Moral Obligation and Epistemic Risk

This chapter concerns pernicious predictive inferences: taking someone to be likely to possess a socially disvalued trait based on statistical information about the prevalence of that trait within a social group to which she belongs. Some scholars have argued that pernicious predictive inferences are morally prohibited, but are sometimes epistemically required, leaving us with a tragic conflict between the requirements of epistemic rationality and those of morality. Others have responded by arguing that pernicious predictive inferences are sometimes epistemically prohibited. The present chapter takes a different approach, considering the sort of reluctance to draw pernicious predictive inferences that seems morally praiseworthy and vindicating its epistemic status. We argue that, even on a simple, orthodox Bayesian picture of the requirements of epistemic rationality, agents must consider the costs of error—including the associated moral and political costs—when forming and revising their credences. Our attitudes toward the costs of error determine how “risky” different credences are for us, and our epistemic states are justified in part by our attitudes toward epistemic risk. Thus, reluctance to draw pernicious predictive inferences need not be epistemically irrational, and the apparent conflict between morality and epistemic rationality is typically illusory.

The relationship between reading literary novels and predictive inference generation

This study examined the relationship between reading literary novels and generating predictive inferences by analyzing a corpus of Japanese novels. Latent semantic analysis (LSA) was used to capture the statistical structure of the corpus. Then, the authors asked 74 Japanese college students to generate predictive inferences (e.g., “The newspaper burned”) in response to Japanese event sentences (e.g., “A newspaper fell into a bonfire”) and obtained more than 5,000 predicted events. The analysis showed a significant relationship between LSA similarity between the event sentences and the predicted events and frequency of the predicted events. This result suggests that exposure to literary works may help develop readers’ inference generation skills. In addition, two vector operation methods for sentence vector constructions from word vectors were compared: the “Average” method and the “Predication Algorithm” method (Kintsch, 2001). The results support the superiority of the Predication Algorithm method over the Average method.