Model interpretability is the degree to which a human can understand the cause of a model's decision. As a research field and methodological approach, it investigates the internal logic, mechanisms, and input-output relationships of predictive or descriptive models to make their behavior comprehensible. Key characteristics include assessing model transparency, identifying influential factors, and explaining predictions, with its significance derived from fostering trust, enabling model debugging and improvement, facilitating scientific discovery, ensuring fairness, and supporting reliable deployment in critical applications, particularly for complex or opaque models.