An Empirical Study of Rich Subgroup Fairness for Machine Learning

An Empirical Study of Rich Subgroup Fairness for Machine Learning

24 August 2018

Zhiwei Steven Wu

Papers citing "An Empirical Study of Rich Subgroup Fairness for Machine Learning"

9 / 9 papers shown

Title
Fairness Practices in Industry: A Case Study in Machine Learning Teams Building Recommender Systems Jing Nathan Yan Junxiong Wang Jeffrey M. Rzeszotarski Allison Koenecke FaML 44 0 0 26 May 2025
On the Promise for Assurance of Differentiable Neurosymbolic Reasoning Paradigms Luke E. Richards Jessie Yaros Jasen Babcock Coung Ly Robin Cosbey Timothy Doster Cynthia Matuszek NAI 92 0 0 13 Feb 2025
EARN Fairness: Explaining, Asking, Reviewing, and Negotiating Artificial Intelligence Fairness Metrics Among Stakeholders Lin Luo Yuri Nakao Mathieu Chollet Hiroya Inakoshi Simone Stumpf 53 1 0 16 Jul 2024
Diversity-aware clustering: Computational Complexity and Approximation Algorithms Suhas Thejaswi Ameet Gadekar Bruno Ordozgoiti Aristides Gionis 45 2 0 10 Jan 2024
Bias Testing and Mitigation in LLM-based Code Generation Dong Huang Qingwen Bu Jie M. Zhang Xiaofei Xie Junjie Chen Heming Cui 70 23 0 03 Sep 2023
One-vs.-One Mitigation of Intersectional Bias: A General Method to Extend Fairness-Aware Binary Classification Kenji Kobayashi Yuri Nakao FaML 54 8 0 26 Oct 2020
A Reductions Approach to Fair Classification Alekh Agarwal A. Beygelzimer Miroslav Dudík John Langford Hanna M. Wallach FaML 133 1,094 0 06 Mar 2018
Fairness in Criminal Justice Risk Assessments: The State of the Art R. Berk Hoda Heidari S. Jabbari Michael Kearns Aaron Roth 44 990 0 27 Mar 2017
Inherent Trade-Offs in the Fair Determination of Risk Scores Jon M. Kleinberg S. Mullainathan Manish Raghavan FaML 84 1,762 0 19 Sep 2016