Improving Explainability of Disentangled Representations using
  Multipath-Attribution Mappings
Lukas Klein João B. S. Carvalho Mennatallah El-Assady Paolo Penna Joachim M. Buhmann Paul F. Jaeger
Explainable AI aims to render model behavior understandable by humans, which
can be seen as an intermediate step in extracting causal relations from
correlative patterns. Due to the high risk of possible fatal decisions in
image-based clinical diagnostics, it is necessary to integrate explainable AI
into these safety-critical systems. Current explanatory methods typically
assign attribution scores to pixel regions in the input image, indicating their
importance for a model's decision. However, they fall short when explaining why
a visual feature is used. We propose a framework that utilizes interpretable
disentangled representations for downstream-task prediction. Through
visualizing the disentangled representations, we enable experts to investigate
possible causation effects by leveraging their domain knowledge. Additionally,
we deploy a multi-path attribution mapping for enriching and validating
explanations. We demonstrate the effectiveness of our approach on a synthetic
benchmark suite and two medical datasets. We show that the framework not only
acts as a catalyst for causal relation extraction but also enhances model
robustness by enabling shortcut detection without the need for testing under
distribution shifts.