Description: Explainable neural networks are a type of artificial intelligence architecture that incorporates mechanisms designed to enhance the interpretability of their decisions and processes. Unlike traditional neural networks, which often operate as ‘black boxes’, these networks allow users to understand how and why certain decisions are made. This is crucial in applications where transparency is essential, such as in the medical, financial, or legal fields. Explainable neural networks use techniques such as activation visualization, feature importance, and attention models to break down their internal processes. This not only helps developers fine-tune and improve models but also builds trust among end-users by providing a clear justification for automated decisions. The ability to explain the outcomes of artificial intelligence models is increasingly valued, as it promotes accountability and ethics in technology use. In a world where automated decisions can significantly impact people’s lives, explainable neural networks represent a step towards a more conscious and responsible use of artificial intelligence.
History: Explainable neural networks began to gain attention in the 2010s when growing concerns about the lack of transparency in artificial intelligence models led researchers and developers to seek ways to make these systems more understandable. In 2016, the term ‘explainable artificial intelligence’ (XAI) was popularized by the Defense Advanced Research Projects Agency (DARPA) in the U.S., which launched a program to develop methods that allow humans to understand and trust the decisions of AI systems. Since then, various techniques and approaches have been developed to enhance the interpretability of neural networks, including methods such as LIME (Local Interpretable Model-agnostic Explanations) and SHAP (SHapley Additive ExPlanations).
Uses: Explainable neural networks are used in various fields where transparency is crucial. In the medical sector, for example, they are applied to help medical professionals understand automated diagnostic decisions. In finance, they are used to assess credit risks and detect fraud, allowing analysts to comprehend the reasons behind model decisions. They are also relevant in the legal field, where a clear justification for automated decisions that may affect individuals is required.
Examples: An example of an explainable neural network is the use of attention models in disease diagnosis from medical images, where it is possible to visualize which parts of the image influenced the model’s decision. Another case is the use of LIME in credit risk prediction models, which allows analysts to see which applicant characteristics were most relevant to the model’s decision.
