The {required element} of the system is {interpretable} in line with the safety case requirements, to the {intended audience} in the {intended context}
The importance of interpretability in machine learning models is highlighted, emphasizing the need for explanations that align with safety case requirements. Interpretability methods should be tailored to meet specific requirements and be understandable to the intended audience within the intended context.
The {required element} of the system is {interpretable} in line with the safety case requirements, to the {intended audience} in the {intended context}
Argument over {interpretability requirements}
{Explanations} are appropriate for {audience}
{Audience}
Argument over appropriateness of {explanations} to {audience}
{Audience Evidence}
{Explanations} produced at appropriate {times}
{Times that explanations
are provided}
Argument over appropriateness of {explanations} {time}
{Time Evidence}
{interpretability method} is faithful to {model} process
{ML Model}
Argument over faithfulness of {interpretability method}
{Explainability method
Evidence}
Implemented {interpretability method} appropriate for {requirements}, i.e. the correct thing is being explained