Constructing Legal Knowledge Graphs and Tracing Bias in Medical Artificial Intelligence Based on Graph Neural Networks

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In medical artificial intelligence (AI) legal compliance analysis, this paper addresses the ambiguity in liability determination caused by the semantic fragmentation of multi-source heterogeneous legal texts and difficulties in tracing algorithmic biases. It constructs a knowledge graph construction and bias propagation path detection method based on graph neural networks. Four types of heterogeneous nodes-legal clauses, medical behaviors, algorithm modules, and responsible parties-are structured and encoded, and a directed graph is constructed based on four types of legal semantic relationships: "violation", "basis", "trigger", and "attribution". Next, a relation-aware graph convolutional network, combined with edge-type-specific weights and an attention mechanism, is used to achieve multi-layer feature alignment for cross-modal legal semantics. Then, gradient-based backpropagation is used to identify edges that significantly contribute to bias, construct a bias-propagation subgraph, and use community discovery to locate root-node clusters. Finally, real-time decision flows are mapped to temporary nodes, compliance is verified by embedding similarity, and the dynamics of key edge weights are monitored to warn of systemic bias accumulation. Experiments show that, in terms of semantic alignment accuracy, the proposed method achieves an average entity alignment accuracy of at least 0.92 and an average relation preservation completeness of at least 0.88 across different knowledge graph density gradients. Regarding bias localization capability, the root node recall rate reaches 0.95±±0.02, and the propagation path precision reaches 0.93±±0.03. This research, to a certain extent, achieves a deep coupling between legal logic and algorithmic behavior, providing an explainable and verifiable compliance support system for the trustworthy deployment of medical AI.