ScatterUQ: Interactive Uncertainty Visualizations for Multiclass Deep Learning Problems

Uncertainty visualization helps users understand when AI models are confident versus uncertain: ScatterUQ creates interactive visual displays that show whether a model is confident in its predictions, unsure between multiple classes, or encountering data it wasn't trained on—helping both ML engineers and non-technical users gauge model reliability.

The system addresses a real gap in responsible AI deployment: While modern deep learning methods can now provide calibrated confidence scores and detect out-of-distribution data, this information was difficult to communicate visually at scale; ScatterUQ solves this by converting complex uncertainty information into understandable 2-D scatter plots.

Interactive comparison enables better decision-making about model failures: Users can hover over predictions to compare test examples with training data, allowing them to understand why a model is uncertain and decide what action to take—whether that's collecting more training data, manually reviewing predictions, or adjusting the system.

The approach was validated on realistic scenarios: The system was tested on image classification tasks where models needed to distinguish between in-distribution examples (Fashion-MNIST images the model was trained on), uncertain cases, and out-of-distribution examples (MNIST digits the model had never seen), demonstrating practical applicability.

Visual transparency supports responsible AI governance: By making model uncertainty explicit and interpretable, ScatterUQ enables stakeholders to identify where models may fail and take appropriate precautions before deployment in high-stakes applications.