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

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...

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Bibliographic Details
Published in:Journalism & mass communication quarterly 2019-03, Vol.96 (1), p.82-100
Main Author: Waddell, T. Franklin
Format: Article
Language:English
Subjects:
Algorithms
Anthropomorphism
Attribution
Bias
Credibility
Journalism
Journalists
Mass communications
News
Perceptions
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Summary: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.
ISSN:1077-6990
2161-430X
DOI:10.1177/1077699018815891