TOS002
Match AI model complexity to explainability requirements so significant audit conclusions always rest on interpretable, human-validated evidence.
CyberTRIZ analysis · Audit contradiction TOS002 · one of 8,235 worked contradictions published by CyberTRIZ.AI
Regulations
AI Efficiency vs Explainability
Business ContextAI can accelerate document review, anomaly detection, risk assessment, classification, and other audit activities. More sophisticated models may produce valuable results while making it difficult for auditors, reviewers, management, or regulators to understand how outputs were generated.
Audit TRIZ ResolutionUse AI according to the explainability required by the audit decision. Complex models can support screening and pattern identification, while significant conclusions require interpretable outputs, traceable evidence, validation, and human evaluation sufficient to defend the resulting judgment.
Applicable TRIZ Principles
Principle 1 – Segmentation separates AI processing from final audit judgment.
Principle 24 – Intermediary introduces interpretable information between AI models and decision-makers.
Principle 26 – Copying creates understandable representations of complex analytical relationships.
Expected Outcome
Greater AI productivity
Improved explainability
More defensible conclusions
Stronger human oversight
Decision Indicators
Auditors cannot explain why AI identifies particular transactions or documents.
Model outputs are treated as evidence without validation.
Reviewers accept conclusions primarily because of model sophistication.
Teams avoid useful AI because results are difficult to defend.
Significant audit judgments depend on opaque analytical processes.