Real-World Efficacy of Explainable Artificial Intelligence using the SAGE Framework and Scenario-Based Design
Year: 2024
Type: article
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.
Source: Applied Artificial Intelligence
Authors: Eleanor Mill, Wolfgang Garn, Chris Turner
Institution: University of Surrey
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,195
OpenAlex ID: W4404726157