Explainability refers to the capacity of an AI system to deliver transparent and comprehensible explanations of its internal workings. It is a vitally important feature for earning trust and strengthening the credibility of AI systems, detecting and remedying errors and biases, as well as making certain AI systems stick to human values and ethical protocols. Different approaches such as those found in model-based, rule-based, and example-based techniques can be utilized to positively impact explainability.
