Abstract
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 |
| Publication status | Published - 2019 |
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Keywords
- Machine learning
- Interpretability
- Guidelines
Cite this
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User-Based Experiment Guidelines for Measuring Interpretability in Machine Learning. / Bibal, Adrien; Dumas, Bruno; Frenay, Benoît.
EGC Workshop on Advances in Interpretable Machine Learning and Artificial Intelligence. Metz, 2019.Research output: Contribution in Book/Catalog/Report/Conference proceeding › Conference contribution
TY - GEN
T1 - User-Based Experiment Guidelines for Measuring Interpretability in Machine Learning
AU - Bibal, Adrien
AU - Dumas, Bruno
AU - Frenay, Benoît
PY - 2019
Y1 - 2019
N2 - With the advent of high-performance black-box models, interpretability is becoming a hot topic today in machine learning. While a lot of research isdone 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.
AB - With the advent of high-performance black-box models, interpretability is becoming a hot topic today in machine learning. While a lot of research isdone 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.
KW - Machine learning
KW - Interpretability
KW - Guidelines
M3 - Conference contribution
BT - EGC Workshop on Advances in Interpretable Machine Learning and Artificial Intelligence
CY - Metz
ER -