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An Empirical Study on Learning Fairness Metrics for COMPAS Data with Human Supervision

22 October 2019

Papers citing "An Empirical Study on Learning Fairness Metrics for COMPAS Data with Human Supervision"

28 / 28 papers shown

Title
Effort-aware Fairness: Incorporating a Philosophy-informed, Human-centered Notion of Effort into Algorithmic Fairness Metrics Tin Trung Nguyen Jiannan Xu Zora Che Phuong-Anh Nguyen-Le Rushil Dandamudi Donald Braman Furong Huang Hal Daumé III Zubin Jelveh 367 0 0 25 May 2025
(De)Noise: Moderating the Inconsistency Between Human Decision-Makers Nina Grgić-Hlavca Junaid Ali Krishna P. Gummadi Jennifer Wortman Vaughan 152 2 0 15 Jul 2024
Counterpart Fairness -- Addressing Systematic between-group Differences in Fairness Evaluation Yifei Wang Zhengyang Zhou Liqin Wang John Laurentiev Peter Hou Li Zhou Pengyu Hong 157 0 0 29 May 2023
Human-Guided Fair Classification for Natural Language ProcessingInternational Conference on Learning Representations (ICLR), 2022 Florian E.Dorner Momchil Peychev Nikola Konstantinov Naman Goel Elliott Ash Martin Vechev FaML 232 7 0 20 Dec 2022
iFlipper: Label Flipping for Individual Fairness Hantian Zhang Ki Hyun Tae Jaeyoung Park Xu Chu Steven Euijong Whang 169 12 0 15 Sep 2022
Comparing Apples to Oranges: Learning Similarity Functions for Data Produced by Different DistributionsNeural Information Processing Systems (NeurIPS), 2022 Leonidas Tsepenekas Shubham Sharma Freddy Lecue Daniele Magazzeni 167 1 0 26 Aug 2022
Perspectives on Incorporating Expert Feedback into Model UpdatesPatterns (Patterns), 2022 Valerie Chen Umang Bhatt Hoda Heidari Adrian Weller Ameet Talwalkar 202 15 0 13 May 2022
Latent Space Smoothing for Individually Fair RepresentationsEuropean Conference on Computer Vision (ECCV), 2021 Momchil Peychev Anian Ruoss Mislav Balunović Maximilian Baader Martin Vechev FaML 216 23 0 26 Nov 2021
Fair Enough: Searching for Sufficient Measures of FairnessACM Transactions on Software Engineering and Methodology (TOSEM), 2021 Suvodeep Majumder Joymallya Chakraborty Gina R. Bai Kathryn T. Stolee Tim Menzies 177 36 0 25 Oct 2021
Individually Fair Gradient BoostingInternational Conference on Learning Representations (ICLR), 2021 Alexander Vargo Fan Zhang Mikhail Yurochkin Yuekai Sun FaML FedML 148 15 0 31 Mar 2021
Statistical inference for individual fairnessInternational Conference on Learning Representations (ICLR), 2021 Subha Maity Songkai Xue Mikhail Yurochkin Yuekai Sun FaML 116 21 0 30 Mar 2021
Individually Fair Ranking Amanda Bower Hamid Eftekhari Mikhail Yurochkin Yuekai Sun FaML 150 12 0 19 Mar 2021
SenSeI: Sensitive Set Invariance for Enforcing Individual FairnessInternational Conference on Learning Representations (ICLR), 2020 Mikhail Yurochkin Yuekai Sun FaML 196 53 0 25 Jun 2020
Fairness without Demographics through Adversarially Reweighted LearningNeural Information Processing Systems (NeurIPS), 2020 Preethi Lahoti Alex Beutel Jilin Chen Kang Lee Flavien Prost Nithum Thain Xuezhi Wang Ed H. Chi FaML 422 362 0 23 Jun 2020
Two Simple Ways to Learn Individual Fairness Metrics from Data Debarghya Mukherjee Mikhail Yurochkin Moulinath Banerjee Yuekai Sun FaML 179 106 0 19 Jun 2020
Learning Certified Individually Fair RepresentationsNeural Information Processing Systems (NeurIPS), 2020 Anian Ruoss Mislav Balunović Marc Fischer Martin Vechev FaML 225 104 0 24 Feb 2020
Operationalizing Individual Fairness with Pairwise Fair RepresentationsProceedings of the VLDB Endowment (PVLDB), 2019 Preethi Lahoti Krishna P. Gummadi Gerhard Weikum FaML 203 113 0 02 Jul 2019
Training individually fair ML models with Sensitive Subspace RobustnessInternational Conference on Learning Representations (ICLR), 2019 Mikhail Yurochkin Amanda Bower Yuekai Sun FaML OOD 196 122 0 28 Jun 2019
Metric Learning for Individual FairnessSymposium on Foundations of Responsible Computing (FRC), 2019 Christina Ilvento FaML 251 101 0 01 Jun 2019
An Algorithmic Framework for Fairness Elicitation Christopher Jung Michael Kearns Seth Neel Aaron Roth Logan Stapleton Zhiwei Steven Wu FedML FaML 174 56 0 25 May 2019
A Unified Approach to Quantifying Algorithmic Unfairness: Measuring Individual & Group Unfairness via Inequality IndicesKnowledge Discovery and Data Mining (KDD), 2018 Till Speicher Hoda Heidari Nina Grgic-Hlaca Krishna P. Gummadi Adish Singla Adrian Weller Muhammad Bilal Zafar FaML 260 279 0 02 Jul 2018
iFair: Learning Individually Fair Data Representations for Algorithmic Decision Making Preethi Lahoti Krishna P. Gummadi Gerhard Weikum FaML 143 180 0 04 Jun 2018
Calibrated Fairness in Bandits Zehua Wang Goran Radanović Christos Dimitrakakis Debmalya Mandal David C. Parkes FedML FaML 145 93 0 06 Jul 2017
Fairness Beyond Disparate Treatment & Disparate Impact: Learning Classification without Disparate Mistreatment Muhammad Bilal Zafar Isabel Valera Manuel Gomez Rodriguez Krishna P. Gummadi FaML 386 1,265 0 26 Oct 2016
Equality of Opportunity in Supervised LearningNeural Information Processing Systems (NeurIPS), 2016 Moritz Hardt Eric Price Nathan Srebro FaML 346 4,744 0 07 Oct 2016
Deep metric learning using Triplet networkInternational Workshop on Similarity-Based Pattern Recognition (SBPR), 2014 Elad Hoffer Nir Ailon SSL DML 533 2,117 0 20 Dec 2014
A Survey on Metric Learning for Feature Vectors and Structured Data A. Bellet Amaury Habrard M. Sebban 384 699 0 28 Jun 2013
Adaptively Learning the Crowd Kernel Omer Tamuz Ce Liu Serge Belongie Ohad Shamir Adam Tauman Kalai 185 98 0 05 May 2011