Hybrid Model
AI systems combining multiple learning paradigms (e.g., symbolic and neural) to balance explainability and performance.
Definition
Architectures that integrate rule-based (symbolic) modules for transparent logic with neural networks for pattern recognition. Hybrids aim to leverage the best of both worlds: maintaining human-readable reasoning paths for critical decisions while achieving high accuracy on unstructured data. Governance includes orchestrating module interactions, validating overall system coherence, and ensuring each component meets its respective compliance requirements.
Real-World Example
A compliance-monitoring tool uses a symbolic engine to encode regulatory rules (e.g., GDPR consent clauses) and a neural-NLP model to extract clause contexts from contracts. The hybrid system flags non-compliant contracts with rule-based logic, while the neural model handles the unstructured text—achieving both transparency and coverage of diverse legal language.
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