Abstract
With the advent of high-performance black-box models, interpretability is becoming a hot topic today in machine learning. While a lot of research is
done on interpretability, machine learning researchers do not have precise guidelines for setting up user-based experiments. This paper provides well-established guidelines from the human-computer interaction community.
done on interpretability, machine learning researchers do not have precise guidelines for setting up user-based experiments. This paper provides well-established guidelines from the human-computer interaction community.
| Original language | English |
|---|---|
| Title of host publication | EGC Workshop on Advances in Interpretable Machine Learning and Artificial Intelligence |
| Place of Publication | Metz, France |
| Publication status | Published - 2019 |
Keywords
- Machine learning
- Interpretability
- Guidelines
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Student Theses
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Interpretability and Explainability in Machine Learning and their Application to Nonlinear Dimensionality Reduction
Author: Bibal, A., 16 Nov 2020Supervisor: FRENAY, B. (Supervisor), VANHOOF, W. (President), Cleve, A. (Jury), Dumas, B. (Jury), Lee, J. A. (External person) (Jury) & Galarraga, L. A. (External person) (Jury)
Student thesis: Doc types › Doctor of Sciences