Explainable Artificial Intelligence
Explainable Artificial Intelligence is a subfield of artificial intelligence that comprises methodologies and frameworks designed to render the decisions and internal workings of machine learning models transparent, interpretable, and comprehensible to human users, thereby distinguishing it from opaque or black-box predictive modeling approaches.
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2017–2026
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From local explanations to global understanding with explainable AI for trees
Scott Lundberg, Gabriel Erion, Hugh Chen et al. · Nature Machine Intelligence · 2020 · 8.9K citations · Full text
A Unified Approach to Interpreting Model Predictions
Scott Lundberg, Su‐In Lee · arXiv (Cornell University) · 2017 · 7.6K citations · Full text
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Fraunhofer Institute for Telecommunications, Heinrich Hertz Institute, Germany
19 publications · 5.4K citations
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Delft University of Technology
Delft, The Netherlands
22 h-index
58 publications
4.6K citations
University of Washington
Seattle, United States
21 h-index
78 publications
158.4K citations

Centre National de la Recherche Scientifique
Paris, France
21 h-index
64 publications
42.8K citations
Carnegie Mellon University
Pittsburgh, United States
20 h-index
81 publications
4.7K citations

Monash University
Melbourne, Australia
19 h-index
42 publications
4.1K citations

University of Pisa
Pisa, Italy
19 h-index
53 publications
7.3K citations
Technische Universität Berlin
Berlin, Germany
19 h-index
58 publications
10K citations

Medical University of Graz
Graz, Austria
18 h-index
58 publications
6.2K citations
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Repository · 273 publications · 26.5K citations
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82 publications · 13.7K citations
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44 publications · 1.9K citations
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45 publications · 2.5K citations
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