explAIner: A Visual Analytics Framework for Interactive and Explainable Machine Learning

Visual tools can make ML models more understandable: The explAIner system integrates explainability features directly into TensorBoard (a commonly used ML tool), allowing practitioners to understand how their models work without requiring specialized expertise in interpretation methods.

Multiple explanation methods help catch different problems: The framework combines eight different monitoring and steering mechanisms—including quality monitoring, provenance tracking, and model comparison—recognizing that no single explanation technique reveals all of a model's limitations.

Interactive refinement creates a feedback loop for better models: Rather than treating model development as a one-time process, the framework enables users to diagnose issues, understand why they occur, and then iteratively refine models based on those insights, leading to what the authors call "an informed machine learning process."

Trust in AI systems requires transparency at multiple levels: The system includes provenance tracking and trust-building mechanisms, suggesting that users need to see not just what a model predicts, but also how it was built, what data it used, and how it compares to alternatives.

Explainability tools work across different skill levels: The user study included participants with varying expertise, and the evaluation confirmed the system's workflow was effective across these groups, suggesting that well-designed visual analytics can democratize access to model understanding.