Interpretable Knowledge

Interpretable knowledge research aims to make the decision-making processes of complex machine learning models, such as large language models and deep reinforcement learning agents, transparent and understandable. Current efforts focus on developing methods that integrate symbolic reasoning with neural networks, leveraging techniques like program synthesis, probabilistic logic programming, and knowledge graph augmentation to create explainable models. This work is crucial for building trust in AI systems, enabling human oversight in high-stakes applications, and facilitating the development of more robust and reliable AI technologies across diverse fields.