Revealing Neural Network Bias to Non-Experts Through Interactive
Counterfactual Examples

v1v2 (latest)

Revealing Neural Network Bias to Non-Experts Through Interactive Counterfactual Examples

7 January 2020

Chelsea M. Myers

Luis Fernando Laris Pardo

ArXiv (abs)PDF HTML

Papers citing "Revealing Neural Network Bias to Non-Experts Through Interactive Counterfactual Examples"

8 / 8 papers shown

Title
On Explaining Proxy Discrimination and Unfairness in Individual Decisions Made by AI Systems Belona Sonna Alban Grastien 76 0 0 30 Sep 2025
Am I Being Treated Fairly? A Conceptual Framework for Individuals to Ascertain Fairness Juliett Suárez Ferreira Marija Slavkovik Jorge Casillas FaML 256 0 0 03 Apr 2025
GAM Coach: Towards Interactive and User-centered Algorithmic RecourseInternational Conference on Human Factors in Computing Systems (CHI), 2023 Zijie J. Wang J. W. Vaughan R. Caruana Duen Horng Chau HAI 263 26 0 27 Feb 2023
A Survey on Interactive Reinforcement Learning: Design Principles and Open Challenges Christian Arzate Cruz Takeo Igarashi OffRL 154 102 0 27 May 2021
If Only We Had Better Counterfactual Explanations: Five Key Deficits to Rectify in the Evaluation of Counterfactual XAI TechniquesInternational Joint Conference on Artificial Intelligence (IJCAI), 2021 Mark T. Keane Eoin M. Kenny Eoin Delaney Barry Smyth CML 226 162 0 26 Feb 2021
BENN: Bias Estimation Using Deep Neural Network Amit Giloni Edita Grolman Tanja Hagemann Ronald Fromm Sebastian Fischer Yuval Elovici A. Shabtai 108 2 0 23 Dec 2020
Counterfactual Explanations and Algorithmic Recourses for Machine Learning: A ReviewACM Computing Surveys (ACM CSUR), 2020 Sahil Verma Varich Boonsanong Minh Hoang Keegan E. Hines John P. Dickerson Chirag Shah CML 612 226 0 20 Oct 2020
AI and Wargaming J. Goodman S. Risi Simon Lucas VLM 270 16 0 18 Sep 2020