The Case for Causal Synthesis as a Foundation for Trusted High-Context Decision-Making

Current approaches to AI-assisted decision-making in high-context domains face persistent limitations in achieving reliable alignment between human intent and system behavior. This review systematically examines the recent literature on AI alignment methods across safety-critical domains to characterize the state of the art and identify recurring failure modes. A focused scoping review of 15 primary studies produced under a single DARPA request, published between 2024 and 2025, was conducted that covered fine-tuning approaches, attribute-based alignment, rule-based systems, probabilistic calibration, and trust dynamics. The findings reveal that even sophisticated approaches achieve only 50--76% accuracy in domain-specific tasks, with trust calibration studies showing approximately 70% overtrust rates in AI recommendations following obvious errors. Across all surveyed methods, a common limitation emerges: statistical pattern matching, regardless of technique, fails to capture the causal relationships underlying high-context decisions. The review identifies causal knowledge structuring and data-centric preprocessing as underexplored directions that may address root limitations in current alignment paradigms. Future research directions are proposed, including domain-specific languages for causal representation, deterministic traversal of knowledge structures, and human-in-the-loop causal editing frameworks.