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What is Fair? Exploring Pareto-Efficiency for Fairness Constrained
  Classifiers

What is Fair? Exploring Pareto-Efficiency for Fairness Constrained Classifiers

30 October 2019
Ananth Balashankar
Alyssa Lees
Chris Welty
L. Subramanian
ArXiv (abs)PDFHTML

Papers citing "What is Fair? Exploring Pareto-Efficiency for Fairness Constrained Classifiers"

31 / 31 papers shown
Title
Fairness Without Harm: An Influence-Guided Active Sampling Approach
Fairness Without Harm: An Influence-Guided Active Sampling Approach
Jinlong Pang
Jialu Wang
Zhaowei Zhu
Yuanshun Yao
Chen Qian
Yang Liu
TDI
199
8
0
20 Feb 2024
On Learning Fairness and Accuracy on Multiple Subgroups
On Learning Fairness and Accuracy on Multiple SubgroupsNeural Information Processing Systems (NeurIPS), 2022
Changjian Shui
Gezheng Xu
Qi Chen
Jiaqi Li
Charles Ling
Tal Arbel
Boyu Wang
Christian Gagné
211
48
0
19 Oct 2022
Bias Mitigation for Machine Learning Classifiers: A Comprehensive Survey
Bias Mitigation for Machine Learning Classifiers: A Comprehensive SurveyACM Journal on Responsible Computing (JRC), 2022
Max Hort
Zhenpeng Chen
Jie M. Zhang
Mark Harman
Federica Sarro
FaMLAI4CE
261
230
0
14 Jul 2022
BARACK: Partially Supervised Group Robustness With Guarantees
BARACK: Partially Supervised Group Robustness With Guarantees
N. Sohoni
Maziar Sanjabi
Nicolas Ballas
Aditya Grover
Shaoliang Nie
Hamed Firooz
Christopher Ré
OOD
238
28
0
31 Dec 2021
Scalable Unidirectional Pareto Optimality for Multi-Task Learning with
  Constraints
Scalable Unidirectional Pareto Optimality for Multi-Task Learning with Constraints
Soumyajit Gupta
Gurpreet Singh
Raghu Bollapragada
Matthew Lease
133
7
0
28 Oct 2021
Understanding and Improving Fairness-Accuracy Trade-offs in Multi-Task
  Learning
Understanding and Improving Fairness-Accuracy Trade-offs in Multi-Task LearningKnowledge Discovery and Data Mining (KDD), 2021
Yuyan Wang
Xuezhi Wang
Alex Beutel
Flavien Prost
Jilin Chen
Ed H. Chi
FaML
138
57
0
04 Jun 2021
Pareto Efficient Fairness in Supervised Learning: From Extraction to
  Tracing
Pareto Efficient Fairness in Supervised Learning: From Extraction to Tracing
Mohammad Mahdi Kamani
R. Forsati
Chao Guo
M. Mahdavi
FaML
175
12
0
04 Apr 2021
A Hybrid 2-stage Neural Optimization for Pareto Front Extraction
A Hybrid 2-stage Neural Optimization for Pareto Front Extraction
Gurpreet Singh
Soumyajit Gupta
Matthew Lease
Clint Dawson
91
4
0
27 Jan 2021
Unbiased Subdata Selection for Fair Classification: A Unified Framework
  and Scalable Algorithms
Unbiased Subdata Selection for Fair Classification: A Unified Framework and Scalable Algorithms
Qing Ye
Weijun Xie
FaML
117
13
0
22 Dec 2020
No Subclass Left Behind: Fine-Grained Robustness in Coarse-Grained
  Classification Problems
No Subclass Left Behind: Fine-Grained Robustness in Coarse-Grained Classification ProblemsNeural Information Processing Systems (NeurIPS), 2020
N. Sohoni
Jared A. Dunnmon
Geoffrey Angus
Albert Gu
Christopher Ré
266
275
0
25 Nov 2020
The Price of Fair PCA: One Extra Dimension
The Price of Fair PCA: One Extra Dimension
Samira Samadi
U. Tantipongpipat
Jamie Morgenstern
Mohit Singh
Santosh Vempala
FaML
268
168
0
31 Oct 2018
Fairness Through Causal Awareness: Learning Latent-Variable Models for
  Biased Data
Fairness Through Causal Awareness: Learning Latent-Variable Models for Biased Data
David Madras
Elliot Creager
T. Pitassi
R. Zemel
FaML
181
140
0
07 Sep 2018
A Unified Approach to Quantifying Algorithmic Unfairness: Measuring
  Individual & Group Unfairness via Inequality Indices
A Unified Approach to Quantifying Algorithmic Unfairness: Measuring Individual & Group Unfairness via Inequality IndicesKnowledge Discovery and Data Mining (KDD), 2018
Till Speicher
Hoda Heidari
Nina Grgic-Hlaca
Krishna P. Gummadi
Adish Singla
Adrian Weller
Muhammad Bilal Zafar
FaML
260
279
0
02 Jul 2018
Fairness Behind a Veil of Ignorance: A Welfare Analysis for Automated
  Decision Making
Fairness Behind a Veil of Ignorance: A Welfare Analysis for Automated Decision Making
