Marrying Fairness and Explainability in Supervised Learning

6 April 2022

Papers citing "Marrying Fairness and Explainability in Supervised Learning"

9 / 9 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 29 0 0 01 May 2025
Whence Is A Model Fair? Fixing Fairness Bugs via Propensity Score Matching Kewen Peng Yicheng Yang Hao Zhuo 30 0 0 23 Apr 2025
FairSense: Long-Term Fairness Analysis of ML-Enabled Systems Yining She Sumon Biswas Christian Kastner Eunsuk Kang 40 0 0 03 Jan 2025
Mapping the Potential of Explainable AI for Fairness Along the AI Lifecycle Luca Deck Astrid Schomacker Timo Speith Jakob Schöffer Lena Kästner Niklas Kühl 38 4 0 29 Apr 2024
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 22 1 0 17 Feb 2023
Explainable Global Fairness Verification of Tree-Based Classifiers Stefano Calzavara Lorenzo Cazzaro Claudio Lucchese Federico Marcuzzi 24 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
An Investigation of Why Overparameterization Exacerbates Spurious Correlations Shiori Sagawa Aditi Raghunathan Pang Wei Koh Percy Liang 144 370 0 09 May 2020
Fair prediction with disparate impact: A study of bias in recidivism prediction instruments Alexandra Chouldechova FaML 201 2,082 0 24 Oct 2016