Change Detection for Local Explainability in Evolving Data Streams. (arXiv:2209.02764v1 [cs.LG])

As complex machine learning models are increasingly used in sensitive
applications like banking, trading or credit scoring, there is a growing demand
for reliable explanation mechanisms. Local feature attribution methods have
become a popular technique for post-hoc and model-agnostic explanations.
However, attribution methods typically assume a stationary environment in which
the predictive model has been trained and remains stable. As a result, it is
often unclear how local attributions behave in realistic, constantly evolving
settings such as streaming and online …

arxiv change data data streams detection explainability