How do we make AI‑driven decisions explainable?
Use interpretable policies (rules/constraints), post‑hoc attribution (e.g., feature importance for a scaling action), and policy introspection for MARL (e.g., counterfactuals explaining why a policy chose configuration A vs. B). Explanations are logged and surfaced via dashboards for operators and researchers.