IXAII: An Interactive Explainable Artificial Intelligence Interface for Decision Support Systems
This addresses the need for more user-centric and interactive explainable AI tools in decision support systems, though it is incremental as it builds on existing methods.
The paper tackled the problem of static post-hoc explainable AI methods by developing IXAII, an interactive interface that integrates LIME, SHAP, Anchors, and DiCE to offer tailored explanations for different user groups, with evaluation showing it is perceived as helpful for increasing transparency.
Although several post-hoc methods for explainable AI have been developed, most are static and neglect the user perspective, limiting their effectiveness for the target audience. In response, we developed the interactive explainable intelligent system called IXAII that offers explanations from four explainable AI methods: LIME, SHAP, Anchors, and DiCE. Our prototype provides tailored views for five user groups and gives users agency over the explanations' content and their format. We evaluated IXAII through interviews with experts and lay users. Our results indicate that IXAII, which provides different explanations with multiple visualization options, is perceived as helpful to increase transparency. By bridging the gaps between explainable AI methods, interactivity, and practical implementation, we provide a novel perspective on AI explanation practices and human-AI interaction.