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Interpretable Representations in Explainable AI: From Theory to Practice

Interpretable Representations in Explainable AI: From Theory to Practice

16 August 2020
Kacper Sokol
Peter A. Flach
ArXivPDFHTML

Papers citing "Interpretable Representations in Explainable AI: From Theory to Practice"

3 / 3 papers shown
Title
FAT Forensics: A Python Toolbox for Implementing and Deploying Fairness,
  Accountability and Transparency Algorithms in Predictive Systems
FAT Forensics: A Python Toolbox for Implementing and Deploying Fairness, Accountability and Transparency Algorithms in Predictive Systems
Kacper Sokol
Alexander Hepburn
Rafael Poyiadzi
M. Clifford
Raúl Santos-Rodríguez
Peter A. Flach
40
29
0
08 Sep 2022
Explaining the Explainer: A First Theoretical Analysis of LIME
Explaining the Explainer: A First Theoretical Analysis of LIME
Damien Garreau
U. V. Luxburg
FAtt
11
175
0
10 Jan 2020
"How do I fool you?": Manipulating User Trust via Misleading Black Box
  Explanations
"How do I fool you?": Manipulating User Trust via Misleading Black Box Explanations
Himabindu Lakkaraju
Osbert Bastani
30
251
0
15 Nov 2019
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