ResearchTrend.AI
  • Papers
  • Communities
  • Events
  • Blog
  • Pricing
Papers
Communities
Social Events
Terms and Conditions
Pricing
Parameter LabParameter LabTwitterGitHubLinkedInBlueskyYoutube

© 2025 ResearchTrend.AI, All rights reserved.

  1. Home
  2. Papers
  3. 2004.01840
  4. Cited By
Abstracting Fairness: Oracles, Metrics, and Interpretability

Abstracting Fairness: Oracles, Metrics, and Interpretability

4 April 2020
Cynthia Dwork
Christina Ilvento
G. Rothblum
Pragya Sur
    FaML
ArXivPDFHTML

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

2 / 2 papers shown
Title
Learning Adversarially Fair and Transferable Representations
Learning Adversarially Fair and Transferable Representations
David Madras
Elliot Creager
T. Pitassi
R. Zemel
FaML
233
674
0
17 Feb 2018
Fair prediction with disparate impact: A study of bias in recidivism
  prediction instruments
Fair prediction with disparate impact: A study of bias in recidivism prediction instruments
Alexandra Chouldechova
FaML
207
2,090
0
24 Oct 2016
1