Interpretability in artificial intelligence (AI) and machine learning refers to how well humans can understand and explain the decisions or predictions made by a model. When a model is interpretable, its inner workings, reasoning processes, and outputs can be traced in a way that makes sense to people. This is especially important in fields where trust, transparency, and accountability are crucial, like healthcare, finance, and law.
Interpretability is not the same as accuracy. A highly accurate model could be a complex ‘black box’—such as many deep neural networks—where it’s hard to see why it makes certain predictions. On the other hand, a more interpretable model might be less accurate but offers clearer explanations for its behavior. For example, simple linear models or decision trees are generally easier to interpret because you can follow the logic behind their predictions step by step.
Why does interpretability matter? For starters, it helps build trust in AI systems. If users, stakeholders, or regulators can understand why a model makes a decision, they’re more likely to trust and adopt it. Interpretability also aids in debugging and improving models. When you can see which parts of the input influence the output, you can spot mistakes, biases, or areas where the model needs improvement. Importantly, in some domains, regulations require explanations for automated decisions. For example, the European Union’s GDPR includes a ‘right to explanation’ for algorithmic decisions affecting individuals.
There are different approaches to achieving interpretability. “Intrinsic interpretability” is about building models that are understandable by design, such as linear regression or small decision trees. “Post-hoc interpretability” involves using techniques to explain complex models after they’ve been trained. Examples include feature importance scores, saliency maps in image models, or local surrogate models like LIME that approximate a complex model‘s behavior in a specific case. The right approach depends on the application, the complexity of the data, and the level of transparency required.
Interpretability is sometimes confused with “explainability.” While the terms are often used interchangeably, some researchers make a distinction: interpretability is about how easy it is for humans to make sense of the model, while explainability refers to the tools and methods designed to provide those explanations.
As AI systems become more complex and are used in more sensitive applications, interpretability continues to be a major area of research and debate. It’s a central issue in discussions around AI ethics, responsible AI, and fairness, because being able to interpret a model‘s decisions can help uncover and address bias or unintended consequences.
