How to Make Your Model Pay for Its Mistakes
A translator who jumps at every rustle in the hall starts mixing up words by the end of the meeting. Their confidence in being right grows, while their accuracy drops. That is roughly how poorly calibrated probabilistic forecasts behave: the model claims a high probability for an event, but reality disagrees.