No abstract

Much of the recent work in psychology (and affective science) has shown that humans regulate their emotions nearly constantly, sometimes well and sometimes poorly. I argue that properly regulating one's emotions displays emotional rationality, and failing to do so displays emotional irrationality. If an agent feels an emotion that is obviously problematic for the agent to feel and she is aware that it is problematic, then the agent ought to regulate her emotions in future similar situations. To capture this aspect of emotional rationality, I introduce the concept of imprudence, which is meant to capture a familiar way that we assess each other's emotions, despite the fact that it has yet to be a factor in the literature on emotions in philosophy, psychology, or affective science.

Artificial Intelligence (AI) pervades humanity in 2022, and it is notoriously difficult to understand how certain aspects of it work. There is a movement—Explainable Artificial Intelligence (XAI)—to develop new methods for explaining the behaviours of AI systems. We aim to highlight one important philosophical significance of XAI—it has a role to play in the elimination of vagueness. To show this, consider that the use of AI in what has been labeled surveillance capitalism has resulted in humans quickly gaining the capability to identify and classify most of the occasions in which languages are used. We show that the knowability of this information is incompatible with what a certain theory of vagueness—epistemicism—says about vagueness. We argue that one way the epistemicist could respond to this threat is to claim that this process brought about the end of vagueness. However, we suggest an alternative interpretation, namely that epistemicism is false, but there is a weaker doctrine we dub technological epistemicism, which is the view that vagueness is due to ignorance of linguistic usage, but the ignorance can be overcome. The idea is that knowing more of the relevant data and how to process it enables us to know the semantic values of our words and sentences with higher confidence and precision. Finally, we argue that humans are probably not going to believe what future AI algorithms tell us about the sharp boundaries of our vague words unless the AI involved can be explained in terms understandable by humans. That is, if people are going to accept that AI can tell them about the sharp boundaries of the meanings of their words, then it is going to have to be XAI.