The analysis of the underlying data of an AI model examines how balanced and representative the data is for the specific application. Additionally, weaknesses or underrepresented data points are identified to ensure the optimal performance of the model.

When evaluating the performance of the AI model, application-specific metrics are calculated to determine how effectively the model fulfills its intended task. This provides insights into whether and under what additional safety measures the model can be deployed safely and reliably.

Uncertainty is an inherent aspect of any AI application, but it is not accounted for by all models. Properly quantified uncertainties can be used to assess whether predictions are trustworthy, leading to better decision-making and enhanced safety.

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The robustness evaluation examines whether the performance of the AI model remains consistent when exposed to various types of input disturbances. Safe deployment of a model can only be ensured if it is resilient to different and changing environmental conditions.

The safe deployment of an AI model and its decisions requires the traceability and interpretability of its results. By considering the explainability of the model, predictions and decisions become transparent and understandable.