V-SHiNE: A Virtual Smart Home Framework for Explainability Evaluation
Mersedeh Sadeghi, Simon Scholz, Max Unterbusch, Andreas Vogelsang
TL;DR
V-SHiNE is a browser-based simulation framework designed to evaluate the quality and impact of explanations in smart home systems, enabling scalable, realistic, and user-centered assessment methods.
Contribution
It introduces a novel, flexible platform for evaluating explainability in smart homes, addressing a key methodological gap in the field.
Findings
Feasibility demonstrated with 159 participants
Supports customizable environments and explanation engines
Facilitates scalable, reproducible evaluations
Abstract
Explanations are essential for helping users interpret and trust autonomous smart-home decisions, yet evaluating their quality and impact remains methodologically difficult in this domain. V-SHiNE addresses this gap: a browser-based smarthome simulation framework for scalable and realistic assessment of explanations. It allows researchers to configure environments, simulate behaviors, and plug in custom explanation engines, with flexible delivery modes and rich interaction logging. A study with 159 participants demonstrates its feasibility. V-SHiNE provides a lightweight, reproducible platform for advancing user-centered evaluation of explainable intelligent systems
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Taxonomy
TopicsExplainable Artificial Intelligence (XAI) · AI in Service Interactions · Scientific Computing and Data Management