2019/02/26 by Sebastian Lapuschkin, Stephan Wäldchen, Alexander Binder +3 · 3 citations
Computer Science · Mathematics · #cs.AI #cs.CV #cs.LG #cs.NE #stat.ML
paper · pdf · doi:10.1038/s41467-019-08987-4
Accepted for publication in Nature Communications
arxiv created 2019/02/26 · arxiv updated 2019/02/28
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.