  Different prediction models might perform equally well (Rashomon set) in the
same task, but offer conflicting interpretations and conclusions about the
data. The Rashomon effect in the context of Explainable AI (XAI) has been
recognized as a critical factor. Although the Rashomon set has been introduced
and studied in various contexts, its practical application is at its infancy
stage and lacks adequate guidance and evaluation. We study the problem of the
Rashomon set sampling from a practical viewpoint and identify two fundamental
axioms - generalizability and implementation sparsity that exploring methods
ought to satisfy in practical usage. These two axioms are not satisfied by most
known attribution methods, which we consider to be a fundamental weakness. We
use the norms to guide the design of an $\epsilon$-subgradient-based sampling
method. We apply this method to a fundamental mathematical problem as a proof
of concept and to a set of practical datasets to demonstrate its ability
compared with existing sampling methods.
