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Our study highlights specific ways in which race and gender create inequality in the workplace. Using in-depth interviews with 67 biology PhD students, we show how engagement with research and service varies by both gender and race. By considering the intersection between gender and race, we find not only that women biology graduate students do more service than men, but also that racial and ethnic minority men do more service than white men. White men benefit from a combination of racial and gender privilege, which places them in the most advantaged position with respect to protected research time and opportunities to build collaborations and networks beyond their labs. Racial/ethnic minority women emerge as uniquely disadvantaged in terms of their experiences relative to other groups. These findings illuminate how gendered organizations are also racialized, producing distinct experiences for women and men from different racial groups, and thus contribute to theorizing the intersectional nature of inequality in the workplace.
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Should Algorithms that Predict Recidivism Have Access to Race?
Abstract Recent studies have shown that recidivism scoring algorithms like COMPAS have significant racial bias: Black defendants are roughly twice as likely as white defendants to be mistakenly classified as medium- or high-risk. This has led some to call for abolishing COMPAS. But many others have argued that algorithms should instead be given access to a defendant's race, which, perhaps counterintuitively, is likely to improve outcomes. This approach can involve either establishing race-sensitive risk thresholds, or distinct racial ‘tracks’. Is there a moral difference between these two approaches? We first consider Deborah Hellman's view that the use of distinct racial tracks (but not distinct thresholds) does not constitute disparate treatment since the effects on individuals are indirect and does not rely on a racial generalization. We argue that this is mistaken: the use of different racial tracks seems both to have direct effects on and to rely on a racial generalization. We then offer an alternative understanding of the distinction between these two approaches—namely, that the use of different cut points is to the counterfactual comparative disadvantage, ex ante, of all white defendants, while the use of different racial tracks can in principle be to the advantage of all groups, though some defendants in both groups will fare worse. Does this mean that the use of cut points is impermissible? Ultimately, we argue, while there are reasons to be skeptical of the use of distinct cut points, it is an open question whether these reasons suffice to make a difference to their moral permissibility.
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- Award ID(s):
- 1917712
- PAR ID:
- 10479879
- Publisher / Repository:
- Board of Trustees of the University of Illinois
- Date Published:
- Journal Name:
- American Philosophical Quarterly
- Volume:
- 60
- Issue:
- 2
- ISSN:
- 0003-0481
- Page Range / eLocation ID:
- 205 to 220
- Subject(s) / Keyword(s):
- Recidivism fair machine learning discrimination
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.5406/21521123.60.2.07
