Concerns over fake news have triggered a renewed interest in various forms of media literacy. Prevailing expectations posit that literacy interventions help audiences to be “inoculated” against any harmful effects of misleading information. This study empirically investigates such assumptions by assessing whether individuals with greater literacy (media, information, news, and digital literacies) are better at recognizing fake news, and which of these literacies are most relevant. The results reveal that information literacy—but not other literacies—significantly increases the likelihood of identifying fake news stories. Interpreting the results, we provide both conceptual and methodological explanations. Particularly, we raise questions about the self-reported competencies that are commonly used in literacy scales.

AI can make mistakes and cause unfavorable consequences. It is important to know how people react to such AI-driven negative consequences and subsequently evaluate the fairness of AI’s decisions. This study theorizes and empirically tests two psychological mechanisms that explain the process: (a) heuristic expectations of AI’s consistent performance (automation bias) and subsequent frustration of unfulfilled expectations (algorithmic aversion) and (b) heuristic perceptions of AI’s controllability over negative results. Our findings from two experimental studies reveal that these two mechanisms work in an opposite direction. First, participants tend to display more sensitive responses to AI’s inconsistent performance and thus make more punitive assessments of AI’s decision fairness, when compared to responses to human experts. Second, as participants perceive AI has less control over unfavorable outcomes than human experts, they are more tolerant in their assessments of AI.