Higher Decision Transparency vs Greater Algorithm Complexity
Build a mandatory explainability layer that produces human-readable decision rationale to meet EU AI Act transparency and auditability obligations.
CyberTRIZ analysis · AIRobotics contradiction AS009 · one of 8,235 worked contradictions published by CyberTRIZ.AI
Regulations
Business Context
Advanced autonomous systems often rely on sophisticated AI algorithms that are difficult for operators, regulators, and stakeholders to understand.
AI & Robotics TRIZ Resolution
Provide explainable decision layers that summarize reasoning, confidence, and influencing factors without exposing unnecessary algorithmic complexity.
Applicable TRIZ Principles
Principle 2 – Taking Out removes unnecessary algorithmic complexity from user-facing explanations.
Principle 24 – Intermediary introduces explainability layers between AI models and end users.
Principle 32 – Color Changes uses visual indicators to communicate confidence and decision status more clearly.
Expected Outcome
Greater decision transparency
Improved stakeholder trust
Better regulatory readiness
High analytical capability
Decision Indicators
Early indicators that decision complexity reduces transparency include:
Operators question autonomous actions.
Decision explanations become difficult.
Audit requests increase.
User confidence declines.
Monitoring these indicators improves explainable autonomy.