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Net benefit, calibration, threshold selection, and training objectives
  for algorithmic fairness in healthcare

Net benefit, calibration, threshold selection, and training objectives for algorithmic fairness in healthcare

3 February 2022
Stephen R. Pfohl
Yizhe Xu
Agata Foryciarz
Nikolaos Ignatiadis
Julian Z. Genkins
N. Shah
ArXivPDFHTML

Papers citing "Net benefit, calibration, threshold selection, and training objectives for algorithmic fairness in healthcare"

4 / 4 papers shown
Title
Specification Overfitting in Artificial Intelligence
Specification Overfitting in Artificial Intelligence
Benjamin Roth
Pedro Henrique Luz de Araujo
Yuxi Xia
Saskia Kaltenbrunner
Christoph Korab
56
0
0
13 Mar 2024
A Closer Look at AUROC and AUPRC under Class Imbalance
A Closer Look at AUROC and AUPRC under Class Imbalance
Matthew B. A. McDermott
Lasse Hyldig Hansen
Haoran Zhang
Giovanni Angelotti
Jack Gallifant
31
29
0
11 Jan 2024
Learning Adversarially Fair and Transferable Representations
Learning Adversarially Fair and Transferable Representations
David Madras
Elliot Creager
T. Pitassi
R. Zemel
FaML
213
669
0
17 Feb 2018
Fair prediction with disparate impact: A study of bias in recidivism
  prediction instruments
Fair prediction with disparate impact: A study of bias in recidivism prediction instruments
Alexandra Chouldechova
FaML
185
2,082
0
24 Oct 2016
1