LiFT: A Scalable Framework for Measuring Fairness in ML Applications

14 August 2020

Papers citing "LiFT: A Scalable Framework for Measuring Fairness in ML Applications"

9 / 9 papers shown

Title
A Catalog of Fairness-Aware Practices in Machine Learning Engineering Gianmario Voria Giulia Sellitto Carmine Ferrara Francesco Abate A. Lucia F. Ferrucci Gemma Catolino Fabio Palomba FaML 33 3 0 29 Aug 2024
Individual Fairness under Uncertainty Wenbin Zhang Zichong Wang Juyong Kim Cheng Cheng Thomas Oommen Pradeep Ravikumar Jeremy C. Weiss FaML 38 12 0 16 Feb 2023
De-biasing "bias" measurement K. Lum Yunfeng Zhang Amanda Bower 15 26 0 11 May 2022
Longitudinal Fairness with Censorship Wenbin Zhang Jeremy C. Weiss 17 40 0 30 Mar 2022
Amazon SageMaker Clarify: Machine Learning Bias Detection and Explainability in the Cloud Michaela Hardt Xiaoguang Chen Xiaoyi Cheng Michele Donini J. Gelman ... Muhammad Bilal Zafar Sanjiv Ranjan Das Kevin Haas Tyler Hill K. Kenthapadi ELM FaML 23 42 0 07 Sep 2021
Fairness in Machine Learning L. Oneto Silvia Chiappa FaML 249 488 0 31 Dec 2020
"What We Can't Measure, We Can't Understand": Challenges to Demographic Data Procurement in the Pursuit of Fairness Mckane Andrus Elena Spitzer Jeffrey Brown Alice Xiang 19 126 0 30 Oct 2020
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 192 742 0 13 Dec 2018
Fair prediction with disparate impact: A study of bias in recidivism prediction instruments Alexandra Chouldechova FaML 207 2,082 0 24 Oct 2016