Measuring Fairness Under Unawareness of Sensitive Attributes: A Quantification-Based Approach

17 September 2021

Papers citing "Measuring Fairness Under Unawareness of Sensitive Attributes: A Quantification-Based Approach"

9 / 9 papers shown

Title
Learning to quantify graph nodes A. Micheli Alejandro Moreo Marco Podda Fabrizio Sebastiani William Simoni Domenico Tortorella 56 0 0 19 Mar 2025
Lazy Data Practices Harm Fairness Research Jan Simson Alessandro Fabris Christoph Kern 25 5 0 26 Apr 2024
On the Relationship Between Interpretability and Explainability in Machine Learning Benjamin Leblanc Pascal Germain FaML 26 0 0 20 Nov 2023
Group-blind optimal transport to group parity and its constrained variants Quan-Gen Zhou Jakub Marecek 24 3 0 17 Oct 2023
Counterfactual Reasoning for Bias Evaluation and Detection in a Fairness under Unawareness setting Giandomenico Cornacchia V. W. Anelli F. Narducci Azzurra Ragone E. Sciascio MLAU FaML 16 1 0 16 Feb 2023
Fair Machine Learning in Healthcare: A Review Qizhang Feng Mengnan Du Na Zou Xia Hu FaML 30 0 0 29 Jun 2022
Evaluating Fairness of Machine Learning Models Under Uncertain and Incomplete Information Pranjal Awasthi Alex Beutel Matthaeus Kleindessner Jamie Morgenstern Xuezhi Wang FaML 54 55 0 16 Feb 2021
Characterizing Intersectional Group Fairness with Worst-Case Comparisons A. Ghosh Lea Genuit Mary Reagan FaML 86 51 0 05 Jan 2021
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