Normalise for Fairness: A Simple Normalisation Technique for Fairness in
Regression Machine Learning Problems

Normalise for Fairness: A Simple Normalisation Technique for Fairness in Regression Machine Learning Problems

2 February 2022

Mostafa M. Mohamed

Björn W. Schuller

Papers citing "Normalise for Fairness: A Simple Normalisation Technique for Fairness in Regression Machine Learning Problems"

6 / 6 papers shown

Title
Intersectional Divergence: Measuring Fairness in Regression Joe Germino Nuno Moniz Nitesh V. Chawla FaML 65 0 0 01 May 2025
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 28 18 0 27 Mar 2023
Error Parity Fairness: Testing for Group Fairness in Regression Tasks Furkan Gursoy I. Kakadiaris 25 4 0 16 Aug 2022
Probing Speech Emotion Recognition Transformers for Linguistic Knowledge Andreas Triantafyllopoulos Johannes Wagner H. Wierstorf Maximilian Schmitt U. Reichel F. Eyben Felix Burkhardt Björn W. Schuller 21 25 0 01 Apr 2022
A Survey on Bias and Fairness in Machine Learning Ninareh Mehrabi Fred Morstatter N. Saxena Kristina Lerman Aram Galstyan SyDa FaML 323 4,212 0 23 Aug 2019
Fair prediction with disparate impact: A study of bias in recidivism prediction instruments Alexandra Chouldechova FaML 207 2,084 0 24 Oct 2016