Challenges in Applying Explainability Methods to Improve the Fairness of NLP Models

8 June 2022

Papers citing "Challenges in Applying Explainability Methods to Improve the Fairness of NLP Models"

20 / 20 papers shown

Title
Gender Bias in Explainability: Investigating Performance Disparity in Post-hoc Methods Mahdi Dhaini Ege Erdogan Nils Feldhus Gjergji Kasneci 39 0 0 02 May 2025
MAPX: An explainable model-agnostic framework for the detection of false information on social media networks Sarah Condran Michael Bewong Selasi Kwashie Md Zahidul Islam Irfan Altas Joshua Condran 23 0 0 13 Sep 2024
On Behalf of the Stakeholders: Trends in NLP Model Interpretability in the Era of LLMs Nitay Calderon Roi Reichart 32 10 0 27 Jul 2024
Mapping the Potential of Explainable AI for Fairness Along the AI Lifecycle Luca Deck Astrid Schomacker Timo Speith Jakob Schöffer Lena Kästner Niklas Kühl 33 4 0 29 Apr 2024
Procedural Fairness in Machine Learning Ziming Wang Changwu Huang Xin Yao FaML 26 0 0 02 Apr 2024
On the Interplay between Fairness and Explainability Stephanie Brandl Emanuele Bugliarello Ilias Chalkidis FaML 22 4 0 25 Oct 2023
Towards Conceptualization of "Fair Explanation": Disparate Impacts of anti-Asian Hate Speech Explanations on Content Moderators Tin Nguyen Jiannan Xu Aayushi Roy Hal Daumé Marine Carpuat 17 5 0 23 Oct 2023
A Critical Survey on Fairness Benefits of Explainable AI Luca Deck Jakob Schoeffer Maria De-Arteaga Niklas Kühl 13 10 0 15 Oct 2023
Beyond XAI:Obstacles Towards Responsible AI Yulu Pi 19 2 0 07 Sep 2023
Concept-Based Explanations to Test for False Causal Relationships Learned by Abusive Language Classifiers I. Nejadgholi S. Kiritchenko Kathleen C. Fraser Esma Balkir 11 0 0 04 Jul 2023
Being Right for Whose Right Reasons? Terne Sasha Thorn Jakobsen Laura Cabello Anders Søgaard 19 10 0 01 Jun 2023
Counterfactuals of Counterfactuals: a back-translation-inspired approach to analyse counterfactual editors Giorgos Filandrianos Edmund Dervakos Orfeas Menis-Mastromichalakis Chrysoula Zerva Giorgos Stamou AAML 13 4 0 26 May 2023
XMD: An End-to-End Framework for Interactive Explanation-Based Debugging of NLP Models Dong-Ho Lee Akshen Kadakia Brihi Joshi Aaron Chan Ziyi Liu ... Takashi Shibuya Ryosuke Mitani Toshiyuki Sekiya Jay Pujara Xiang Ren LRM 24 9 0 30 Oct 2022
Towards Procedural Fairness: Uncovering Biases in How a Toxic Language Classifier Uses Sentiment Information I. Nejadgholi Esma Balkir Kathleen C. Fraser S. Kiritchenko 13 3 0 19 Oct 2022
Privacy Explanations - A Means to End-User Trust Wasja Brunotte Alexander Specht Larissa Chazette K. Schneider 22 25 0 18 Oct 2022
Less Learn Shortcut: Analyzing and Mitigating Learning of Spurious Feature-Label Correlation Yanrui Du Jing Yang Yan Chen Jing Liu Sendong Zhao Qiaoqiao She Huaqin Wu Haifeng Wang Bing Qin 20 9 0 25 May 2022
Token-Modification Adversarial Attacks for Natural Language Processing: A Survey Tom Roth Yansong Gao A. Abuadbba Surya Nepal Wei Liu AAML 10 12 0 01 Mar 2021
Extracting Training Data from Large Language Models Nicholas Carlini Florian Tramèr Eric Wallace Matthew Jagielski Ariel Herbert-Voss ... Tom B. Brown D. Song Ulfar Erlingsson Alina Oprea Colin Raffel MLAU SILM 267 1,808 0 14 Dec 2020
A Survey on Bias and Fairness in Machine Learning Ninareh Mehrabi Fred Morstatter N. Saxena Kristina Lerman Aram Galstyan SyDa FaML 294 4,187 0 23 Aug 2019
Towards A Rigorous Science of Interpretable Machine Learning Finale Doshi-Velez Been Kim XAI FaML 225 3,672 0 28 Feb 2017