Interpretability

Interpretability refers to the degree to which a human can understand and explain the predictions or decisions made by a machine learning model or artificial intelligence system. It encompasses techniques and methodologies that enhance transparency, allowing users to gain insights into how models arrive at their conclusions. High interpretability is crucial for trust, accountability, and compliance in AI applications, particularly in sectors like finance, healthcare, and law. By prioritizing interpretability, organizations can improve decision-making and foster user confidence in advanced data-driven technologies.

‍