Introduction

Regulators increasingly demand model explainability. Explainer dashboards—interactive visualizations of model explanations—help regulators understand model logic, identify bias, validate compliance. Designing dashboards for regulatory audiences requires clarity, rigor, and user-friendly interface.

Dashboard Components

Model overview: architecture, training data, performance metrics. Feature importance: which features drive predictions most? Individual explanations: for specific predictions, why did model decide X? Fairness metrics: are predictions biased? Performance by segment: does model perform differently for subgroups? Audit logs: decision trails for compliance.

Design Principles

Clarity: avoid jargon; explain concepts at regulatory audience level. Accuracy: show actual model internals, not simplified approximations. Interactivity: enable deep exploration (drill down into specific predictions). Traceability: link predictions to training data. Reproducibility: enable regulators to verify explanations independently.

Implementation

Use interactive BI tools (Tableau, Looker) or custom web interfaces. Ensure data security (segregated access for regulators only). Enable export/audit trails. Test with regulators during design to ensure usability.

Conclusion

Well-designed explainer dashboards facilitate regulatory oversight and build confidence in model governance.