AI Bias Reduction vs Model Accuracy
Use representative datasets, continuous bias testing, and periodic recalibration to meet EU AI Act data governance requirements without sacrificing model accuracy.
CyberTRIZ analysis · Regulatory contradiction R156 · one of 8,235 worked contradictions published by CyberTRIZ.AI
Regulations
Overview
Organizations developing AI systems must reduce discriminatory outcomes while maintaining high predictive performance. The AI Act requires high-risk AI systems to use appropriate data governance, testing, and risk management practices that minimize bias and unfair outcomes. RegulatoryTRIZ resolves this contradiction by combining representative datasets, continuous validation, bias testing, and periodic model recalibration throughout the AI lifecycle. Compliance is supported through dataset documentation, validation reports, and bias assessments, while effectiveness can be measured through model accuracy and bias-related findings.