  AI business process applications automate high-stakes business decisions
where there is an increasing demand to justify or explain the rationale behind
algorithmic decisions. Business process applications have ordering or
constraints on tasks and feature values that cause lightweight, model-agnostic,
existing explanation methods like LIME to fail. In response, we propose a local
explanation framework extending LIME for explaining AI business process
applications. Empirical evaluation of our extension underscores the advantage
of our approach in the business process setting.
