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2103.06172
Cited By
Fairness On The Ground: Applying Algorithmic Fairness Approaches to Production Systems
10 March 2021
Chloé Bakalar
Renata Barreto
Stevie Bergman
Miranda Bogen
Bobbie Chern
S. Corbett-Davies
Melissa Hall
Isabel Kloumann
Michelle S. Lam
J. Q. Candela
Manish Raghavan
J. Simons
Jonathan Tannen
Edmund Tong
Kate Vredenburgh
Jiejing Zhao
FaML
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Papers citing
"Fairness On The Ground: Applying Algorithmic Fairness Approaches to Production Systems"
10 / 10 papers shown
Title
Resource-constrained Fairness
Sofie Goethals
Eoin Delaney
Brent Mittelstadt
Christopher Russell
FaML
86
1
0
03 Jun 2024
Towards a Non-Ideal Methodological Framework for Responsible ML
Ramaravind Kommiya Mothilal
Shion Guha
Syed Ishtiaque Ahmed
48
7
0
20 Jan 2024
De-biasing "bias" measurement
K. Lum
Yunfeng Zhang
Amanda Bower
18
27
0
11 May 2022
Improving the Fairness of Chest X-ray Classifiers
Haoran Zhang
Natalie Dullerud
Karsten Roth
Lauren Oakden-Rayner
Stephen R. Pfohl
Marzyeh Ghassemi
23
65
0
23 Mar 2022
Towards Responsible Natural Language Annotation for the Varieties of Arabic
A. S. Bergman
Mona T. Diab
27
18
0
17 Mar 2022
A Systematic Study of Bias Amplification
Melissa Hall
L. V. D. van der Maaten
Laura Gustafson
Maxwell Jones
Aaron B. Adcock
102
70
0
27 Jan 2022
Assessing the Fairness of AI Systems: AI Practitioners' Processes, Challenges, and Needs for Support
Michael A. Madaio
Lisa Egede
Hariharan Subramonyam
Jennifer Wortman Vaughan
Hanna M. Wallach
27
141
0
10 Dec 2021
Finding Representative Group Fairness Metrics Using Correlation Estimations
Hadis Anahideh
Nazanin Nezami
Abolfazl Asudeh
29
1
0
13 Sep 2021
A comparison of approaches to improve worst-case predictive model performance over patient subpopulations
Stephen R. Pfohl
Haoran Zhang
Yizhe Xu
Agata Foryciarz
Marzyeh Ghassemi
N. Shah
OOD
29
22
0
27 Aug 2021
Improving fairness in machine learning systems: What do industry practitioners need?
Kenneth Holstein
Jennifer Wortman Vaughan
Hal Daumé
Miroslav Dudík
Hanna M. Wallach
FaML
HAI
192
743
0
13 Dec 2018
1