aceConstructing Legal Knowledge Graphs and Tracing Bias in Medical Artificial Intelligence Based on Graph Neural Networks
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, an…
In benchmark tests, the method kept at least 0.88 of legal relations intact across graph densities and traced bias propagation paths with 0.93±0.03 precision.
The authors qualify the coupling of legal logic and algorithmic behavior as achieved only to a certain extent, indicating incomplete validation for trustworthy deployment.
Evidence
- Peer-reviewedJournal of Visualized Experiments2026-09-15
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Truvace Impact Record TRV-2026-1123, v2: “Constructing Legal Knowledge Graphs and Tracing Bias in Medical Artificial Intelligence Based on Graph Neural Networks.” Truvace, 2026-11-04. /record/TRV-2026-1123 (accessed at citation time). sha256 9b98b138b8d4dab4…
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Editorial pass: sharper headline; gain/problem restated as concrete outcomes; refiled trace to g_space (gain outcome, problem unsupported).
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