Multi-Target Multiplicity: Flexibility and Fairness in Target Specification under Resource Constraints-Reference-Cited by-同舟云学术

Multi-Target Multiplicity: Flexibility and Fairness in Target Specification under Resource Constraints

Published:2023-06-12 Issue: Volume: Page:
ISSN:
Container-title:2023 ACM Conference on Fairness, Accountability, and Transparency
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Author:

Watson-Daniels Jamelle¹^ORCID,Barocas Solon²^ORCID,Hofman Jake M.²^ORCID,Chouldechova Alexandra³^ORCID

Affiliation:

1. Harvard University, USA

2. Microsoft Research, USA

3. Microsoft Resarch, USA

Funder

National Science Foundation

Ford Foundation

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3593013.3593998

Reference43 articles.

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2. Big Data’s Disparate Impact;Barocas Solon;California Law Review,2016

3. Emily Black , Hadi Elzayn , Alexandra Chouldechova , Jacob Goldin , and Daniel Ho . 2022 . Algorithmic fairness and vertical equity: Income fairness with IRS tax audit models . In 2022 ACM Conference on Fairness, Accountability, and Transparency. 1479–1503 . Emily Black, Hadi Elzayn, Alexandra Chouldechova, Jacob Goldin, and Daniel Ho. 2022. Algorithmic fairness and vertical equity: Income fairness with IRS tax audit models. In 2022 ACM Conference on Fairness, Accountability, and Transparency. 1479–1503.

4. Emily Black and Matt Fredrikson. 2021. Leave-one-out Unfairness. (2021). Emily Black and Matt Fredrikson. 2021. Leave-one-out Unfairness. (2021).

5. Model Multiplicity: Opportunities, Concerns, and Solutions