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1809.04578
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Simplicity Creates Inequity: Implications for Fairness, Stereotypes, and Interpretability
12 September 2018
Jon M. Kleinberg
S. Mullainathan
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Papers citing
"Simplicity Creates Inequity: Implications for Fairness, Stereotypes, and Interpretability"
21 / 21 papers shown
Title
Operationalizing the Blueprint for an AI Bill of Rights: Recommendations for Practitioners, Researchers, and Policy Makers
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11 Jul 2024
On the Power of Randomization in Fair Classification and Representation
Sushant Agarwal
Amit Deshpande
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83
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05 Jun 2024
SoK: Taming the Triangle -- On the Interplays between Fairness, Interpretability and Privacy in Machine Learning
Julien Ferry
Ulrich Aïvodji
Sébastien Gambs
Marie-José Huguet
Mohamed Siala
FaML
69
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0
22 Dec 2023
A Critical Survey on Fairness Benefits of Explainable AI
Luca Deck
Jakob Schoeffer
Maria De-Arteaga
Niklas Kühl
117
13
0
15 Oct 2023
LUCID-GAN: Conditional Generative Models to Locate Unfairness
Andres Algaba
Carmen Mazijn
Carina E. A. Prunkl
J. Danckaert
Vincent Ginis
SyDa
97
1
0
28 Jul 2023
Algorithms, Incentives, and Democracy
E. M. Penn
John W. Patty
FaML
62
1
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05 Jul 2023
Decongestion by Representation: Learning to Improve Economic Welfare in Marketplaces
Omer Nahum
Gali Noti
David C. Parkes
Nir Rosenfeld
73
3
0
18 Jun 2023
Cross-Institutional Transfer Learning for Educational Models: Implications for Model Performance, Fairness, and Equity
Joshua Gardner
Renzhe Yu
Quan Nguyen
Christopher A. Brooks
René F. Kizilcec
FedML
89
16
0
01 May 2023
LUCID: Exposing Algorithmic Bias through Inverse Design
Carmen Mazijn
Carina E. A. Prunkl
Andres Algaba
J. Danckaert
Vincent Ginis
SyDa
88
4
0
26 Aug 2022
Learning Optimal Predictive Checklists
Haoran Zhang
Q. Morris
Berk Ustun
Marzyeh Ghassemi
120
11
0
02 Dec 2021
On the Fairness of Machine-Assisted Human Decisions
Talia B. Gillis
Bryce Mclaughlin
Jann Spiess
FaML
64
16
0
28 Oct 2021
Amazon SageMaker Clarify: Machine Learning Bias Detection and Explainability in the Cloud
Michaela Hardt
Xiaoguang Chen
Xiaoyi Cheng
Michele Donini
J. Gelman
...
Muhammad Bilal Zafar
Sanjiv Ranjan Das
Kevin Haas
Tyler Hill
K. Kenthapadi
ELM
FaML
80
43
0
07 Sep 2021
MIMIC-IF: Interpretability and Fairness Evaluation of Deep Learning Models on MIMIC-IV Dataset
Chuizheng Meng
Loc Trinh
Nan Xu
Yan Liu
67
30
0
12 Feb 2021
Removing Spurious Features can Hurt Accuracy and Affect Groups Disproportionately
Fereshte Khani
Percy Liang
FaML
61
66
0
07 Dec 2020
Formalizing Trust in Artificial Intelligence: Prerequisites, Causes and Goals of Human Trust in AI
Alon Jacovi
Ana Marasović
Tim Miller
Yoav Goldberg
328
450
0
15 Oct 2020
A Possibility in Algorithmic Fairness: Can Calibration and Equal Error Rates Be Reconciled?
Claire Lazar Reich
Suhas Vijaykumar
FaML
67
19
0
18 Feb 2020
A Precise High-Dimensional Asymptotic Theory for Boosting and Minimum-
ℓ
1
\ell_1
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-Norm Interpolated Classifiers
Tengyuan Liang
Pragya Sur
133
70
0
05 Feb 2020
Measurement and Fairness
Abigail Z. Jacobs
Hanna M. Wallach
90
404
0
11 Dec 2019
Fairness Sample Complexity and the Case for Human Intervention
Ananth Balashankar
Alyssa Lees
23
3
0
24 Oct 2019
SensitiveNets: Learning Agnostic Representations with Application to Face Images
Aythami Morales
Julian Fierrez
R. Vera-Rodríguez
Ruben Tolosana
CVBM
97
42
0
01 Feb 2019
Manipulating and Measuring Model Interpretability
Forough Poursabzi-Sangdeh
D. Goldstein
Jake M. Hofman
Jennifer Wortman Vaughan
Hanna M. Wallach
133
702
0
21 Feb 2018
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