Feature Importance Disparities for Data Bias Investigations

3 March 2023

Papers citing "Feature Importance Disparities for Data Bias Investigations"

20 / 20 papers shown

Title
OpenXAI: Towards a Transparent Evaluation of Model Explanations Chirag Agarwal Dan Ley Satyapriya Krishna Eshika Saxena Martin Pawelczyk Nari Johnson Isha Puri Marinka Zitnik Himabindu Lakkaraju XAI 57 145 0 22 Jun 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 64 78 0 06 May 2022
Rethinking Stability for Attribution-based Explanations Chirag Agarwal Nari Johnson Martin Pawelczyk Satyapriya Krishna Eshika Saxena Marinka Zitnik Himabindu Lakkaraju FAtt 62 51 0 14 Mar 2022
The Disagreement Problem in Explainable Machine Learning: A Practitioner's Perspective Satyapriya Krishna Tessa Han Alex Gu Steven Wu S. Jabbari Himabindu Lakkaraju 241 193 0 03 Feb 2022
Omnipredictors Parikshit Gopalan Adam Tauman Kalai Omer Reingold Vatsal Sharan Udi Wieder 69 51 0 11 Sep 2021
Explainability for fair machine learning T. Begley Tobias Schwedes Christopher Frye Ilya Feige FaML FedML 84 47 0 14 Oct 2020
Fairness in Machine Learning: A Survey Simon Caton C. Haas FaML 92 637 0 04 Oct 2020
Moment Multicalibration for Uncertainty Estimation Christopher Jung Changhwa Lee Mallesh M. Pai Aaron Roth R. Vohra UQCV 225 65 0 18 Aug 2020
Fooling LIME and SHAP: Adversarial Attacks on Post hoc Explanation Methods Dylan Slack Sophie Hilgard Emily Jia Sameer Singh Himabindu Lakkaraju FAtt AAML MLAU 70 817 0 06 Nov 2019
On the Robustness of Interpretability Methods David Alvarez-Melis Tommi Jaakkola 76 526 0 21 Jun 2018
Datasheets for Datasets Timnit Gebru Jamie Morgenstern Briana Vecchione Jennifer Wortman Vaughan Hanna M. Wallach Hal Daumé Kate Crawford 258 2,181 0 23 Mar 2018
A Reductions Approach to Fair Classification Alekh Agarwal A. Beygelzimer Miroslav Dudík John Langford Hanna M. Wallach FaML 224 1,100 0 06 Mar 2018
Axiomatic Attribution for Deep Networks Mukund Sundararajan Ankur Taly Qiqi Yan OOD FAtt 179 5,986 0 04 Mar 2017
Fair prediction with disparate impact: A study of bias in recidivism prediction instruments Alexandra Chouldechova FaML 297 2,110 0 24 Oct 2016
Equality of Opportunity in Supervised Learning Moritz Hardt Eric Price Nathan Srebro FaML 222 4,307 0 07 Oct 2016
Inherent Trade-Offs in the Fair Determination of Risk Scores Jon M. Kleinberg S. Mullainathan Manish Raghavan FaML 114 1,769 0 19 Sep 2016
"Why Should I Trust You?": Explaining the Predictions of Any Classifier Marco Tulio Ribeiro Sameer Singh Carlos Guestrin FAtt FaML 1.2K 16,954 0 16 Feb 2016
A Deeper Look at Dataset Bias Tatiana Tommasi Novi Patricia Barbara Caputo Tinne Tuytelaars 97 327 0 06 May 2015
Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps Karen Simonyan Andrea Vedaldi Andrew Zisserman FAtt 309 7,292 0 20 Dec 2013
How to Explain Individual Classification Decisions D. Baehrens T. Schroeter Stefan Harmeling M. Kawanabe K. Hansen K. Müller FAtt 128 1,103 0 06 Dec 2009