Algorithmic Emotion, Personal Data, and Informational Judgment

It is no secret in consumer marketing literature that people project personalities to the machines they interact with to elicit intimate emotions. Importantly, the fact that proprietary algorithms are ‘black box’ machines likely leaves emotional gut feelings to be the means to which to resort in evaluating algorithmic outputs. This study advances a thesis that emotion is an intuitive basis for guiding informational judgment—with algorithms exacerbating human susceptibility and possibly leaving people vulnerable to automated decisions and their potential bias. Three-related studies were conducted, using subsamples of U.S. population survey to investigate the dynamics of emotional correlates associated with people’s exposure to algorithm (Study 1 and Study 2) and its consequences (Study 3). Study 1 found the exposure to algorithm via personal data endorsement was significantly associated with emotional traits, with their divergent functions to people’s reception of algorithm. Study 2 replicated these findings in the contexts of (1) Facebook algorithm and (2) stand-alone AI (legal, financial, and employment decisions). Study 3 found the consequences of affirmative algorithmic reception in its contribution to the way people perceive the accuracy of information represented in algorithm-based social media. This study’s proposition is that in dealing with personal data demand, constant rewards of automatic gains of access, and use make digital consumption susceptible. Evidence suggests that cognitive burden may ease out, as emotion is encouraged to reign as a prime source of judgment discouraging a user to switch off, reject, or critically receive algorithm-mediated information.