Abstract
Enterprise strategic decision support requires AI systems that are not only accurate but also uncertainty-aware, risk-calibrated, explainable, and governance-compliant. This paper proposes TRUST-ESD, a risk-calibrated and governance-aware framework for enterprise decision support under uncertainty.
TRUST-ESD evaluates feasible counterfactual strategies through:
- Predictive utility estimation
- Conformal uncertainty calibration
- CVaR-based downside-risk scoring
- Risk-memory retrieval
- Policy-as-code governance
- Explainability and human oversight
Unlike prediction-only methods that select actions by maximum expected utility, TRUST-ESD recommends strategies that balance value, reliability, risk exposure, and compliance.
Experimental results show that TRUST-ESD improves risk-adjusted utility by 7.95%, reduces risk exposure by 23.22%, lowers CVaR by 23.78%, decreases calibration error by 13.89%, enhances explanation fidelity by 10.90%, and increases governance compliance by 9.76% compared with strong uncertainty-aware baselines, while maintaining competitive predictive accuracy.
Ablation and case-study analyses further confirm that uncertainty calibration, downside-risk scoring, risk memory, explainability, and governance validation jointly improve trustworthy enterprise decision-making.
Blogger's Review: The TRUST-ESD framework introduces a novel approach to risk calibration and governance awareness in enterprise decision support, demonstrating outstanding performance in uncertainty management. Its multi-dimensional evaluation strategies provide comprehensive security for AI applications in enterprises, highlighting significant practical and research value.