Decoupling Pixel Flipping and Occlusion Strategy for Consistent XAI
  Benchmarks
Stefan Blücher Johanna Vielhaben Nils Strodthoff
Feature removal is a central building block for eXplainable AI (XAI), both
for occlusion-based explanations (Shapley values) as well as their evaluation
(pixel flipping, PF). However, occlusion strategies can vary significantly from
simple mean replacement up to inpainting with state-of-the-art diffusion
models. This ambiguity limits the usefulness of occlusion-based approaches. For
example, PF benchmarks lead to contradicting rankings. This is amplified by
competing PF measures: Features are either removed starting with most
influential first (MIF) or least influential first (LIF). This study proposes
two complementary perspectives to resolve this disagreement problem. Firstly,
we address the common criticism of occlusion-based XAI, that artificial samples
lead to unreliable model evaluations. We propose to measure the reliability by
the R(eference)-Out-of-Model-Scope (OMS) score. The R-OMS score enables a
systematic comparison of occlusion strategies and resolves the disagreement
problem by grouping consistent PF rankings. Secondly, we show that the
insightfulness of MIF and LIF is conversely dependent on the R-OMS score. To
leverage this, we combine the MIF and LIF measures into the symmetric relevance
gain (SRG) measure. This breaks the inherent connection to the underlying
occlusion strategy and leads to consistent rankings. This resolves the
disagreement problem, which we verify for a set of 40 different occlusion
strategies.