CST019
Classify AI applications by decision consequence and apply EU AI Act transparency and human-oversight obligations proportionally to risk level.
CyberTRIZ analysis · BrownFieldIndustrialProjects contradiction C14-CST019 · one of 8,235 worked contradictions published by CyberTRIZ.AI
Regulations
AI-Assisted Decisions vs Explainability
Business ContextAI-assisted tools can accelerate engineering analysis, planning, forecasting, inspection, and risk identification. Decisions affecting safety, cost, schedule, or operations, however, require sufficient understanding of how recommendations were produced.
Brown Field Industrial Projects TRIZ ResolutionUse AI according to decision consequence. Automated recommendations can support routine or reversible decisions, while higher-consequence applications require traceable inputs, validation, human review, and comparison against engineering constraints.
Applicable TRIZ Principles
Principle 3 – Local Quality: varies oversight according to decision consequence.
Principle 23 – Feedback: validates AI recommendations against actual outcomes.
Principle 25 – Self-Service: automates suitable analytical tasks while retaining human authority where necessary.
Expected Outcome
Faster analysis
Greater decision transparency
Reduced inappropriate automation
Improved confidence in AI-assisted decisions
Decision IndicatorsEarly indicators that this contradiction is limiting project performance include:
Teams cannot explain consequential AI recommendations.
AI outputs are accepted without technical validation.
Engineers reject useful tools because reasoning is opaque.
Model inputs and assumptions are poorly documented.
Human review is identical regardless of decision consequence.
Monitoring these indicators helps organizations use AI efficiently while maintaining appropriate technical accountability.