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Calvin Risk Platform
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Quality Assessment and Automation (QAA)
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Model Quality Metrics

Obtain a comprehensive view on model quality

Upload your AI model and training/test data to calculate 20+ metrics corresponding to 6 quality dimensions.

Performance and Robustness

Compute performance scores from different statistical perspectives such as Temporal Degeneration or Prediction Interval Coverage. Compute robustness scores by perturbing queries in multiple ways including random and adverserial perturbations.

Fairness and Explainability

Estimate fairness from multiple perspectives including group vs. individual fairness, enthropy of predicted results, bias ratios and differences, and others. Estimate explainability aspects such as linearity, model size, or Shap Length.

Accountability and Safety

Assess Accountability based on Governance, Data, Performance Goals, and Monitoring maturity. Estimate Safety based on Human Oversight, End-user Control, Fallback Measures, and Automated Control.

Related Features

Risk Frequency Assessment

Report the aggregated annual risk frequency in 6 dimensions, which enables historical comparisons and calculation of the total estimated loss and value at risk .

Learn more

Severity Assessment

Estimate economic losses based on the business use case. Combine model risk frequencies and severity to obtain a unified portfolio risk distribution with time tracking and to discover the principal risk contributions across models, use cases, and risk dimensions.

Learn more

Additional Resources

Blog: Navigating the Ethics of AI

Navigating the Ethics of AI: An In-depth Analysis of Fairness

Blog: How Secure is Your AI?

How Secure is Your AI? Risk Analysis for Cross-Selling Prediction Models

Blog: AI Risk Management in the DACH region

AI Risk Management in the DACH region: Overviews and Trends for AI in 2024

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