Can Fairness be Automated? Guidelines and Opportunities for
Fairness-aware AutoML

Can Fairness be Automated? Guidelines and Opportunities for Fairness-aware AutoML

15 March 2023

Hilde J. P. Weerts

Florian Pfisterer

Matthias Feurer

Katharina Eggensperger

Joaquin Vanschoren

Mykola Pechenizkiy

Papers citing "Can Fairness be Automated? Guidelines and Opportunities for Fairness-aware AutoML"

13 / 13 papers shown

Title
Operationalizing Machine Learning: An Interview Study Shreya Shankar Rolando Garcia J. M. Hellerstein Aditya G. Parameswaran 51 47 0 16 Sep 2022
Fair and Green Hyperparameter Optimization via Multi-objective and Multiple Information Source Bayesian Optimization Antonio Candelieri Andrea Ponti F. Archetti 12 15 0 18 May 2022
Exploring How Machine Learning Practitioners (Try To) Use Fairness Toolkits Wesley Hanwen Deng Manish Nagireddy M. S. Lee Jatinder Singh Zhiwei Steven Wu Kenneth Holstein Haiyi Zhu 29 85 0 13 May 2022
FairAutoML: Embracing Unfairness Mitigation in AutoML Qingyun Wu Chi Wang FaML 29 5 0 11 Nov 2021
Explaining Hyperparameter Optimization via Partial Dependence Plots Julia Moosbauer J. Herbinger Giuseppe Casalicchio Marius Lindauer Bernd Bischl 36 56 0 08 Nov 2021
Whither AutoML? Understanding the Role of Automation in Machine Learning Workflows Doris Xin Eva Yiwei Wu D. Lee Niloufar Salehi Aditya G. Parameswaran 44 71 0 13 Jan 2021
AutoGluon-Tabular: Robust and Accurate AutoML for Structured Data Nick Erickson Jonas W. Mueller Alexander Shirkov Hang Zhang Pedro Larroy Mu Li Alex Smola LMTD 81 576 0 13 Mar 2020
Human-AI Collaboration in Data Science: Exploring Data Scientists' Perceptions of Automated AI Dakuo Wang Justin D. Weisz Michael J. Muller Parikshit Ram Werner Geyer Casey Dugan Y. Tausczik Horst Samulowitz Alexander G. Gray 156 312 0 05 Sep 2019
A Survey on Bias and Fairness in Machine Learning Ninareh Mehrabi Fred Morstatter N. Saxena Kristina Lerman Aram Galstyan SyDa FaML 286 4,143 0 23 Aug 2019
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 189 730 0 13 Dec 2018
Model Evaluation, Model Selection, and Algorithm Selection in Machine Learning S. Raschka 72 749 0 13 Nov 2018
Efficient Multi-objective Neural Architecture Search via Lamarckian Evolution T. Elsken J. H. Metzen Frank Hutter 117 498 0 24 Apr 2018
Fair prediction with disparate impact: A study of bias in recidivism prediction instruments Alexandra Chouldechova FaML 185 2,079 0 24 Oct 2016