Lack of transparency and interpretability
Today's Frontier AI is difficult to interpret and lacks transparency. Contextual understanding of the training data is not explicitly embedded within these models. They can fail to capture perspectives of underrepresented groups or the limitations within which they are expected to perform without fine tuning or reinforcement learning with human feedback (RLHF).
ENTITY
2 - AI
INTENT
2 - Unintentional
TIMING
1 - Pre-deployment
Risk ID
mit913
Domain lineage
7. AI System Safety, Failures, & Limitations
7.4 > Lack of transparency or interpretability
Mitigation strategy
1. Adopt an Interpretability by Design paradigm by selecting or engineering model architectures (e.g., inherently interpretable models or specialized neural networks with attention mechanisms) that prioritize algorithmic transparency from the conception and design phases of the AI lifecycle. 2. Deploy Post-Hoc Explainable AI (XAI) Methodologies (e.g., SHAP, LIME) in conjunction with continuous auditing and monitoring frameworks to generate human-readable explanations of complex model outputs and to verify adherence to established fairness and equity metrics across sensitive groups. 3. Establish a rigorous Transparency Documentation Protocol that requires clear, standardized reporting of the model's design, training dataset characteristics (including any known imbalances or limitations), and defined operational constraints for all pre-deployment and deployment phases.