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Can I Trust the Explainer? Verifying Post-hoc Explanatory Methods

Can I Trust the Explainer? Verifying Post-hoc Explanatory Methods

4 October 2019
Oana-Maria Camburu
Eleonora Giunchiglia
Jakob N. Foerster
Thomas Lukasiewicz
Phil Blunsom
    FAtt
    AAML
ArXivPDFHTML

Papers citing "Can I Trust the Explainer? Verifying Post-hoc Explanatory Methods"

6 / 6 papers shown
Title
Feature Importance Depends on Properties of the Data: Towards Choosing the Correct Explanations for Your Data and Decision Trees based Models
Feature Importance Depends on Properties of the Data: Towards Choosing the Correct Explanations for Your Data and Decision Trees based Models
Célia Wafa Ayad
Thomas Bonnier
Benjamin Bosch
Sonali Parbhoo
Jesse Read
FAtt
XAI
92
0
0
11 Feb 2025
Clairvoyance: A Pipeline Toolkit for Medical Time Series
Clairvoyance: A Pipeline Toolkit for Medical Time Series
Daniel Jarrett
Jinsung Yoon
Ioana Bica
Zhaozhi Qian
A. Ercole
M. Schaar
AI4TS
6
35
0
28 Oct 2023
Knowledge-Grounded Self-Rationalization via Extractive and Natural
  Language Explanations
Knowledge-Grounded Self-Rationalization via Extractive and Natural Language Explanations
Bodhisattwa Prasad Majumder
Oana-Maria Camburu
Thomas Lukasiewicz
Julian McAuley
13
35
0
25 Jun 2021
Evaluating and Aggregating Feature-based Model Explanations
Evaluating and Aggregating Feature-based Model Explanations
Umang Bhatt
Adrian Weller
J. M. F. Moura
XAI
14
219
0
01 May 2020
e-SNLI: Natural Language Inference with Natural Language Explanations
e-SNLI: Natural Language Inference with Natural Language Explanations
Oana-Maria Camburu
Tim Rocktaschel
Thomas Lukasiewicz
Phil Blunsom
LRM
252
620
0
04 Dec 2018
Learning Attitudes and Attributes from Multi-Aspect Reviews
Learning Attitudes and Attributes from Multi-Aspect Reviews
Julian McAuley
J. Leskovec
Dan Jurafsky
193
296
0
15 Oct 2012
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