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Why we do need Explainable AI for Healthcare

Why we do need Explainable AI for Healthcare

30 June 2022
Giovanni Cina
Tabea E. Rober
Rob Goedhart
Ilker Birbil
ArXiv (abs)PDFHTML

Papers citing "Why we do need Explainable AI for Healthcare"

18 / 18 papers shown
Title
Diagnosing AI Explanation Methods with Folk Concepts of Behavior
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)
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
Interpretable Machine Learning: Fundamental Principles and 10 Grand Challenges
Cynthia Rudin
Chaofan Chen
Zhi Chen
Haiyang Huang
Lesia Semenova
Chudi Zhong
FaMLAI4CELRM
235
674
0
20 Mar 2021
Trust Issues: Uncertainty Estimation Does Not Enable Reliable OOD
  Detection On Medical Tabular Data
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
Explainable Deep Learning: A Field Guide for the Uninitiated
Gabrielle Ras
Ning Xie
Marcel van Gerven
Derek Doran
AAMLXAI
109
379
0
30 Apr 2020
One Explanation Does Not Fit All: The Promise of Interactive
  Explanations for Machine Learning Transparency
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
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
Evaluating Explanation Without Ground Truth in Interpretable Machine Learning
Fan Yang
Mengnan Du
Helen Zhou
XAIELM
67
67
0
16 Jul 2019
Optimal Sparse Decision Trees
Optimal Sparse Decision Trees
Xiyang Hu
Cynthia Rudin
Margo Seltzer
139
175
0
29 Apr 2019
Assessing the Local Interpretability of Machine Learning Models
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
Fooling Neural Network Interpretations via Adversarial Model Manipulation
Juyeon Heo
Sunghwan Joo
Taesup Moon
AAMLFAtt
113
205
0
06 Feb 2019
Explaining Explanations: An Overview of Interpretability of Machine
  Learning
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
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
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
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
Towards A Rigorous Science of Interpretable Machine Learning
Finale Doshi-Velez
Been Kim
XAIFaML
415
3,824
0
28 Feb 2017
The Mythos of Model Interpretability
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
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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