AAIA: Advanced in AI Audit

Consistency and reliability of AI outcomes depend on appropriate training data and sound model evaluation and validation. Examining the training data and evaluation parameters directly identifies whether data quality, bias, representativeness, or validation settings are causing similar inputs to receive inconsistent results. NIST’s AI Risk Management Framework identifies valid and reliable AI systems and evaluation of data as core trustworthiness concerns.