Real-World Efficacy of Explainable Artificial Intelligence using the SAGE Framework and Scenario-Based Design

Abstract

This paper demonstrates a design and evaluation approach for delivering real world efficacy of an explainable artificial intelligence (XAI) model. The first of its kind, it leverages three distinct but complementary frameworks to support a user-centric and context-sensitive, post-hoc explanation for fraud detection. Using the principles of scenario-based design, it amalgamates two independent real-world sources to establish a realistic card fraud prediction scenario. The SAGE (Settings, Audience, Goals and Ethics) framework is then used to identify key context-sensitive criteria for model selection and refinement. The application of SAGE reveals gaps in the current XAI model design and provides opportunities for further model development. The paper then employs a functionally-grounded evaluation method to assess its effectiveness. The resulting explanation represents real-world requirements more accurately than established models.

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