A bi-directional adversarial explainability for decision support

In this paper we present an approach to creating Bi-directional Decision Support System (DSS) as an intermediary between an expert (U) and a machine learning (ML) system for choosing an optimal solution. As a first step, such DSS analyzes the stability of expert decision and looks for critical values in data that support such a decision. If the expert’s decision and that of a machine learning system continue to be different, the DSS makes an attempt to explain such a discrepancy. We discuss a detailed description of this approach with examples. Three studies are included to illustrate some features of our approach.

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