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Residual Unfairness in Fair Machine Learning from Prejudiced Data

Residual Unfairness in Fair Machine Learning from Prejudiced Data

7 June 2018
Nathan Kallus
Angela Zhou
    FaML
ArXivPDFHTML

Papers citing "Residual Unfairness in Fair Machine Learning from Prejudiced Data"

25 / 25 papers shown
Title
AI Mismatches: Identifying Potential Algorithmic Harms Before AI Development
AI Mismatches: Identifying Potential Algorithmic Harms Before AI Development
Devansh Saxena
Ji-Youn Jung
Jodi Forlizzi
Kenneth Holstein
John Zimmerman
74
0
0
25 Feb 2025
Learning Fair Policies for Multi-stage Selection Problems from
  Observational Data
Learning Fair Policies for Multi-stage Selection Problems from Observational Data
Zhuangzhuang Jia
G. A. Hanasusanto
P. Vayanos
Weijun Xie
FaML
23
2
0
20 Dec 2023
Fair Off-Policy Learning from Observational Data
Fair Off-Policy Learning from Observational Data
Dennis Frauen
Valentyn Melnychuk
Stefan Feuerriegel
FaML
OffRL
30
6
0
15 Mar 2023
Fairguard: Harness Logic-based Fairness Rules in Smart Cities
Fairguard: Harness Logic-based Fairness Rules in Smart Cities
Yiqi Zhao
Ziyan An
Xuqing Gao
Ayan Mukhopadhyay
Meiyi Ma
AI4TS
21
1
0
22 Feb 2023
A Survey on Preserving Fairness Guarantees in Changing Environments
A Survey on Preserving Fairness Guarantees in Changing Environments
Ainhize Barrainkua
Paula Gordaliza
Jose A. Lozano
Novi Quadrianto
FaML
34
3
0
14 Nov 2022
Equalizing Credit Opportunity in Algorithms: Aligning Algorithmic
  Fairness Research with U.S. Fair Lending Regulation
Equalizing Credit Opportunity in Algorithms: Aligning Algorithmic Fairness Research with U.S. Fair Lending Regulation
Indra Elizabeth Kumar
Keegan E. Hines
John P. Dickerson
FaML
51
21
0
05 Oct 2022
Social Bias Meets Data Bias: The Impacts of Labeling and Measurement
  Errors on Fairness Criteria
Social Bias Meets Data Bias: The Impacts of Labeling and Measurement Errors on Fairness Criteria
Yiqiao Liao
Parinaz Naghizadeh Ardabili
24
8
0
31 May 2022
What's the Harm? Sharp Bounds on the Fraction Negatively Affected by
  Treatment
What's the Harm? Sharp Bounds on the Fraction Negatively Affected by Treatment
Nathan Kallus
38
22
0
20 May 2022
Accountability in an Algorithmic Society: Relationality, Responsibility,
  and Robustness in Machine Learning
Accountability in an Algorithmic Society: Relationality, Responsibility, and Robustness in Machine Learning
A. Feder Cooper
Emanuel Moss
Benjamin Laufer
Helen Nissenbaum
MLAU
34
85
0
10 Feb 2022
Diagnosing failures of fairness transfer across distribution shift in
  real-world medical settings
Diagnosing failures of fairness transfer across distribution shift in real-world medical settings
Jessica Schrouff
Natalie Harris
Oluwasanmi Koyejo
Ibrahim M. Alabdulmohsin
Eva Schnider
...
Vivek Natarajan
Alan Karthikesalingam
Katherine A. Heller
Silvia Chiappa
Alexander DÁmour
OOD
65
53
0
02 Feb 2022
Adaptive Data Debiasing through Bounded Exploration
Adaptive Data Debiasing through Bounded Exploration
Yifan Yang
Yang Liu
Parinaz Naghizadeh
FaML
32
7
0
25 Oct 2021
Fair Enough: Searching for Sufficient Measures of Fairness
Fair Enough: Searching for Sufficient Measures of Fairness
Suvodeep Majumder
Joymallya Chakraborty
Gina R. Bai
Kathryn T. Stolee
Tim Menzies
27
26
0
25 Oct 2021
Re-imagining Algorithmic Fairness in India and Beyond
Re-imagining Algorithmic Fairness in India and Beyond
Nithya Sambasivan
Erin Arnesen
Ben Hutchinson
Tulsee Doshi
Vinodkumar Prabhakaran
FaML
17
174
0
25 Jan 2021
Characterizing Fairness Over the Set of Good Models Under Selective
  Labels
Characterizing Fairness Over the Set of Good Models Under Selective Labels
Amanda Coston
Ashesh Rambachan
Alexandra Chouldechova
FaML
38
82
0
02 Jan 2021
The Importance of Modeling Data Missingness in Algorithmic Fairness: A
  Causal Perspective
The Importance of Modeling Data Missingness in Algorithmic Fairness: A Causal Perspective
Naman Goel
Alfonso Amayuelas
Amit Deshpande
Ajay Sharma
FaML
41
29
0
21 Dec 2020
Incorporating Interpretable Output Constraints in Bayesian Neural
  Networks
Incorporating Interpretable Output Constraints in Bayesian Neural Networks
Wanqian Yang
Lars Lorch
Moritz Graule
Himabindu Lakkaraju
Finale Doshi-Velez
UQCV
BDL
17
16
0
21 Oct 2020
An Empirical Characterization of Fair Machine Learning For Clinical Risk
  Prediction
An Empirical Characterization of Fair Machine Learning For Clinical Risk Prediction
Stephen R. Pfohl
Agata Foryciarz
N. Shah
FaML
33
108
0
20 Jul 2020
Bias In, Bias Out? Evaluating the Folk Wisdom
Bias In, Bias Out? Evaluating the Folk Wisdom
Ashesh Rambachan
J. Roth
FaML
32
31
0
18 Sep 2019
Fairness Warnings and Fair-MAML: Learning Fairly with Minimal Data
Fairness Warnings and Fair-MAML: Learning Fairly with Minimal Data
Dylan Slack
Sorelle A. Friedler
Emile Givental
FaML
32
54
0
24 Aug 2019
Transfer of Machine Learning Fairness across Domains
Transfer of Machine Learning Fairness across Domains
Candice Schumann
Xuezhi Wang
Alex Beutel
Jilin Chen
Hai Qian
Ed H. Chi
35
69
0
24 Jun 2019
Assessing Algorithmic Fairness with Unobserved Protected Class Using
  Data Combination
Assessing Algorithmic Fairness with Unobserved Protected Class Using Data Combination
Nathan Kallus
Xiaojie Mao
Angela Zhou
FaML
24
155
0
01 Jun 2019
The Fairness of Risk Scores Beyond Classification: Bipartite Ranking and
  the xAUC Metric
The Fairness of Risk Scores Beyond Classification: Bipartite Ranking and the xAUC Metric
Nathan Kallus
Angela Zhou
19
73
0
15 Feb 2019
Fair Decisions Despite Imperfect Predictions
Fair Decisions Despite Imperfect Predictions
Niki Kilbertus
Manuel Gomez Rodriguez
Bernhard Schölkopf
Krikamol Muandet
Isabel Valera
FaML
OffRL
28
5
0
08 Feb 2019
Fair and Unbiased Algorithmic Decision Making: Current State and Future
  Challenges
Fair and Unbiased Algorithmic Decision Making: Current State and Future Challenges
Songül Tolan
FaML
24
31
0
15 Jan 2019
Actionable Recourse in Linear Classification
Actionable Recourse in Linear Classification
Berk Ustun
Alexander Spangher
Yang Liu
FaML
45
539
0
18 Sep 2018
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