Prediction Accuracy vs Explainability
Mandate explainability commensurate with decision consequence, routing predictions that fail the required evidence threshold to human review.
CyberTRIZ analysis · Benchmarking contradiction MDM032 · one of 8,235 worked contradictions published by CyberTRIZ.AI
Regulations
Business Context
Predictive benchmarking can estimate future performance, identify emerging gaps, anticipate failures, and evaluate likely outcomes before lagging measures become available. Complex predictive models can sometimes improve accuracy but make individual predictions difficult to explain. Explainable models may be easier to govern and trust but can lose predictive power where relationships are highly nonlinear or dependent on many interacting variables.
Benchmarking TRIZ Resolution
Organizations should separate prediction from explanation where necessary while maintaining governance between them. High-performance predictive models can identify risk or opportunity, while interpretable secondary models, feature-attribution methods, scenario analysis, or rule-based explanations clarify the factors influencing the result. For high-consequence decisions, explainability requirements should be stronger, and predictions that cannot satisfy the required evidence threshold should trigger human review rather than automatic action.
Applicable TRIZ Principles
Principle 1 – Segmentation separates predictive computation from explanatory and governance functions.
Principle 24 – Intermediary introduces explanatory mechanisms between complex models and decision-makers.
Principle 35 – Parameter Changes adjusts the balance between predictive complexity and explanation according to decision consequence.
Expected Outcome
Higher predictive performance
Greater model transparency
Better governance of predictive benchmarks
More appropriate use of automated predictions
Decision Indicators
Early indicators include:
Managers cannot explain why predictive benchmarks change.
High-accuracy models are rejected because stakeholders do not trust them.
Explainability requirements force teams to use materially weaker models in every situation.
Automated predictions influence consequential decisions without adequate explanation.
Model outputs cannot be challenged using understandable evidence.
These indicators reveal where prediction and explanation should be designed as complementary functions.