In this paper, I address the theory of excuse, or more precisely, exculpatory excuse, and the question of what it is that justifies the category of excuse. I address different potential grounds for the law of excuse, which are often run together in ways that confound rather than clarify, focusing on the role of blamelessness and unfairness of expectations in the theory of excuse.

When blame is understood to be emotion-based or affective, its emotional tone is standardly identified as one of anger. We argue that this conception of affective blame is overly restrictive. By attending to cases of blame that emerge against a background of a particular kind of hope invested in others, we identify a blaming response characterized not by anger but by sadness: reactive disappointment. We develop an account of reactive disappointment as affective blame, maintaining that while angry blame and disappointed (...) blame are both condemnatory responses, they have distinct evaluative foci and occupy different but complementary roles in our accountability practices. (shrink)

This work offers a systematic interpretation of the problematics of disinterest in the Critique of Aesthetic Judgment. It examines Kant's thesis that the judgement of taste is free of considerations of morality or utility. Dahan Fan carefully traces Kant's differentiation between the agreeable, the good, and the beautiful, thus casting the relationship between the interests of reason and aesthetic disinterest in a new light.

The use of artificial intelligence (AI) to aid legal decision making has become prominent. This paper investigates the use of AI in a critical issue in employment law, the determination of a worker’s status—employee vs. independent contractor—in two common law countries (the U.S. and Canada). This legal question has been a contentious labor issue insofar as independent contractors are not eligible for the same benefits as employees. It has become an important societal issue due to the ubiquity of the gig (...) economy and the recent disruptions in employment arrangements. To address this problem, we collected, annotated, and structured the data for all Canadian and Californian court cases related to this legal question between 2002 and 2021, resulting in 538 Canadian cases and 217 U.S. cases. In contrast to legal literature focusing on complex and correlated characteristics of the employment relationship, our statistical analyses of the data show very strong correlations between the worker’s status and a small subset of quantifiable characteristics of the employment relationship. In fact, despite the variety of situations in the case law, we show that simple, off-the-shelf AI models classify the cases with an out-of-sample accuracy of more than 90%. Interestingly, the analysis of misclassified cases reveals consistent misclassification patterns by most algorithms. Legal analyses of these cases led us to identify how equity is ensured by judges in ambiguous situations. Finally, our findings have practical implications for access to legal advice and justice. We deployed our AI model via the open-access platform, https://MyOpenCourt.org/, to help users answer employment legal questions. This platform has already assisted many Canadian users, and we hope it will help democratize access to legal advice to large crowds. (shrink)