Explanation Consistency
Explanation Consistency is a property of explainable machine learning models that refers to the degree to which similar inputs or model behaviors yield similar explanations, ensuring stability and reliability in interpretability across variations in data or model perturbations.
34
Publications
2.6K
Citations
111
Authors
56
Institutions
Publications per year
2017–2026
24
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Explainable Machine Learning for Scientific Insights and Discoveries
Ribana Roscher, Bastian Bohn, Marco F. Duarte et al. · IEEE Access · 2020 · 973 citations · Full text
Prediction and Design of Nanozymes using Explainable Machine Learning
Yonghua Wei, Jin Wu, Yixuan Wu et al. · Advanced Materials · 2022 · 176 citations
The Disagreement Problem in Explainable Machine Learning: A Practitioner's Perspective
Satyapriya Krishna, Tessa Han, Alex Gu et al. · Research Square · 2023 · 154 citations · Full text
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Princeton University
Princeton, United States
3 h-index
3 publications
248 citations

Harvard University Press
Cambridge, United States
3 h-index
6 publications
587 citations
Brown University
Providence, United States
2 h-index
5 publications
165 citations

United States Naval Research Laboratory
Washington, United States
2 h-index
2 publications
68 citations
IRT M2P
Metz, France
2 h-index
4 publications
62 citations
Massachusetts Institute of Technology
Cambridge, United States
2 h-index
4 publications
510 citations
AT&T (United States)
Bedminster, United States
2 h-index
2 publications
56 citations

Taipei Veterans General Hospital
Taipei, Taiwan
1 h-index
4 publications
180 citations
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Repository · 4 publications · 94 citations
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Conference · 2 publications · 46 citations
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2 publications · 56 citations
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2 publications · 44 citations
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1 publications · 30 citations
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