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InsightBoard: An Interactive Multi-Metric Visualization and Fairness Analysis Plugin for TensorBoard

Ray Zeyao Chen, Christan Grant

Abstract

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.

InsightBoard: An Interactive Multi-Metric Visualization and Fairness Analysis Plugin for TensorBoard

Abstract

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.

Paper Structure

This paper contains 12 sections, 3 figures, 3 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. System Architecture and Implementation
  4. Backend Processing Pipeline
  5. Frontend Design and Interactive Visualization
  6. Fairness Metrics and Interaction Design
  7. Experiments and Case Studies
  8. Implementation Details
  9. System Overhead Evaluation
  10. Case Study: Correlation-Driven Debugging and Fairness Diagnosis
  11. Key Findings
  12. Conclusion

Figures (3)

  • Figure 1: InsightBoard System Architecture. The backend plugin intercepts data requests to perform on-the-fly metric aggregation and fairness calculations, serving this processed data to a new frontend dashboard.
  • Figure 2: The Fairness Dashboard visualizing IN-distribution evaluation. The radar chart axes represent disaggregated mAP across specific subgroups (e.g., Male/Daytime vs. Female/Nighttime). The area of the polygon allows practitioners to visually compare the overall equity footprint of competing model runs.
  • Figure 3: The configuration interface. The left panel shows specific hyperparameter deltas (e.g., fairness penalty weights), while the linked timeline on the right displays the corresponding real-time impact on the Equalized Odds gap during the training progression.