Hoda Heidari
Claudio Ferrari
Krishna P. Gummadi
Andreas Krause
387
141
0
13 Jun 2018
Blind Justice: Fairness with Encrypted Sensitive Attributes
Blind Justice: Fairness with Encrypted Sensitive Attributes
Niki Kilbertus
Adria Gascon
Matt J. Kusner
Michael Veale
Krishna P. Gummadi
Adrian Weller
127
159
0
08 Jun 2018
The Externalities of Exploration and How Data Diversity Helps
  Exploitation
The Externalities of Exploration and How Data Diversity Helps Exploitation
Manish Raghavan
Aleksandrs Slivkins
Jennifer Wortman Vaughan
Zhiwei Steven Wu
280
54
0
01 Jun 2018
Delayed Impact of Fair Machine Learning
Delayed Impact of Fair Machine LearningInternational Conference on Machine Learning (ICML), 2018
Lydia T. Liu
Sarah Dean
Esther Rolf
Max Simchowitz
Moritz Hardt
FaML
290
499
0
12 Mar 2018
Probably Approximately Metric-Fair Learning
Probably Approximately Metric-Fair Learning
G. Rothblum
G. Yona
FaMLFedML
143
89
0
08 Mar 2018
A Reductions Approach to Fair Classification
A Reductions Approach to Fair Classification
Alekh Agarwal
A. Beygelzimer
Miroslav Dudík
John Langford
Hanna M. Wallach
FaML
571
1,189
0
06 Mar 2018
Human Perceptions of Fairness in Algorithmic Decision Making: A Case
  Study of Criminal Risk Prediction
Human Perceptions of Fairness in Algorithmic Decision Making: A Case Study of Criminal Risk PredictionThe Web Conference (WWW), 2018
Nina Grgic-Hlaca
Elissa M. Redmiles
Krishna P. Gummadi
Adrian Weller
FaML
120
237
0
26 Feb 2018
Path-Specific Counterfactual Fairness
Path-Specific Counterfactual Fairness
Silvia Chiappa
Thomas P. S. Gillam
CMLFaML
285
366
0
22 Feb 2018
Preventing Fairness Gerrymandering: Auditing and Learning for Subgroup
  Fairness
Preventing Fairness Gerrymandering: Auditing and Learning for Subgroup Fairness
Michael Kearns
Seth Neel
Aaron Roth
Zhiwei Steven Wu
FaML
653
843
0
14 Nov 2017
On Fairness and Calibration
On Fairness and Calibration
Geoff Pleiss
Manish Raghavan
Felix Wu
Jon M. Kleinberg
Kilian Q. Weinberger
FaML
341
944
0
06 Sep 2017
Men Also Like Shopping: Reducing Gender Bias Amplification using
  Corpus-level Constraints
Men Also Like Shopping: Reducing Gender Bias Amplification using Corpus-level Constraints
Jieyu Zhao
Tianlu Wang
Mark Yatskar
Vicente Ordonez
Kai-Wei Chang
FaML
249
1,028
0
29 Jul 2017
Data Decisions and Theoretical Implications when Adversarially Learning
  Fair Representations
Data Decisions and Theoretical Implications when Adversarially Learning Fair Representations
Alex Beutel
Jilin Chen
Zhe Zhao
Ed H. Chi
FaML
244
460
0
01 Jul 2017
From Parity to Preference-based Notions of Fairness in Classification
From Parity to Preference-based Notions of Fairness in Classification
Muhammad Bilal Zafar
Isabel Valera
Manuel Gomez Rodriguez
Krishna P. Gummadi
Adrian Weller
FaML
257
216
0
30 Jun 2017
Semi-supervised Knowledge Transfer for Deep Learning from Private
  Training Data
Semi-supervised Knowledge Transfer for Deep Learning from Private Training Data
Nicolas Papernot
Martín Abadi
Ulfar Erlingsson
Ian Goodfellow
Kunal Talwar
442
1,080
0
18 Oct 2016
Equality of Opportunity in Supervised Learning
Equality of Opportunity in Supervised LearningNeural Information Processing Systems (NeurIPS), 2016
Moritz Hardt
Eric Price
Nathan Srebro
FaML
346
4,744
0
07 Oct 2016
Scalable Learning of Non-Decomposable Objectives
Scalable Learning of Non-Decomposable ObjectivesInternational Conference on Artificial Intelligence and Statistics (AISTATS), 2016
Elad Eban
Mariano Schain
Alan Mackey
A. Gordon
R. Rifkin
G. Elidan
149
115
0
16 Aug 2016
Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word
  Embeddings
Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddings
Tolga Bolukbasi
Kai-Wei Chang
James Zou
Venkatesh Saligrama
Adam Kalai
CVBMFaML
323
3,449
0
21 Jul 2016
Sparse group lasso and high dimensional multinomial classification
Sparse group lasso and high dimensional multinomial classificationComputational Statistics & Data Analysis (CSDA), 2012
Martin Vincent
N. Hansen
257
133
0
06 May 2012
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