IXAII让AI解释更互动可定制，提升决策透明度
IXAII: An Interactive Explainable Artificial Intelligence Interface for Decision Support Systems
- 集成四种可解释AI方法，支持用户自定义解释内容与形式
- 针对五类用户设计不同视图，增强交互控制权
- 专家与普通用户均认为其有效提升AI透明性
尽管已有多种事后可解释AI方法，但多数为静态展示，忽视用户视角，限制了实际应用效果。为此，我们开发了交互式可解释人工智能系统IXAII，整合LIME、SHAP、Anchors和DiCE四种解释方法。原型系统为五类用户群体提供定制化视图，并赋予用户对解释内容与呈现形式的自主权。通过专家与普通用户的访谈评估，结果表明：提供多模式可视化与多样化解释的IXAII，被普遍认为有助于提升决策过程的透明度。该研究在可解释AI方法、交互设计与实际部署之间建立桥梁，为人类-AI协作提供了新范式。
原文摘要 · Abstract (English)
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.
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