Artificial Intelligence algorithms have now become pervasive in multiple
high-stakes domains. However, their internal logic can be obscure to humans.
Explainable Artificial Intelligence aims to design tools and techniques to
illustrate the predictions of the so-called black-box algorithms. The
Human-Computer Interaction community has long stressed the need for a more
user-centered approach to Explainable AI. This approach can benefit from
research in user interface, user experience, and visual analytics. This paper
proposes a visual-based method to illustrate rules paired with feature
importance. A user study with 15 participants was conducted comparing our
visual method with the original output of the algorithm and textual
representation to test its effectiveness with users.

人工智能算法在多个高风险领域中已变得无处不在，但其内部逻辑可能对于人类而言难以理解。可解释的人工智能旨在设计工具和技术，以说明所谓的黑匣子算法的预测。人机交互界长期强调对可解释人工智能采用更加以用户为中心的方法的需求。本文提出了一种基于可视化的方法，以说明规则与特征重要性的配对。通过与算法的原始输出和文本表示进行比较，使用了包括 15 个参与者的用户研究，测试了我们的可视化方法在用户中的有效性。

Fiper: 结合规则和特征重要性的基于视觉的解释

Fiper: a Visual-based Explanation Combining Rules and Feature Importance

Explainable recommender systems (RS) have traditionally followed a
one-size-fits-all approach, delivering the same explanation level of detail to
each user, without considering their individual needs and goals. Further,
explanations in RS have so far been presented mostly in a static and
non-interactive manner. To fill these research gaps, we aim in this paper to
adopt a user-centered, interactive explanation model that provides explanations
with different levels of detail and empowers users to interact with, control,
and personalize the explanations based on their needs and preferences. We
followed a user-centered approach to design interactive explanations with three
levels of detail (basic, intermediate, and advanced) and implemented them in
the transparent Recommendation and Interest Modeling Application (RIMA). We
conducted a qualitative user study (N=14) to investigate the impact of
providing interactive explanations with varying level of details on the users'
perception of the explainable RS. Our study showed qualitative evidence that
fostering interaction and giving users control in deciding which explanation
they would like to see can meet the demands of users with different needs,
preferences, and goals, and consequently can have positive effects on different
crucial aspects in explainable recommendation, including transparency, trust,
satisfaction, and user experience.

通过用户中心、交互式的多级解释模型，提供不同层次的解释，让用户可以根据需求和偏好自定义解释，研究表明这种方法可以提高透明度、信任感、满意度和用户体验。