Proxy Tasks and Subjective Measures Can Be Misleading in Evaluating Explainable AI Systems

22 January 2020

Papers citing "Proxy Tasks and Subjective Measures Can Be Misleading in Evaluating Explainable AI Systems"

8 / 58 papers shown

Title
Outcome-Explorer: A Causality Guided Interactive Visual Interface for Interpretable Algorithmic Decision Making Md. Naimul Hoque Klaus Mueller CML 59 30 0 03 Jan 2021
What Do You See? Evaluation of Explainable Artificial Intelligence (XAI) Interpretability through Neural Backdoors Yi-Shan Lin Wen-Chuan Lee Z. Berkay Celik XAI 34 93 0 22 Sep 2020
Play MNIST For Me! User Studies on the Effects of Post-Hoc, Example-Based Explanations & Error Rates on Debugging a Deep Learning, Black-Box Classifier Courtney Ford Eoin M. Kenny Mark T. Keane 23 6 0 10 Sep 2020
Does the Whole Exceed its Parts? The Effect of AI Explanations on Complementary Team Performance Gagan Bansal Tongshuang Wu Joyce Zhou Raymond Fok Besmira Nushi Ece Kamar Marco Tulio Ribeiro Daniel S. Weld 42 584 0 26 Jun 2020
Does Explainable Artificial Intelligence Improve Human Decision-Making? Y. Alufaisan L. Marusich J. Bakdash Yan Zhou Murat Kantarcioglu XAI 22 94 0 19 Jun 2020
Explaining Explanations: Axiomatic Feature Interactions for Deep Networks Joseph D. Janizek Pascal Sturmfels Su-In Lee FAtt 35 143 0 10 Feb 2020
Leveraging Latent Features for Local Explanations Ronny Luss Pin-Yu Chen Amit Dhurandhar P. Sattigeri Yunfeng Zhang Karthikeyan Shanmugam Chun-Chen Tu FAtt 54 37 0 29 May 2019
Towards A Rigorous Science of Interpretable Machine Learning Finale Doshi-Velez Been Kim XAI FaML 257 3,698 0 28 Feb 2017