In this chapter, the authors present an algorithm that addresses the issue. They focus on pairwise interactions that involve pairs of explanatory variables, but the algorithm can be easily extended to interactions involving a larger number of variables. In particular, the algorithm identifies interactions between pairs of variables and takes them into account when constructing break-down plots. Interaction means that the effect of an explanatory variable depends on the value(s) of other variable(s). The authors present an algorithm that allows including interactions in the BD plots. However, in the case of models with interactions, iBD plots provide more correct explanations. Though the numerical complexity of the iBD procedure is quadratic, it may be time-consuming in case of models with a large number of explanatory variables. It is also worth noting that the presented procedure of identification of interactions is not based on any formal statistical-significance test.