Wasserstein-based fairness interpretability framework for machine
learning models

v1v2v3v4v5 (latest)

Wasserstein-based fairness interpretability framework for machine learning models

6 November 2020

Konstandinos Kotsiopoulos

Arjun Ravi Kannan

ArXiv (abs)PDF HTML

Papers citing "Wasserstein-based fairness interpretability framework for machine learning models"

9 / 9 papers shown

Title
Enforcing Fairness Where It Matters: An Approach Based on Difference-of-Convex Constraints Yutian He Yankun Huang Yao Yao Qihang Lin FaML 55 0 0 18 May 2025
Explainable post-training bias mitigation with distribution-based fairness metrics Ryan Franks A. Miroshnikov 78 0 0 01 Apr 2025
OT-Net: A Reusable Neural Optimal Transport Solver Zezeng Li Shenghao Li Lianbao Jin Na Lei Zhongxuan Luo OT 89 4 0 14 Jun 2023
Retiring $Δ$ DP: New Distribution-Level Metrics for Demographic Parity Xiaotian Han Zhimeng Jiang Hongye Jin Zirui Liu Na Zou Qifan Wang Helen Zhou 106 4 0 31 Jan 2023
AI Fairness: from Principles to Practice A. Bateni Matthew Chan Ray Eitel-Porter 38 4 0 20 Jul 2022
Model-agnostic bias mitigation methods with regressor distribution control for Wasserstein-based fairness metrics A. Miroshnikov Konstandinos Kotsiopoulos Ryan Franks Arjun Ravi Kannan 49 5 0 19 Nov 2021
FairCanary: Rapid Continuous Explainable Fairness Avijit Ghosh Aalok Shanbhag Christo Wilson 107 22 0 13 Jun 2021
FiSH: Fair Spatial Hotspots Deepak P Sowmya S. Sundaram 69 1 0 01 Jun 2021
Unified Shapley Framework to Explain Prediction Drift Aalok Shanbhag A. Ghosh Josh Rubin FAtt FedML AI4TS 72 3 0 15 Feb 2021