Description: Interpretability refers to the degree to which a human can understand the cause of a decision made by a model. In the context of machine learning and artificial intelligence, interpretability is crucial to ensure that models are not only accurate but also comprehensible. This is especially relevant in applications where automated decisions can have a significant impact on people’s lives, such as in various fields including healthcare, law enforcement, and banking. Interpretability allows users and model developers to understand how and why certain decisions are made, which in turn fosters trust in technology. There are different approaches to achieving interpretability, ranging from intrinsically interpretable models, such as decision trees, to post-hoc techniques that explain the decisions of more complex models, such as deep neural networks. The ability to interpret a model is also fundamental to identifying biases and errors, contributing to the continuous improvement of artificial intelligence systems.
History: The concept of interpretability in machine learning models has evolved over the past few decades. In the 1990s, the focus was primarily on model accuracy, but as artificial intelligence began to integrate into critical applications, the need to understand how these models worked emerged. In 2016, the term ‘explainable AI’ gained popularity, driven by growing concerns about transparency and ethics in artificial intelligence. Research such as Lipton’s (2016) began to address the need for methods that would allow humans to understand the decisions of complex models.
Uses: Interpretability is used in various fields, including healthcare, where predictive models assist in diagnosing diseases and understanding their decisions is crucial. In finance, it is applied to assess credit risks and detect fraud, allowing analysts to comprehend the reasons behind automated decisions. It is also fundamental in the development of responsible AI systems, where transparency is key to public acceptance and regulation.
Examples: An example of interpretability in action is the use of decision tree models in credit risk assessment, where each decision can be easily traced and explained. Another case is the use of interpretation techniques like LIME (Local Interpretable Model-agnostic Explanations) to explain the decisions of deep learning models in medical diagnosis, allowing doctors to understand the reasons behind the model’s recommendations.
