Title: Default for Evaluation - Introduction to Explainable AI
Course: Introduction to Explainable AI

Q: Explanations can be used to learn from AIs.
- True
- False
A: True

Q: Explaining an AI's decisions can make it more likely for humans to miss errors.
- True
- False (Explanations are another part the clinician has to pay attention to and can therefore lead to them missing other factors.)
A: True

Q: Why is clinical imaging well-suited for applying AI?
- Large historical datasets available
- All relevant information contained in the image
- Clinical images look similar for vastly different people
- Good predictors on images are easy do describe in mathematical terms
A: Large historical datasets available; All relevant information contained in the image

Q: If an AI system has similar performance as a clinician, it also makes similar mistakes.
- True
- False
A: False

Q: It can lead to problems if an AI is performing its task extremely well.
- True
- False (High performing AI systems can introduce automation bias, where the users of the well performing AI system simply accept its decisions instead of critically evaluating them.)
A: True