InsightBoard: 一个用于TensorBoard的交互式多指标可视化和公平性分析插件
InsightBoard: An Interactive Multi-Metric Visualization and Fairness Analysis Plugin for TensorBoard
AI总结：
InsightBoard通过统一界面整合多指标可视化与切片公平性诊断，帮助研究者在训练过程中分析训练动态、性能指标和子群体差异，提升模型公平性诊断的及时性和准确性。
摘要：
现代应用于安全关键领域的机器学习系统需要不仅能够查看整体性能，还能够分析训练动态如何影响子群体公平性。现有训练仪表盘主要支持单指标监控，难以分析异质指标之间的关系或诊断训练过程中的子群体差异。我们提出了InsightBoard，一个集成同步多指标可视化与基于切片的公平性诊断的TensorBoard插件。InsightBoard通过联动多视图图表、相关性分析和用户定义切片上的标准群体公平指标，使研究者能够联合检查训练动态、性能指标和子群体差异。通过在BDD100k数据集上使用YOLOX的案例研究，我们证明了即使模型在整体性能上表现优异，也可能存在显著的种族和环境差异，这些差异在传统监控下会隐藏起来。通过在训练过程中提供公平性诊断，InsightBoard支持更早、更充分的模型检查，而无需修改现有训练流程或引入额外的数据存储。
英文摘要：
Modern machine learning systems deployed in safety-critical domains require visibility not only into aggregate performance but also into how training dynamics affect subgroup fairness over time. Existing training dashboards primarily support single-metric monitoring and offer limited support for examining relationships between heterogeneous metrics or diagnosing subgroup disparities during training. We present InsightBoard, an interactive TensorBoard plugin that integrates synchronized multi-metric visualization with slice-based fairness diagnostics in a unified interface. InsightBoard enables practitioners to jointly inspect training dynamics, performance metrics, and subgroup disparities through linked multi-view plots, correlation analysis, and standard group fairness indicators computed over user-defined slices. Through case studies with YOLOX on the BDD100k dataset, we demonstrate that models achieving strong aggregate performance can still exhibit substantial demographic and environmental disparities that remain hidden under conventional monitoring. By making fairness diagnostics available during training, InsightBoard supports earlier, more informed model inspection without modifying existing training pipelines or introducing additional data stores.
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