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1902.03501
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Assessing the Local Interpretability of Machine Learning Models
9 February 2019
Dylan Slack
Sorelle A. Friedler
C. Scheidegger
Chitradeep Dutta Roy
FAtt
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Papers citing
"Assessing the Local Interpretability of Machine Learning Models"
26 / 26 papers shown
Title
Towards Human-centered Design of Explainable Artificial Intelligence (XAI): A Survey of Empirical Studies
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OpenHEXAI: An Open-Source Framework for Human-Centered Evaluation of Explainable Machine Learning
Jiaqi Ma
Vivian Lai
Yiming Zhang
Chacha Chen
Paul Hamilton
Davor Ljubenkov
Himabindu Lakkaraju
Chenhao Tan
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46
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20 Feb 2024
Overconfident and Unconfident AI Hinder Human-AI Collaboration
Jingshu Li
Yitian Yang
Renwen Zhang
Yi-Chieh Lee
76
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12 Feb 2024
Q-SENN: Quantized Self-Explaining Neural Networks
Thomas Norrenbrock
Marco Rudolph
Bodo Rosenhahn
FAtt
AAML
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102
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21 Dec 2023
A Review on Explainable Artificial Intelligence for Healthcare: Why, How, and When?
M. Rubaiyat
Hossain Mondal
Prajoy Podder
91
64
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10 Apr 2023
Take 5: Interpretable Image Classification with a Handful of Features
Thomas Norrenbrock
Marco Rudolph
Bodo Rosenhahn
FAtt
84
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23 Mar 2023
Selective Explanations: Leveraging Human Input to Align Explainable AI
Vivian Lai
Yiming Zhang
Chacha Chen
Q. V. Liao
Chenhao Tan
95
45
0
23 Jan 2023
Evaluation and Improvement of Interpretability for Self-Explainable Part-Prototype Networks
Qihan Huang
Mengqi Xue
Wenqi Huang
Haofei Zhang
Mingli Song
Yongcheng Jing
Mingli Song
AAML
74
28
0
12 Dec 2022
Why we do need Explainable AI for Healthcare
Giovanni Cina
Tabea E. Rober
Rob Goedhart
Ilker Birbil
71
14
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30 Jun 2022
Interpretation Quality Score for Measuring the Quality of interpretability methods
Sean Xie
Soroush Vosoughi
Saeed Hassanpour
XAI
111
5
0
24 May 2022
Machine Explanations and Human Understanding
Chacha Chen
Shi Feng
Amit Sharma
Chenhao Tan
92
26
0
08 Feb 2022
Rethinking Explainability as a Dialogue: A Practitioner's Perspective
Himabindu Lakkaraju
Dylan Slack
Yuxin Chen
Chenhao Tan
Sameer Singh
LRM
110
64
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03 Feb 2022
Towards a Science of Human-AI Decision Making: A Survey of Empirical Studies
Vivian Lai
Chacha Chen
Q. V. Liao
Alison Smith-Renner
Chenhao Tan
123
188
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21 Dec 2021
Opportunities for Machine Learning to Accelerate Halide Perovskite Commercialization and Scale-Up
Rishi E. Kumar
A. Tiihonen
Shijing Sun
D. Fenning
Zhe Liu
Tonio Buonassisi
35
11
0
08 Oct 2021
A Review of Explainable Artificial Intelligence in Manufacturing
G. Sofianidis
Jože M. Rožanec
Dunja Mladenić
D. Kyriazis
84
17
0
05 Jul 2021
On the Interaction of Belief Bias and Explanations
Ana Valeria González
Anna Rogers
Anders Søgaard
FAtt
80
19
0
29 Jun 2021
Interpretable Deep Learning: Interpretation, Interpretability, Trustworthiness, and Beyond
Xuhong Li
Haoyi Xiong
Xingjian Li
Xuanyu Wu
Xiao Zhang
Ji Liu
Jiang Bian
Dejing Dou
AAML
FaML
XAI
HAI
82
341
0
19 Mar 2021
Evaluating the Interpretability of Generative Models by Interactive Reconstruction
A. Ross
Nina Chen
Elisa Zhao Hang
Elena L. Glassman
Finale Doshi-Velez
158
49
0
02 Feb 2021
Explainable Artificial Intelligence Approaches: A Survey
Sheikh Rabiul Islam
W. Eberle
S. Ghafoor
Mohiuddin Ahmed
XAI
87
104
0
23 Jan 2021
Interpretable Machine Learning -- A Brief History, State-of-the-Art and Challenges
Christoph Molnar
Giuseppe Casalicchio
B. Bischl
AI4TS
AI4CE
114
405
0
19 Oct 2020
The role of explainability in creating trustworthy artificial intelligence for health care: a comprehensive survey of the terminology, design choices, and evaluation strategies
A. Markus
J. Kors
P. Rijnbeek
91
471
0
31 Jul 2020
Towards Quantification of Explainability in Explainable Artificial Intelligence Methods
Sheikh Rabiul Islam
W. Eberle
S. Ghafoor
XAI
82
43
0
22 Nov 2019
Fairness Warnings and Fair-MAML: Learning Fairly with Minimal Data
Dylan Slack
Sorelle A. Friedler
Emile Givental
FaML
118
55
0
24 Aug 2019
Machine Learning Testing: Survey, Landscapes and Horizons
Jie M. Zhang
Mark Harman
Lei Ma
Yang Liu
VLM
AILaw
110
756
0
19 Jun 2019
Proposed Guidelines for the Responsible Use of Explainable Machine Learning
Patrick Hall
Navdeep Gill
N. Schmidt
SILM
XAI
FaML
77
29
0
08 Jun 2019
Quantifying Model Complexity via Functional Decomposition for Better Post-Hoc Interpretability
Christoph Molnar
Giuseppe Casalicchio
B. Bischl
FAtt
56
60
0
08 Apr 2019
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