Description: Model interpretability refers to the degree to which a human can understand the cause of a decision made by a machine learning model. This concept is crucial in the field of artificial intelligence, as it allows users to comprehend how and why a model arrives at certain conclusions. Interpretability can be classified into two categories: intrinsic interpretability, which refers to models that are inherently understandable, such as linear regression or decision trees, and post-hoc interpretability, which refers to techniques applied to more complex models, like neural networks, to explain their decisions. The importance of interpretability lies in its ability to foster trust in AI systems, facilitate the identification of biases and errors, and comply with ethical and legal regulations. In a world where automated decisions increasingly impact daily life, the ability to break down and understand these decisions becomes essential to ensure transparency and accountability in the use of artificial intelligence.
History: The concept of interpretability in machine learning models began to gain attention in the 1990s when researchers started to recognize the need to understand the decisions of complex models. However, it was in the 2010s that interest surged, driven by the rise of neural networks and deep learning. In 2016, the term ‘interpretability’ was formalized within the AI community, leading to conferences and workshops dedicated to this topic. Since then, various techniques and tools have been developed to enhance the interpretability of complex models, such as LIME and SHAP.
Uses: Model interpretability is used in various fields, including medicine, where understanding a model’s diagnostic decisions is crucial; in finance, to explain credit decisions; and in the legal realm, to ensure that automated decisions are fair and transparent. It is also applied in the automotive industry, where autonomous driving systems must be understandable to users and regulators.
Examples: An example of interpretability in action is the use of LIME (Local Interpretable Model-agnostic Explanations) to explain the decisions of an image classification model. Another case is the use of SHAP (SHapley Additive exPlanations) in credit models to identify which features most influence the decision to grant a loan.
