Accumulated Local Effects (ALE) is a widely-used explainability method for
isolating the average effect of a feature on the output, because it handles
cases with correlated features well. However, it has two limitations. First, it
does not quantify the deviation of instance-level (local) effects from the
average (global) effect, known as heterogeneity. Second, for estimating the
average effect, it partitions the feature domain into user-defined, fixed-sized
bins, where different bin sizes may lead to inconsistent ALE estimations. To
address these limitations, we propose Robust and Heterogeneity-aware ALE
(RHALE). RHALE quantifies the heterogeneity by considering the standard
deviation of the local effects and automatically determines an optimal
variable-size bin-splitting. In this paper, we prove that to achieve an
unbiased approximation of the standard deviation of local effects within each
bin, bin splitting must follow a set of sufficient conditions. Based on these
conditions, we propose an algorithm that automatically determines the optimal
partitioning, balancing the estimation bias and variance. Through evaluations
on synthetic and real datasets, we demonstrate the superiority of RHALE
compared to other methods, including the advantages of automatic bin splitting,
especially in cases with correlated features.Comment: Accepted at ECAI 2023 (European Conference on Artificial
Intelligence