AI RESEARCH
Causal Explanations from the Geometric Properties of ReLU Neural Networks
arXiv CS.LG
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ArXi:2605.10396v1 Announce Type: new Neural networks have proved an effective means of learning control policies for autonomous systems, but these learned policies are difficult to understand due to the black-box nature of neural networks. This lack of interpretability makes safety assurance for such autonomous systems challenging. The fields of eXplainable Artificial Intelligence (XAI) and eXplainable Reinforcement Learning (XRL) aim to interpret the decision making processes of neural networks and autonomous agents, respectively.