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Case-based Explainability for Random Forest: Prototypes, Critics, Counter-factuals and Semi-factuals

Case-based Explainability for Random Forest: Prototypes, Critics, Counter-factuals and Semi-factuals

Gregory Yampolsky
Dhruv Desai
Mingshu Li
Stefano Pasquali
Dhagash Mehta
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Papers citing "Case-based Explainability for Random Forest: Prototypes, Critics, Counter-factuals and Semi-factuals"

4 / 4 papers shown
Title
An Objective Metric for Explainable AI: How and Why to Estimate the Degree of Explainability
An Objective Metric for Explainable AI: How and Why to Estimate the Degree of Explainability
Francesco Sovrano
F. Vitali
72
31
0
11 Sep 2021
Polyjuice: Generating Counterfactuals for Explaining, Evaluating, and Improving Models
Polyjuice: Generating Counterfactuals for Explaining, Evaluating, and Improving Models
Tongshuang Wu
Marco Tulio Ribeiro
Jeffrey Heer
Daniel S. Weld
103
250
0
01 Jan 2021
Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI
Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI
Alejandro Barredo Arrieta
Natalia Díaz Rodríguez
Javier Del Ser
Adrien Bennetot
Siham Tabik
...
S. Gil-Lopez
Daniel Molina
Richard Benjamins
Raja Chatila
Francisco Herrera
XAI
130
6,293
0
22 Oct 2019
Deep Learning for Case-Based Reasoning through Prototypes: A Neural Network that Explains Its Predictions
Deep Learning for Case-Based Reasoning through Prototypes: A Neural Network that Explains Its Predictions
Oscar Li
Hao Liu
Chaofan Chen
Cynthia Rudin
178
592
0
13 Oct 2017

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