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Why Are Some Statistical Generalizations Epistemically Risky?

Abstract

dc:description.abstract

Moral encroachment theses (MET) operate like pragmatic encroachment theses. When the stakes of belief are high, so are the standards for evidence. This means that evidence which is sufficient in a low stakes-of-belief scenario may be insufficient when the stakes are raised. Simply, METs aim to appeal to the varying moral intuitions that one may have in cases with different moral stakes and build an epistemological difference out of that moral distinction. For example, one might think that in cases of racial profiling, because the moral stakes of belief are high, what would otherwise constitute good evidence for belief is insufficient. However, most METs assume that the probabilistic evidence on which one relies to form their belief is good evidence. Instead of examining the reliability of statistical generalizations, like those used in cases of racial profiling, the moral encroacher focuses on the moral facts of the circumstance of belief formation to explain why the subsequent belief is wrong epistemically. I will focus on Sarah Moss's account because she focuses on cases in which one forms an opinion on the basis of probabilistic evidence. I use Moss's version of the MET as a target to illustrate the challenges METs face in general. Broadly, Moss holds that a judgment's moral risk bears on its epistemic status. In Section 1, I briefly outline Sarah Moss's MET and explain why it fails to identify which cases produce epistemically problematic judgments and fails to explain why those judgments are epistemically problematic. In Section 2, I offer an alternative account, which explains why statistical generalizations about marginalized social groups are likely unreliable as evidence. Thus, use of this kind of evidence leads to epistemically problematic beliefs. I conclude by introducing epistemic risk as an explanation for why the inference made in Shopper is epistemically problematic while the inference made in Fraternity Member is not.

Degree

thesis:*
Name thesis:degree_name
Master of Arts
Level thesis:degree_level
masters
Discipline thesis:degree_discipline
Philosophy
Department dc:contributor.department
Philosophy
Grantor dc:publisher
Virginia Tech
Year dc:date.issued
2023

Author and committee

dc:creator, dc:contributor.*
Author dc:creator
  • Marley, Maeve
Chair dc:contributor.committeechair
  • Parker, Wendy
Committee members dc:contributor.committeemember
  • MacKenzie, Jordan
  • Corredor, Mercedes Maria

Subjects

dc:subject × 4

Rights

dc:rights
Statement dc:rights
  • In Copyright
Language dc:language.iso
en

Identifiers

dc:identifier.*
Dc Identifier Other
vt_gsexam:37046
OAI identifier oai:identifier
oai:vtechworks.lib.vt.edu:10919/114735

Chain of custody

source
Harvested from
Virginia Tech
Base URL
vtechworks.lib.vt.edu/oai/request
Last updated
2026-07-22
Source record
OAI-PMH GetRecord
citation

Marley, Maeve. Why Are Some Statistical Generalizations Epistemically Risky?. masters thesis, Virginia Tech, 2023. http://hdl.handle.net/10919/114735