How can expert systems improve the interpretability of deep learning models?
Deep learning models are powerful tools for data mining, but they often suffer from a lack of interpretability. This means that it is hard to understand how they make decisions, what features they use, and how they can be improved or validated. This can limit their trustworthiness, applicability, and ethicality in various domains. Expert systems are a type of artificial intelligence that use knowledge bases and inference rules to mimic human experts. They can help to improve the interpretability of deep learning models by providing explanations, insights, and recommendations based on domain knowledge and logic. In this article, you will learn how expert systems can enhance the interpretability of deep learning models in four ways: by generating natural language explanations, by visualizing the model structure and behavior, by detecting and correcting biases and errors, and by facilitating human-machine collaboration.