Description: Interpretable models are those that provide clear information on how predictions are made, allowing users to understand and trust the results generated. These models are fundamental in contexts where transparency is crucial, such as decision-making in regulated sectors, medicine, or justice. Unlike ‘black box’ models, which are complex and difficult to interpret, interpretable models offer a simpler representation of the relationships between input variables and predictions. This is achieved through techniques that highlight the importance of each feature, intuitive visualizations, and clear decision rules. Interpretability not only enhances user trust in the model but also facilitates the identification of biases and errors, which is essential for the continuous improvement of artificial intelligence systems. In summary, interpretable models are a valuable tool for ensuring that automated decisions are understandable and fair, thus promoting broader adoption of artificial intelligence in various applications.
History: The concept of interpretable models has evolved over the past few decades, especially with the rise of machine learning and artificial intelligence. As models became more complex, the need to understand how and why they made decisions emerged. In the 1990s, methods began to be developed to make models more transparent, but it was in the 2010s that interest in interpretability surged, driven by ethical concerns and the need to comply with regulations in sectors such as healthcare and finance.
Uses: Interpretable models are used in a variety of fields, including medicine, where practitioners need to understand diagnostic decisions; in finance, to assess credit risks; and in the legal field, where it is crucial to understand automated decisions that may affect individuals. They are also applied in marketing to segment customers and in various industries to improve safety and reliability of automated systems.
Examples: An example of an interpretable model is logistic regression, which allows analysts to see how each variable affects the probability of an outcome. Another example is the use of decision trees, which provide a clear visual representation of the decisions made. In the field of anomaly detection, models like Isolation Forest can be used to identify unusual data points in an understandable manner.
