with female readers’” (Marconi, 2020). Human writers are the arbiter of this algorithmic feedback, choosing to adjust their story based on suggestions, or not, but the correlation to engagement translates to marketing revenue and higher-quality work, so it is likely that the technology will continue to be deployed. The computer processing power and databases required to support these on-demand suggestions are immense, but the infrastructure exists already. Known as neural networks because their complexity is like “the way neurons are wired in the brain” (Hutson, 2021), these algorithms can browse massive datasets in milliseconds to find connections within the data, connections based on templates and prompts created by newsrooms. “Content and news organizations are making increasing use of AI systems to uncover data from multiple sources and automatically summarize them into articles or supporting research for those articles” (Schmelzer, 2019). A case study of the 2015 French election coverage by Le Monde explains how the news organization beat a major competitor “by having more published stories online” (Marconi, 2020) increasing page views, which is also happening in the Real Estate- and Sports-writing industries (Duncan, 2020). Quality content that is “automatically generated provides a net value for us – and our readers” has increased conversions of new paying subscribers (Kalim, 2021) just like coverage of corporate earnings. Finally, the most obvious new technology is the Comment feature in digital media. This pervasive and ubiquitous technology enables an audience to give feedback immediately, not a week later, and anyone can access their feedback. Algorithms are being used to moderate the comments, group comments for human review, and score comments for civility. The New York Times (NYT) uses a tool called Moderator that has allowed them to open more stories for comments (Etim, 2016; Salganik & Lee, 2020). Not just an efficiency tool, the “main goal is to create a safe space for discussions” (Kovalyova, 2021) that drives engagement with readers. Internally, these moderator tools are used to fact-check reporters, too, because both processes depend on accuracy. Collaboration, a strength of networked journalism, improves the entire news-gathering process from story research and writing to the comments posted by audiences. Personal Reflection and Conclusion The ethical obligations of organizations that use algorithms have been altered by digital media. Bylines include references to the robot reporters based on suggested headlines and images. More news content is being written as audience participation escalates through comments. I appreciate the benefits of efficiency and inclusivity in the form of more articles that offer a broader perspective, even if the writer is a machine, or a machine influences the writer. Egalitarian systems that allow everyone to post feedback improve the user experience of journalism by eliminating the delays created by snail mail and by forcing news organizations to address alternative opinions that are posted online. Many of these systems exist in “black boxes” though, so only a privileged few understand the inputs and outputs of the algorithms. Transparency is being prioritized by organizations like NYT and my hope is that this open-source approach continues. I think it will continue because the business models measure the financial benefits through audience engagement. I believe that algorithms will save us in the end, because they can overcome the limits of the human mind. Only an algorithm can easily track our use of personal pronouns in our articles, and mention when we’re off track. News writing, research, and audience participation have been changed by digital media. The slow pace of writing in the past is exponentially faster using algorithms. News producers’ dependence on “the use of algorithms to automatically generate news from structured data has shaken up the journalism industry” (Graefe, 2016). Disruption of old enterprises is a good thing, though we need to carefully monitor ethical concerns. News is being optimized by computer technology, but it is also being optimized by greater audience participation, the inclusion of more gender and minority perspectives, and giving human writers more time to reflect and revise. References
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