Can an Algorithm Reduce the Perceived Bias of News? Testing the Effect of Machine Attribution on News Readers’ Evaluations of Bias, Anthropomorphism, and Credibility

Abstract

Although accusations of editorial slant are ubiquitous to the contemporary media environment, recent advances in journalism such as news writing algorithms may hold the potential to reduce readers' perceptions of media bias. Informed by the Modality-Agency-Interactivity-Navigability (MAIN) model and the principle of similarity attraction, an online experiment (n = 612) was conducted to test if news attributed to an automated author is perceived as less biased and more credible than news attributed to a human author. Results reveal that perceptions of bias are attenuated when news is attributed to a journalist and algorithm in tandem, with positive downstream consequences for perceived news credibility.

Towards a Sustainable News Business: Understanding Readers’ Perceptions of Algorithm-Generated News Based on Cultural Conditioning

“…In this case, the HSM assumes that news readers tend to rely on heuristics to determine the reliability of information. Moreover, in addition to mental shortcuts triggered by features of a message like length or source attractiveness [27], the affordances of digital media that operate on the periphery of media can also activate heuristics that influence news readers' evaluations of message credibility [31,32].…”

“…Meanwhile, news readers may prefer human journalists over AI algorithms due to the effect of similarity-attraction [33]. Many studies in social psychology have revealed that individuals tend to prefer others who are similar to themselves based on factors such as appearance, opinion, or personality [31,33]. This principle of human-human communication through similarity-attraction is often generalizable to nonhuman actors [31].…”

“…Many studies in social psychology have revealed that individuals tend to prefer others who are similar to themselves based on factors such as appearance, opinion, or personality [31,33]. This principle of human-human communication through similarity-attraction is often generalizable to nonhuman actors [31]. In other words, the principle of similarity-attraction may cause news readers to have negative perceptions towards machine automation because their preferences for communicators with human attributes (e.g., humanlike appearance) can trigger the similarity-attraction effect [31,34,35].…”

Origin and evolution of the News Finds Me perception: Review of theory and effects

“…Algorithmic news provides the benefit of presenting a coherent and legitimated array of news information to social media users (Carlson, 2018;Gillespie, 2014;Thurman et al, 2019). Additionally, news users perceive machine-generated news to be more informative, accurate and objective than those selected by human journalists (Clerwall, 2014), and automated news can elicit people's machine heuristic where the algorithmic news carries higher credibility and bias-free (Sundar, 2008;Waddell, 2019). Besides, social media covers a wide mix of professionally generated news content and user-generated content (Bode, 2016), satisfying news users' surveillance needs.…”

What’s in a Label? Negative Credibility Labels in Partisan News

“…Even when audiences trust the human journalists at a news organization, a machine byline increases the story's perceived credibility and objectivity (Liu & Wei, 2019). While these studies are about news stories rather than news labels, the perception that machines are an authoritative source and less biased than humans appear pervasive (Jung, Hong, Kim, Im, & Oh, 2017;Liu & Wei, 2019;Waddell, 2019). This suggests that if the audience is told that a credibility label is calculated by an algorithm, audiences will consider that an authoritative cue to the news story's credibility.…”