Abstract
Current learning machines have successfully solved hard application problems, reaching high accuracy and displaying seemingly intelligent behavior. Here we apply recent techniques for explaining decisions of state-of-the-art learning machines and analyze various tasks from computer vision and arcade games. This showcases a spectrum of problem-solving behaviors ranging from naive and short-sighted, to well-informed and strategic. We observe that standard performance evaluation metrics can be oblivious to distinguishing these diverse problem solving behaviors. Furthermore, we propose our semi-automated Spectral Relevance Analysis that provides a practically effective way of characterizing and validating the behavior of nonlinear learning machines. This helps to assess whether a learned model indeed delivers reliably for the problem that it was conceived for. Furthermore, our work intends to add a voice of caution to the ongoing excitement about machine intelligence and pledges to evaluate and judge some of these recent successes in a more nuanced manner.
| Original language | English |
|---|---|
| Article number | 1096 |
| Journal | Nature Communications |
| Volume | 10 |
| Issue number | 1 |
| DOIs | |
| Publication status | Published - 2019 Dec 1 |
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ASJC Scopus subject areas
- Chemistry(all)
- Biochemistry, Genetics and Molecular Biology(all)
- Physics and Astronomy(all)
Cite this
Unmasking Clever Hans predictors and assessing what machines really learn. / Lapuschkin, Sebastian; Wäldchen, Stephan; Binder, Alexander; Montavon, Grégoire; Samek, Wojciech; Muller, Klaus.
In: Nature Communications, Vol. 10, No. 1, 1096, 01.12.2019.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Unmasking Clever Hans predictors and assessing what machines really learn
AU - Lapuschkin, Sebastian
AU - Wäldchen, Stephan
AU - Binder, Alexander
AU - Montavon, Grégoire
AU - Samek, Wojciech
AU - Muller, Klaus
PY - 2019/12/1
Y1 - 2019/12/1
N2 - Current learning machines have successfully solved hard application problems, reaching high accuracy and displaying seemingly intelligent behavior. Here we apply recent techniques for explaining decisions of state-of-the-art learning machines and analyze various tasks from computer vision and arcade games. This showcases a spectrum of problem-solving behaviors ranging from naive and short-sighted, to well-informed and strategic. We observe that standard performance evaluation metrics can be oblivious to distinguishing these diverse problem solving behaviors. Furthermore, we propose our semi-automated Spectral Relevance Analysis that provides a practically effective way of characterizing and validating the behavior of nonlinear learning machines. This helps to assess whether a learned model indeed delivers reliably for the problem that it was conceived for. Furthermore, our work intends to add a voice of caution to the ongoing excitement about machine intelligence and pledges to evaluate and judge some of these recent successes in a more nuanced manner.
AB - Current learning machines have successfully solved hard application problems, reaching high accuracy and displaying seemingly intelligent behavior. Here we apply recent techniques for explaining decisions of state-of-the-art learning machines and analyze various tasks from computer vision and arcade games. This showcases a spectrum of problem-solving behaviors ranging from naive and short-sighted, to well-informed and strategic. We observe that standard performance evaluation metrics can be oblivious to distinguishing these diverse problem solving behaviors. Furthermore, we propose our semi-automated Spectral Relevance Analysis that provides a practically effective way of characterizing and validating the behavior of nonlinear learning machines. This helps to assess whether a learned model indeed delivers reliably for the problem that it was conceived for. Furthermore, our work intends to add a voice of caution to the ongoing excitement about machine intelligence and pledges to evaluate and judge some of these recent successes in a more nuanced manner.
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U2 - 10.1038/s41467-019-08987-4
DO - 10.1038/s41467-019-08987-4
M3 - Article
C2 - 30858366
AN - SCOPUS:85062765505
VL - 10
JO - Nature Communications
JF - Nature Communications
SN - 2041-1723
IS - 1
M1 - 1096
ER -