Explainability for fair machine learning

Explainability for fair machine learning

14 October 2020

Tobias Schwedes

Christopher Frye

Papers citing "Explainability for fair machine learning"

15 / 15 papers shown

Title
Explanations as Bias Detectors: A Critical Study of Local Post-hoc XAI Methods for Fairness Exploration Vasiliki Papanikou Danae Pla Karidi E. Pitoura Emmanouil Panagiotou Eirini Ntoutsi 33 0 0 01 May 2025
Constructing Fair Latent Space for Intersection of Fairness and Explainability Hyungjun Joo Hyeonggeun Han Sehwan Kim Sangwoo Hong Jungwoo Lee 40 0 0 23 Dec 2024
Procedural Fairness in Machine Learning Ziming Wang Changwu Huang Xin Yao FaML 50 0 0 02 Apr 2024
REFRESH: Responsible and Efficient Feature Reselection Guided by SHAP Values Shubham Sharma Sanghamitra Dutta Emanuele Albini Freddy Lecue Daniele Magazzeni Manuela Veloso 40 1 0 13 Mar 2024
On Explaining Unfairness: An Overview Christos Fragkathoulas Vasiliki Papanikou Danae Pla Karidi E. Pitoura XAI FaML 19 2 0 16 Feb 2024
ALMANACS: A Simulatability Benchmark for Language Model Explainability Edmund Mills Shiye Su Stuart J. Russell Scott Emmons 54 7 0 20 Dec 2023
Towards Fair and Calibrated Models Anand Brahmbhatt Vipul Rathore Mausam Parag Singla FaML 21 2 0 16 Oct 2023
Function Composition in Trustworthy Machine Learning: Implementation Choices, Insights, and Questions Manish Nagireddy Moninder Singh Samuel C. Hoffman Evaline Ju K. Ramamurthy Kush R. Varshney 32 1 0 17 Feb 2023
Manifestations of Xenophobia in AI Systems Nenad Tomašev J. L. Maynard Iason Gabriel 24 9 0 15 Dec 2022
Tensions Between the Proxies of Human Values in AI Teresa Datta D. Nissani Max Cembalest Akash Khanna Haley Massa John P. Dickerson 34 2 0 14 Dec 2022
Fairness and Explainability: Bridging the Gap Towards Fair Model Explanations Yuying Zhao Yu-Chiang Frank Wang Tyler Derr FaML 35 13 0 07 Dec 2022
Explainable Global Fairness Verification of Tree-Based Classifiers Stefano Calzavara Lorenzo Cazzaro Claudio Lucchese Federico Marcuzzi 27 2 0 27 Sep 2022
Challenges in Applying Explainability Methods to Improve the Fairness of NLP Models Esma Balkir S. Kiritchenko I. Nejadgholi Kathleen C. Fraser 21 36 0 08 Jun 2022
Fool SHAP with Stealthily Biased Sampling Gabriel Laberge Ulrich Aïvodji Satoshi Hara M. Marchand Foutse Khomh MLAU AAML FAtt 13 2 0 30 May 2022
Explaining Algorithmic Fairness Through Fairness-Aware Causal Path Decomposition Weishen Pan Sen Cui Jiang Bian Changshui Zhang Fei Wang CML FaML 11 33 0 11 Aug 2021