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2206.15363
Cited By
Why we do need Explainable AI for Healthcare
30 June 2022
Giovanni Cina
Tabea E. Rober
Rob Goedhart
Ilker Birbil
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Papers citing
"Why we do need Explainable AI for Healthcare"
18 / 18 papers shown
Title
Diagnosing AI Explanation Methods with Folk Concepts of Behavior
Alon Jacovi
Jasmijn Bastings
Sebastian Gehrmann
Yoav Goldberg
Katja Filippova
79
17
0
27 Jan 2022
Model Learning with Personalized Interpretability Estimation (ML-PIE)
M. Virgolin
A. D. Lorenzo
Francesca Randone
Eric Medvet
M. Wahde
87
31
0
13 Apr 2021
Interpretable Machine Learning: Fundamental Principles and 10 Grand Challenges
Cynthia Rudin
Chaofan Chen
Zhi Chen
Haiyang Huang
Lesia Semenova
Chudi Zhong
FaML
AI4CE
LRM
235
674
0
20 Mar 2021
Trust Issues: Uncertainty Estimation Does Not Enable Reliable OOD Detection On Medical Tabular Data
Dennis Ulmer
L. Meijerink
Giovanni Cina
OOD
48
72
0
06 Nov 2020
Explainable Deep Learning: A Field Guide for the Uninitiated
Gabrielle Ras
Ning Xie
Marcel van Gerven
Derek Doran
AAML
XAI
109
379
0
30 Apr 2020
One Explanation Does Not Fit All: The Promise of Interactive Explanations for Machine Learning Transparency
Kacper Sokol
Peter A. Flach
44
177
0
27 Jan 2020
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
135
6,321
0
22 Oct 2019
Evaluating Explanation Without Ground Truth in Interpretable Machine Learning
Fan Yang
Mengnan Du
Helen Zhou
XAI
ELM
67
67
0
16 Jul 2019
Optimal Sparse Decision Trees
Xiyang Hu
Cynthia Rudin
Margo Seltzer
139
175
0
29 Apr 2019
Assessing the Local Interpretability of Machine Learning Models
Dylan Slack
Sorelle A. Friedler
C. Scheidegger
Chitradeep Dutta Roy
FAtt
50
71
0
09 Feb 2019
Fooling Neural Network Interpretations via Adversarial Model Manipulation
Juyeon Heo
Sunghwan Joo
Taesup Moon
AAML
FAtt
113
205
0
06 Feb 2019
Explaining Explanations: An Overview of Interpretability of Machine Learning
Leilani H. Gilpin
David Bau
Ben Z. Yuan
Ayesha Bajwa
Michael A. Specter
Lalana Kagal
XAI
112
1,865
0
31 May 2018
The Challenge of Crafting Intelligible Intelligence
Daniel S. Weld
Gagan Bansal
58
244
0
09 Mar 2018
What Does Explainable AI Really Mean? A New Conceptualization of Perspectives
Derek Doran
Sarah Schulz
Tarek R. Besold
XAI
68
439
0
02 Oct 2017
Explanation in Artificial Intelligence: Insights from the Social Sciences
Tim Miller
XAI
259
4,287
0
22 Jun 2017
Towards A Rigorous Science of Interpretable Machine Learning
Finale Doshi-Velez
Been Kim
XAI
FaML
415
3,824
0
28 Feb 2017
The Mythos of Model Interpretability
Zachary Chase Lipton
FaML
183
3,716
0
10 Jun 2016
Interpretable classifiers using rules and Bayesian analysis: Building a better stroke prediction model
Benjamin Letham
Cynthia Rudin
Tyler H. McCormick
D. Madigan
FAtt
72
745
0
05 Nov 2015
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