This paper sought to understand the impact of labelling an argument as AI-generated compared to human-authored, and how factors such as portrayals of expertise and the nature of arguments presented (narrative versus statistical) may affect the persuasiveness of the arguments. Three domains were explored: health, finance, and politics. We show that arguments with AI source labels, both non-expert and expert, were rated by participants as less persuasive than when they had their counterpart human-authored source labels attached. Moreover, although the statistical arguments were found to be more persuasive than the narrative arguments, this did not affect the impact of an AI source label, with a significant interaction effect only being seen for the domain of politics for the expert AI source. The study explored the role of attitude towards AI on the impact of source labels as an exploratory analysis and found no significant interaction effect across the three domains.

In this paper, we explore the Principle of Charity. This is an epistemic assumption that people should not judge people to be irrational unless they have an empirically justified account of what they are doing when they violate normative standards. Through two studies, we provide evidence in support of the principle. Study 1 suggests people believe others will arrive at the same conclusions they would themselves given the same information. Study 2 suggests that people assume others may differ in the subjective degrees of belief but that they broadly use the same (Bayesian) updating mechanism when evaluating information about other people. We believe this paper provides the first empirical test of this principle.