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manuscript/06.0-example.Rmd

@@ -51,7 +51,7 @@ The chapters in this part cover the following example-based interpretability met
 By creating counterfactual instances, we learn about how the model makes its predictions and can explain individual predictions.
 - [Adversarial examples](#adversarial) are counterfactuals used to fool machine learning models. 
 The emphasis is on flipping the prediction and not explaining it. 
-- [Prototypes and criticisms](proto): Prototypes are a selection of representative instances from the data and criticisms are instances that are not well represented by those prototypes. [^critique]
+- [Prototypes and criticisms](#proto): Prototypes are a selection of representative instances from the data and criticisms are instances that are not well represented by those prototypes. [^critique]
 - [Influential instances](#influential) are the training data points that were the most influential for the parameters of a prediction model or the predictions itself. 
 Identifying and analysing influential instances helps to find problems with the data, debug the model and understand the model's behavior better.
 - [k-nearest neighbours model](#other-interpretable): An (interpretable) machine learning model based on examples.