AI Model Visualization & Explainability Reports

Help stakeholders understand your model decisions with interactive explainability visualizations.

AI Model Visualization

Making AI Transparent

As AI systems become more prevalent in decision-making processes, the need for transparency and explainability has never been greater. Our platform enables AI engineers to create and share interactive visualizations that help stakeholders understand how models make decisions, building trust and facilitating better collaboration between technical and non-technical teams.

Explainability Techniques

Our platform supports hosting various explainability visualizations:

Explainability Techniques
  • SHAP (SHapley Additive exPlanations) value visualizations for feature importance
  • LIME (Local Interpretable Model-agnostic Explanations) for local decision explanation
  • Grad-CAM and other attention visualization techniques for computer vision models
  • Decision tree visualizations for tree-based models
  • Interactive confusion matrices and performance metrics
  • Counterfactual explanations showing how inputs could change to alter predictions

How It Works

Creating and sharing model explainability reports is straightforward:

  • Generate explainability visualizations using libraries like SHAP, LIME, or custom tools
  • Export the visualizations as interactive HTML/JavaScript files
  • Upload the files to our platform
  • Share the generated URL with stakeholders, clients, or regulatory bodies

Use Cases

AI teams are using our platform for various explainability needs:

  • Regulatory compliance in industries with explainability requirements
  • Stakeholder presentations to explain model behavior
  • Internal model auditing and bias detection
  • Educational resources for explaining AI concepts
  • Client-facing dashboards showing how AI systems make recommendations
  • Research publications with interactive model analysis

Benefits of Interactive Explainability

Our platform offers significant advantages for AI explainability:

  • Transform complex model behavior into intuitive visualizations
  • Enable non-technical stakeholders to understand model decisions
  • Build trust in AI systems through transparency
  • Facilitate collaboration between data scientists and domain experts
  • Identify and address potential biases or issues in models
  • Meet regulatory requirements for AI explainability
  • Differentiate your AI solutions with transparent decision-making

Ready to make your AI models more transparent?

Experience the power of interactive explainability visualizations with our simple hosting platform.