Assured, Explainable, And Auditable AI For High-Stakes Decisions: A Survey Of Trustworthy Machine Learning In Mission-Critical Systems

Abstract

Deployment of artificial intelligence in mission-critical domains healthcare, criminal justice, finance, and public administration, demands systems that withstand legal, ethical, and reliability scrutiny. This survey synthesizes techniques that transform black-box models into accountable decision aids. Post-hoc explanation methods, including feature attribution and counterfactual reasoning, are contrasted with intrinsically interpretable architectures and causal frameworks that support real-world interventions. Uncertainty quantification through conformal prediction and calibrated probabilistic outputs bounds error in safety-critical workflows, while fairness auditing across protected groups employs metrics and bias mitigation strategies to navigate accuracy-equity trade-offs. Operational assurance mechanisms, dataset shift detection, continuous monitoring, model versioning, rollback protocols, and red-team evaluation —are mapped to emerging risk-management and documentation frameworks such as model cards and system cards. Open challenges include scaling explainability to foundation models, multi-objective optimization balancing competing desiderata, and aligning machine-generated rationale with human cognitive processes in consequential decisions. The synthesis establishes a comprehensive agenda for building AI systems that support verifiable, responsible choices where failure carries unacceptable consequences.