XAI-CHEST: Interpretability for Black-Box Channel Estimation in 6G

In this article, we delve into the realm of Explainable AI (XAI), a rapidly growing field that seeks to provide insights into the decision-making process of complex machine learning models. By leveraging the concepts of interpretability and regularization, XAI aims to build trust between humans and machines, ensuring that the latter make decisions in a transparent and accountable manner.
To achieve this goal, we employ the utilities model (U) and the noise model (N), both built on the foundations of feedforward neural networks (FNNs). The U model is trained to optimize the performance of the FNN architecture, while the N model is designed to induce noise in the FNN input vector. By combining these two models, we can generate interpretability-enhanced point estimates that capture the uncertainty associated with the model’s predictions.
To put it simply, XAI is like a team of investigators working together to solve a complex mystery. The U model serves as the detective, using its trained FNN architecture to uncover clues and make accurate predictions. Meanwhile, the N model acts as the noise cleaner, adding uncertainty to the prediction process by introducing random fluctuations in the input vector. By combining these two models, we can generate a more comprehensive understanding of the mystery, allowing us to make better decisions with greater confidence.
In summary, XAI is a powerful tool that enables us to demystify complex machine learning models and gain insights into their decision-making processes. By leveraging interpretability and regularization techniques, we can build trust between humans and machines, ultimately leading to more reliable and effective AI systems.