Investigating the Relationship Between Debiasing and Artifact Removal using Saliency Maps

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Investigating the Relationship Between Debiasing and Artifact Removal using Saliency Maps

28 February 2025

Lukasz Sztukiewicz

Michał Wiliński

Jerzy Stefanowski

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Papers citing "Investigating the Relationship Between Debiasing and Artifact Removal using Saliency Maps"

18 / 18 papers shown

Title
Properties of fairness measures in the context of varying class imbalance and protected group ratiosACM Transactions on Knowledge Discovery from Data (TKDD), 2024 D. Brzezinski Julia Stachowiak Jerzy Stefanowski Izabela Szczech R. Susmaga Sofya Aksenyuk Uladzimir Ivashka Oleksandr Yasinskyi 326 7 0 13 Nov 2024
Locally Testing Model Detections for Semantic Global Concepts Franz Motzkus Georgii Mikriukov Christian Hellert Ute Schmid 235 3 0 27 May 2024
From Hope to Safety: Unlearning Biases of Deep Models via Gradient Penalization in Latent SpaceAAAI Conference on Artificial Intelligence (AAAI), 2023 Maximilian Dreyer Frederik Pahde Christopher J. Anders Wojciech Samek Sebastian Lapuschkin AI4CE 148 18 0 18 Aug 2023
Quantus: An Explainable AI Toolkit for Responsible Evaluation of Neural Network Explanations and BeyondJournal of machine learning research (JMLR), 2022 Anna Hedström Leander Weber Dilyara Bareeva Daniel G. Krakowczyk Franz Motzkus Wojciech Samek Sebastian Lapuschkin Marina M.-C. Höhne XAI ELM 277 214 0 14 Feb 2022
Software for Dataset-wide XAI: From Local Explanations to Global Insights with Zennit, CoRelAy, and ViRelAy Christopher J. Anders David Neumann Wojciech Samek K. Müller Sebastian Lapuschkin 204 80 0 24 Jun 2021
Fairness in Machine Learning: A SurveyACM Computing Surveys (ACM CSUR), 2020 Simon Caton C. Haas FaML 417 775 0 04 Oct 2020
Intra-Processing Methods for Debiasing Neural Networks Yash Savani Colin White G. NaveenSundar 164 48 0 15 Jun 2020
Towards Best Practice in Explaining Neural Network Decisions with LRPIEEE International Joint Conference on Neural Network (IJCNN), 2019 M. Kohlbrenner Alexander Bauer Shinichi Nakajima Alexander Binder Wojciech Samek Sebastian Lapuschkin 248 164 0 22 Oct 2019
A Survey on Bias and Fairness in Machine LearningACM Computing Surveys (ACM CSUR), 2019 Ninareh Mehrabi Fred Morstatter N. Saxena Kristina Lerman Aram Galstyan SyDa FaML 1.1K 5,080 0 23 Aug 2019
Mitigating Unwanted Biases with Adversarial Learning B. Zhang Blake Lemoine Margaret Mitchell FaML 361 1,514 0 22 Jan 2018
Counterfactual Explanations without Opening the Black Box: Automated Decisions and the GDPR Sandra Wachter Brent Mittelstadt Chris Russell MLAU 729 2,656 0 01 Nov 2017
Axiomatic Attribution for Deep Networks Mukund Sundararajan Ankur Taly Qiqi Yan OOD FAtt 1.1K 6,917 0 04 Mar 2017
Equality of Opportunity in Supervised LearningNeural Information Processing Systems (NeurIPS), 2016 Moritz Hardt Eric Price Nathan Srebro FaML 338 4,730 0 07 Oct 2016
Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddings Tolga Bolukbasi Kai-Wei Chang James Zou Venkatesh Saligrama Adam Kalai CVBM FaML 291 3,437 0 21 Jul 2016
Deep Residual Learning for Image Recognition Kaiming He Xinming Zhang Shaoqing Ren Jian Sun MedIm 3.4K 213,467 0 10 Dec 2015
Analyzing Classifiers: Fisher Vectors and Deep Neural Networks Sebastian Bach Alexander Binder G. Montavon K. Müller Wojciech Samek 197 202 0 01 Dec 2015
Deep Learning Face Attributes in the WildIEEE International Conference on Computer Vision (ICCV), 2014 Ziwei Liu Ping Luo Xiaogang Wang Xiaoou Tang CVBM 1.2K 9,081 0 28 Nov 2014
Automated Experiments on Ad Privacy Settings: A Tale of Opacity, Choice, and DiscriminationProceedings on Privacy Enhancing Technologies (PoPETs), 2014 Amit Datta Michael Carl Tschantz Anupam Datta 214 759 0 27 Aug 2014