Rule-based explanation explicitly state the decision boundary. Given this fact, we hypothesized that these explanations improve a participant’s understanding of system behavior, causing an improved task performance compared to example-based explanations.
rule-based explanations indeed seem to allow participants to more accurately identify the factor from a situation that was decisive in the system’s advice. However, rule-based nor example-based explanation allowed participants to learn to predict system behavior.
However, rule-based explanations are very factual and provide little information to convince the participant of the correctness of a given advice.
An example-based explanation refers to historical situations in which the advice was found to be true or false.
Such example or instance-based explanations are often used between humans, as they illustrate past behavior and allow for generalization to new situations.
Example-based explanations were generally less preferred: many participants noted that they were too coarse and impression-based to be useful.