Chapter 9, The Counterfactual Explanations Method

1. A true positive prediction does not require a justification. (True|False)

   False. Any prediction should be justified, whether it is true or false.

2. Justification by showing the accuracy of the model will satisfy a user. (True|False)

   False. A model can be accurate but for the wrong reasons, whatever they may be.

3. A user needs to believe an AI prediction. (True|False)

   True. A user will not trust a prediction without a certain amount of belief.

   False. In some cases, such as a medical diagnosis, some truths are difficult to believe.

4. A counterfactual explanation is unconditional. (True|False)

   True.

5. The counterfactual explanation method will vary from one model to another. (True|False)

   False. A counterfactual explanation is model-agnostic.

6. A counterfactual data point is found with a distance function. (True|False...