Authors
Boris Babic
University of Toronto, St. George Campus
Abstract
Many philosophers have argued that statistical evidence regarding group char- acteristics (particularly stereotypical ones) can create normative conflicts between the requirements of epistemic rationality and our moral obligations to each other. In a recent paper, Johnson-King and Babic argue that such conflicts can usually be avoided: what ordinary morality requires, they argue, epistemic rationality permits. In this paper, we show that as data gets large, Johnson-King and Babic’s approach becomes less plausible. More constructively, we build on their project and develop a generalized model of reasoning about stereotypes under which one can indeed avoid normative conflicts, even in a big data world, when data contain some noise. In doing so, we also articulate a general approach to rational belief updating for noisy data.
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DOI 10.1086/715196
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References found in this work BETA

Accuracy and the Laws of Credence.Richard Pettigrew - 2016 - Oxford University Press UK.
Doxastic Wronging.Rima Basu & Mark Schroeder - 2019 - In Brian Kim & Matthew McGrath (eds.), Pragmatic Encroachment in Epistemology. Routledge. pp. 181-205.
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On the Epistemic Costs of Implicit Bias.Tamar Szabó Gendler - 2011 - Philosophical Studies 156 (1):33-63.

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