Complexity-induced knowledge gap

Mitigation strategy

1. Implement **Explainable Artificial Intelligence (XAI) techniques**, such as SHAP and LIME, to provide post-hoc interpretability for complex models. This clarifies the contribution of input features to specific predictions and translates opaque decision-making processes into human-understandable explanations, which is crucial for demonstrating harm and establishing accountability. 2. Integrate **Causal AI methodologies** to shift from identifying correlation to establishing validated cause-and-effect relationships between AI actions and their outcomes. Employing counterfactual reasoning and causal modeling techniques provides a deeper understanding of the "why" behind decisions, which is essential for proving a clear causal link for purposes of liability and accurate risk assessment. 3. Establish a **comprehensive AI risk management system** across the entire system lifecycle, mandating continuous record-keeping of events, rigorous adversarial testing (red teaming), and model evaluations. This governance structure ensures that the system's behavior, even in its complexity, is continuously monitored, documented, and subject to scrutiny, thereby providing the necessary evidence base for human oversight and regulatory compliance.