Characterizing the risk of fairwashing

14 June 2021

Papers citing "Characterizing the risk of fairwashing"

20 / 20 papers shown

Title
Fair Play for Individuals, Foul Play for Groups? Auditing Anonymization's Impact on ML Fairness Héber H. Arcolezi Mina Alishahi Adda-Akram Bendoukha Nesrine Kaaniche 36 0 0 12 May 2025
The Curious Case of Arbitrariness in Machine Learning Prakhar Ganesh Afaf Taik G. Farnadi 59 2 0 28 Jan 2025
SoK: Taming the Triangle -- On the Interplays between Fairness, Interpretability and Privacy in Machine Learning Julien Ferry Ulrich Aïvodji Sébastien Gambs Marie-José Huguet Mohamed Siala FaML 26 5 0 22 Dec 2023
A Path to Simpler Models Starts With Noise Lesia Semenova Harry Chen Ronald E. Parr Cynthia Rudin 33 15 0 30 Oct 2023
A Critical Survey on Fairness Benefits of Explainable AI Luca Deck Jakob Schoeffer Maria De-Arteaga Niklas Kühl 34 11 0 15 Oct 2023
Probabilistic Dataset Reconstruction from Interpretable Models Julien Ferry Ulrich Aïvodji Sébastien Gambs Marie-José Huguet Mohamed Siala 24 5 0 29 Aug 2023
Manipulation Risks in Explainable AI: The Implications of the Disagreement Problem S. Goethals David Martens Theodoros Evgeniou 36 4 0 24 Jun 2023
Adversarial Attacks on the Interpretation of Neuron Activation Maximization Géraldin Nanfack A. Fulleringer Jonathan Marty Michael Eickenberg Eugene Belilovsky AAML FAtt 25 10 0 12 Jun 2023
Adversarial attacks and defenses in explainable artificial intelligence: A survey Hubert Baniecki P. Biecek AAML 42 63 0 06 Jun 2023
On the relevance of APIs facing fairwashed audits Jade Garcia Bourrée Erwan Le Merrer Gilles Tredan Benoit Rottembourg MLAU 17 0 0 23 May 2023
Connecting the Dots in Trustworthy Artificial Intelligence: From AI Principles, Ethics, and Key Requirements to Responsible AI Systems and Regulation Natalia Díaz Rodríguez Javier Del Ser Mark Coeckelbergh Marcos López de Prado E. Herrera-Viedma Francisco Herrera XAI 27 264 0 02 May 2023
Why is plausibility surprisingly problematic as an XAI criterion? Weina Jin Xiaoxiao Li Ghassan Hamarneh 49 3 0 30 Mar 2023
Learning Hybrid Interpretable Models: Theory, Taxonomy, and Methods Julien Ferry Gabriel Laberge Ulrich Aïvodji 28 5 0 08 Mar 2023
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
The Road to Explainability is Paved with Bias: Measuring the Fairness of Explanations Aparna Balagopalan Haoran Zhang Kimia Hamidieh Thomas Hartvigsen Frank Rudzicz Marzyeh Ghassemi 38 77 0 06 May 2022
Computing the Collection of Good Models for Rule Lists Kota Mata Kentaro Kanamori Hiroki Arimura 11 7 0 24 Apr 2022
When and How to Fool Explainable Models (and Humans) with Adversarial Examples Jon Vadillo Roberto Santana Jose A. Lozano SILM AAML 36 11 0 05 Jul 2021
Counterfactual Explanations and Algorithmic Recourses for Machine Learning: A Review Sahil Verma Varich Boonsanong Minh Hoang Keegan E. Hines John P. Dickerson Chirag Shah CML 24 162 0 20 Oct 2020
Learning Certifiably Optimal Rule Lists for Categorical Data E. Angelino Nicholas Larus-Stone Daniel Alabi Margo Seltzer Cynthia Rudin 48 195 0 06 Apr 2017
Fair prediction with disparate impact: A study of bias in recidivism prediction instruments Alexandra Chouldechova FaML 207 2,084 0 24 Oct 2016