Does Polarizing News Become Less Polarizing When Written by an AI?

Abstract: In this study, we examine how readers perceive the credibility of polarizing news purportedly written by a machine. In particular, we study whether a machine attribution can decrease the polarization inflicted by the self-confirmation bias. To that end, we expect that attitude-confirming polarizing news is perceived as less credible when attributed to a machine than when attributed to a human author. We assume this is due to the lower source credibility of machines and less emotional involvement. In a preregistered online experiment, we presented N = 508 participants with a polarizing news article attributed either to a human author or a machine. The article also either confirmed or disconfirmed participants’ attitudes towards the polarizing issue. Our results show that participants did not differentiate between human and machine-attributed news. Moreover, we found no evidence that machine-attributed news affected the self-confirmation bias. However, we found that, while machine authors were perceived equally competent as human authors, they were perceived as less trustworthy. In addition, we found that the machine attribution induced less emotional involvement in terms of experienced enthusiasm but not experienced anger.