AAIA: Advanced in AI Audit

Retrieval-augmented generation (RAG) retrieves relevant, trusted medical research at response time and provides it as grounding context for the model, reducing inaccurate or fabricated results. Lower temperature makes outputs less variable but does not validate their factual accuracy.

Synthetic data must be validated for quality and fitness for purpose because it can fail to represent the real population accurately or can introduce distortions and bias. Validation helps ensure the training data supports reliable credit-risk model outcomes while preserving the intended privacy benefits.

Feature scaling places numeric values on comparable ranges, preventing a feature with a comparatively large magnitude or variance from exerting disproportionate influence on correlations and model training.

Natural language processing (NLP) enables systems to understand, extract, classify, and analyze information in unstructured contract text, making it well suited to automated contract review and analysis.

Cross-referencing multiple models is a risk-reducing control intended to improve AI-output accuracy and reduce the risk associated with inaccurate results. Implementing controls to reduce risk is risk mitigation.