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Abstracting Fairness: Oracles, Metrics, and Interpretability

Abstracting Fairness: Oracles, Metrics, and Interpretability

Symposium on Foundations of Responsible Computing (FRC), 2020
4 April 2020
Cynthia Dwork
Christina Ilvento
G. Rothblum
Pragya Sur
    FaML
ArXiv (abs)PDFHTML

Papers citing "Abstracting Fairness: Oracles, Metrics, and Interpretability"

3 / 3 papers shown
Semivalue-based data valuation is arbitrary and gameable
Semivalue-based data valuation is arbitrary and gameable
Hannah Diehl
Ashia C. Wilson
TDI
328
1
0
14 Jun 2025
AIM: Attributing, Interpreting, Mitigating Data Unfairness
AIM: Attributing, Interpreting, Mitigating Data Unfairness
Zhining Liu
Ruizhong Qiu
Zhichen Zeng
Yada Zhu
Hendrik Hamann
Hanghang Tong
FaML
455
12
0
13 Jun 2024
Fair Algorithm Design: Fair and Efficacious Machine Scheduling
Fair Algorithm Design: Fair and Efficacious Machine SchedulingAlgorithmic Game Theory (AGT), 2022
April Niu
Agnes Totschnig
A. Vetta
FaML
182
5
0
13 Apr 2022
1
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