Interpretable Representations in Explainable AI: From Theory to Practice

16 August 2020

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

4 / 4 papers shown

Title
What and How of Machine Learning Transparency: Building Bespoke Explainability Tools with Interoperable Algorithmic Components Kacper Sokol Alexander Hepburn Raúl Santos-Rodríguez Peter A. Flach 54 8 0 08 Sep 2022
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 45 29 0 08 Sep 2022
Explaining the Explainer: A First Theoretical Analysis of LIME Damien Garreau U. V. Luxburg FAtt 43 175 0 10 Jan 2020
"How do I fool you?": Manipulating User Trust via Misleading Black Box Explanations Himabindu Lakkaraju Osbert Bastani 48 252 0 15 Nov 2019