Explainability

Explainability is the ability of an AI system to provide clear and understandable explanations of its decision-making processes to users. It is essential for building trust and credibility in AI systems, identifying and correcting errors and biases, and ensuring that AI systems align with human values and ethical principles. 

There are various methods for achieving explainability, including model-based, rule-based, and example-based approaches. 

The development of explainable AI is an active area of research, although it is doubtful that full explainability of the newer, powerful neural network AI is actually achievable.