Human-centered Explainable Machine Learning
Human-Centered Explainable Machine Learning is an academic framework within explainable artificial intelligence that prioritizes human stakeholders' cognitive, contextual, and experiential needs by systematically integrating human-centric design principles, interpretability requirements, and interactive feedback loops into the development, evaluation, and deployment of machine learning models.
69
Publications
5.1K
Citations
349
Authors
181
Institutions
Publications per year
2017–2026
69
Parents
69
Explainable AI for Designers: A Human-Centered Perspective on Mixed-Initiative Co-Creation
Jichen Zhu, Antonios Liapis, Sebastian Risi et al. · 2018 · 241 citations · Full text
Arts, Computer Science, Design +17
Explanatory Interactive Machine Learning
Stefano Teso, Kristian Kersting · 2019 · 195 citations · Full text
1–5 of 69
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Hong Kong University of Science and Technology, Hong Kong SAR China
2 publications · 123 citations
2
h-index
1–5 of 349
181

Delft University of Technology
Delft, The Netherlands
3 h-index
9 publications
609 citations
Fraunhofer Institute for Applied Information Technology
Sankt Augustin, Germany
2 h-index
6 publications
146 citations

City, University of London
London, United Kingdom
2 h-index
3 publications
145 citations

Commonwealth Scientific and Industrial Research Organisation
Canberra, Australia
2 h-index
4 publications
166 citations
University of Augsburg
Augsburg, Germany
2 h-index
4 publications
112 citations
University College Dublin
Dublin, Ireland
2 h-index
6 publications
122 citations

Technische Universität Darmstadt
Darmstadt, Germany
2 h-index
4 publications
267 citations

Hong Kong University of Science and Technology
Hong Kong, Hong Kong
2 h-index
6 publications
369 citations
1–8 of 181
Repository · 8 publications · 152 citations
8
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TIIS · 3 publications · 222 citations
3
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3 publications · 149 citations
3
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2 publications · 678 citations
2
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Conference · 1 publications · 21 citations
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