Organizations of all kinds, whether public or private, profit-driven or
non-profit, and across various industries and sectors, rely on dashboards for
effective data visualization. However, the reliability and efficacy of these
dashboards rely on the quality of the visual and data they present. Studies
show that less than a quarter of dashboards provide information about their
sources, which is just one of the expected metadata when provenance is
seriously considered. Provenance is a record that describes people,
organizations, entities, and activities that had a role in the production,
influence, or delivery of a piece of data or an object. This paper aims to
provide a provenance representation model, that entitles standardization,
modeling, generation, capture, and visualization, specifically designed for
dashboards and its visual and data components. The proposed model will offer a
comprehensive set of essential provenance metadata that enables users to
evaluate the quality, consistency, and reliability of the information presented
on dashboards. This will allow a clear and precise understanding of the context
in which a specific dashboard was developed, ultimately leading to better
decision-making.

该论文旨在提供一种专为仪表板及其可视化和数据组件设计的溯源表示模型，以提供一套完整的基本溯源元数据，使用户能够评估仪表板上呈现的信息的质量、一致性和可靠性，从而在特定仪表板开发的上下文中实现清晰而准确的理解，最终促进更好的决策制定。

仪表盘渊源建模

Modeling the Dashboard Provenance

Fraud detection is a difficult problem that can benefit from predictive
modeling. However, the verification of a prediction is challenging; for a
single insurance policy, the model only provides a prediction score. We present
a case study where we reflect on different instance-level model explanation
techniques to aid a fraud detection team in their work. To this end, we
designed two novel dashboards combining various state-of-the-art explanation
techniques. These enable the domain expert to analyze and understand
predictions, dramatically speeding up the process of filtering potential fraud
cases. Finally, we discuss the lessons learned and outline open research
issues.

通过设计两个新颖的仪表板结合各种最新的解释技术，帮助欺诈检测人员分析和理解预测结果，从而快速过滤潜在的欺诈案件。