Decoupled classifiers for fair and efficient machine learning

Decoupled classifiers for fair and efficient machine learning

20 July 2017

Nicole Immorlica

Adam Tauman Kalai

Max D. M. Leiserson

Papers citing "Decoupled classifiers for fair and efficient machine learning"

9 / 9 papers shown

Title
Towards Intersectionality in Machine Learning: Including More Identities, Handling Underrepresentation, and Performing Evaluation Angelina Wang V. V. Ramaswamy Olga Russakovsky FaML 34 92 0 10 May 2022
CONFAIR: Configurable and Interpretable Algorithmic Fairness Ankit Kulshrestha Ilya Safro FaML 22 2 0 17 Nov 2021
Multi-group Agnostic PAC Learnability G. Rothblum G. Yona FaML 44 38 0 20 May 2021
Ensuring Fairness under Prior Probability Shifts Arpita Biswas Suvam Mukherjee OOD 24 33 0 06 May 2020
Multiaccuracy: Black-Box Post-Processing for Fairness in Classification Michael P. Kim Amirata Ghorbani James Zou MLAU 25 336 0 31 May 2018
Probably Approximately Metric-Fair Learning G. Rothblum G. Yona FaML FedML 21 85 0 08 Mar 2018
Does mitigating ML's impact disparity require treatment disparity? Zachary Chase Lipton Alexandra Chouldechova Julian McAuley 37 16 0 19 Nov 2017
From Parity to Preference-based Notions of Fairness in Classification Muhammad Bilal Zafar Isabel Valera Manuel Gomez Rodriguez Krishna P. Gummadi Adrian Weller FaML 27 207 0 30 Jun 2017
Fair prediction with disparate impact: A study of bias in recidivism prediction instruments Alexandra Chouldechova FaML 207 2,092 0 24 Oct 2016