Particularism and Generalism: How AI can Help us to Better Understand Moral Cognition
نویسنده
چکیده
Particularism and Generalism refer to families of attitudes towards moral principles. This paper explores the suggestion that neural network models of cognition may aid in vindicating particularist views of moral reasoning. Neural network models of moral case classification are presented, and the contrast case method for testing and revising case classifications is considered. It is concluded that while particularism may have some legitimate insights, it may underestimate the importance of the role played by certain kinds of moral principles. 1. Particularism and Generalism Much ink has been spilled on the nature of moral reasons. Some philosophers have defended Particularism, while others have defended Generalism. These terms can be misleading since there are a number of different theses that appear to go under the heading of ‘Particularism.’ Since Generalists are taken as denying what Particularists claim, getting clear on some of the different positions referred to as particularistic will also help us to clarify some possible generalist commitments. The first part of this paper will clarify some of the different positions that have come under the heading of Particularism. Part Two will raise some questions for particularism and present two artificial neural network models in an attempt to explore what possible answers might be to those questions. Part Three will discuss how the construction of such models may lead to insights that require us to move beyond the boundaries of the Particularism-Generalism debate, leading us to a better understanding of the space of possibilities for the nature of moral reasons. Part Four will discuss the relationship between this work and other work.
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