Example-Based Explanations
Example-based explanations help you understand how an AI model makes decisions by referring to specific instances from the data. These methods are particularly useful when you want to see concrete, relatable cases that illustrate the model's reasoning. The main types of example-based methods include counterfactuals, prototypes, and criticisms.
Counterfactual explanations show what minimal changes to an input would have led to a different prediction from the model. This approach helps answer questions like, "What would need to change in this loan application for it to be approved instead of denied?" Prototypes are typical examples that represent a class or outcome — think of them as the most representative cases for a certain prediction. Criticisms, on the other hand, are unusual or problematic examples that help highlight the limitations or blind spots of the model.
Counterfactual explanation is a description of how an input would need to change for a model to yield a different output, showing the smallest modifications necessary to alter the prediction.
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Example-based explanations help you understand how an AI model makes decisions by referring to specific instances from the data. These methods are particularly useful when you want to see concrete, relatable cases that illustrate the model's reasoning. The main types of example-based methods include counterfactuals, prototypes, and criticisms.
Counterfactual explanations show what minimal changes to an input would have led to a different prediction from the model. This approach helps answer questions like, "What would need to change in this loan application for it to be approved instead of denied?" Prototypes are typical examples that represent a class or outcome — think of them as the most representative cases for a certain prediction. Criticisms, on the other hand, are unusual or problematic examples that help highlight the limitations or blind spots of the model.
Counterfactual explanation is a description of how an input would need to change for a model to yield a different output, showing the smallest modifications necessary to alter the prediction.
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