What does interpretability refer to in AI models?

Interpretability in AI models refers to the ability to explain a model's reasoning clearly. This concept is essential because as AI and machine learning systems are employed in more critical decisions—like in healthcare, financial services, or legal contexts—understanding how these models arrive at their conclusions is vital for trust, accountability, and compliance with regulations.

When a model is interpretable, stakeholders can grasp the logic behind the predictions or decisions it makes. This is particularly important for evaluating whether the model is fair, unbiased, and aligns with ethical standards. Various methods can enhance interpretability, such as creating visual explanations, simplifying complex algorithms, or using inherently interpretable models like linear regression or decision trees.

In contrast, aspects such as the complexity of model algorithms, methods of model training through data, or the size of datasets do not directly relate to how well the reasoning behind a model's predictions can be clarified. They can influence predictive performance but do not ensure that the reasoning can be communicated clearly to users or decision-makers.