Measure Twice, Cut Once: Quantifying Bias and Fairness in Deep Neural Networks

8 October 2021

Yan Yan

Papers citing "Measure Twice, Cut Once: Quantifying Bias and Fairness in Deep Neural Networks"

3 / 3 papers shown

Title
Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing Sofia Yfantidou Marios Constantinides Dimitris Spathis Athena Vakali Daniele Quercia F. Kawsar HAI FaML 33 18 0 27 Mar 2023
Improved Multi-label Classification under Temporal Concept Drift: Rethinking Group-Robust Algorithms in a Label-Wise Setting Ilias Chalkidis Anders Søgaard OOD AI4TS 19 29 0 15 Mar 2022
A Survey on Bias and Fairness in Machine Learning Ninareh Mehrabi Fred Morstatter N. Saxena Kristina Lerman Aram Galstyan SyDa FaML 385 4,271 0 23 Aug 2019