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

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

7 January 2020

Chelsea M. Myers

Luis Fernando Laris Pardo

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

10 / 10 papers shown

Title
Explaining Vulnerabilities to Adversarial Machine Learning through Visual Analytics Yuxin Ma Tiankai Xie Jundong Li Ross Maciejewski AAML 41 67 0 17 Jul 2019
Learning Not to Learn: Training Deep Neural Networks with Biased Data Byungju Kim Hyunwoo Kim Kyungsu Kim Sungjin Kim Junmo Kim OOD 57 409 0 26 Dec 2018
Turning a Blind Eye: Explicit Removal of Biases and Variation from Deep Neural Network Embeddings Mohsan S. Alvi Andrew Zisserman C. Nellåker FaML 115 248 0 06 Sep 2018
Word Embeddings Quantify 100 Years of Gender and Ethnic Stereotypes Nikhil Garg L. Schiebinger Dan Jurafsky James Zou AI4TS 69 963 0 22 Nov 2017
Counterfactual Explanations without Opening the Black Box: Automated Decisions and the GDPR Sandra Wachter Brent Mittelstadt Chris Russell MLAU 107 2,352 0 01 Nov 2017
Distill-and-Compare: Auditing Black-Box Models Using Transparent Model Distillation S. Tan R. Caruana Giles Hooker Yin Lou MLAU 101 185 0 17 Oct 2017
Men Also Like Shopping: Reducing Gender Bias Amplification using Corpus-level Constraints Jieyu Zhao Tianlu Wang Mark Yatskar Vicente Ordonez Kai-Wei Chang FaML 90 970 0 29 Jul 2017
Counterfactual Fairness Matt J. Kusner Joshua R. Loftus Chris Russell Ricardo M. A. Silva FaML 215 1,577 0 20 Mar 2017
European Union regulations on algorithmic decision-making and a "right to explanation" B. Goodman Seth Flaxman FaML AILaw 63 1,900 0 28 Jun 2016
"Why Should I Trust You?": Explaining the Predictions of Any Classifier Marco Tulio Ribeiro Sameer Singh Carlos Guestrin FAtt FaML 1.2K 16,976 0 16 Feb 2016

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