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T-Explainer: A Model-Agnostic Explainability Framework Based on Gradients

T-Explainer: A Model-Agnostic Explainability Framework Based on Gradients

25 April 2024
Evandro S. Ortigossa
Fábio F. Dias
Brian Barr
Claudio T. Silva
L. G. Nonato
    FAtt
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Papers citing "T-Explainer: A Model-Agnostic Explainability Framework Based on Gradients"

3 / 3 papers shown
Title
Fairness via Explanation Quality: Evaluating Disparities in the Quality
  of Post hoc Explanations
Fairness via Explanation Quality: Evaluating Disparities in the Quality of Post hoc Explanations
Jessica Dai
Sohini Upadhyay
Ulrich Aivodji
Stephen H. Bach
Himabindu Lakkaraju
19
55
0
15 May 2022
The Disagreement Problem in Explainable Machine Learning: A Practitioner's Perspective
The Disagreement Problem in Explainable Machine Learning: A Practitioner's Perspective
Satyapriya Krishna
Tessa Han
Alex Gu
Steven Wu
S. Jabbari
Himabindu Lakkaraju
142
181
0
03 Feb 2022
Feature Importance Ranking for Deep Learning
Feature Importance Ranking for Deep Learning
Maksymilian Wojtas
Ke Chen
118
116
0
18 Oct 2020
1