Rilevamento e Mitigazione Bias Modelli

Systematic processes and tools for identifying, measuring, and reducing unfair bias in AI models to ensure equitable treatment across demographic groups and protected characteristics. We implement comprehensive bias testing across development lifecycle evaluating training data, model predictions, and real-world outcomes. Our approach includes statistical fairness metrics, intersectional analysis, and disparate impact assessment. Mitigation strategies include data augmentation, resampling, algorithmic debiasing, and threshold optimization. We establish ongoing monitoring for bias emergence in production. Documentation includes bias audit reports and mitigation decisions. This reduces discrimination risks, ensures regulatory compliance, protects brand reputation, and promotes equitable AI systems that serve all users fairly regardless of background or characteristics.