fixes #328

Author: christophM

manuscript/evaluation.qmd

@@ -106,6 +106,8 @@ A complex transformation of features might be less comprehensible than the origi
 Many machine learning models only give predictions without a statement about the models confidence that the prediction is correct.
 If the model predicts a 4% probability of cancer for one patient, is it as certain as the 4% probability that another patient, with different feature values, received?
 An explanation that includes the model's certainty is very useful.
+In addition, the explanation itself may be a model or an estimate based on data and therefore itself subject to uncertainty.
+This uncertainty of the explanation is also a relevant property of the explanation itself.
 - **Degree of Importance**: How well does the explanation reflect the importance of features or parts of the explanation?
 For example, if a decision rule is generated as an explanation for an individual prediction, is it clear which of the conditions of the rule was the most important?
 - **Novelty**: Does the explanation reflect whether a data instance to be explained comes from a "new" region far removed from the distribution of training data?