TRUST-ESD让企业决策在不确定中更安全可靠，兼顾风险、合规与可解释性。
TRUST-ESD: A Risk-Calibrated and Governance-Aware AI Framework for Enterprise Strategic Decision Support Under Uncertainty
- 融合预测效用、风险评分与治理验证，动态评估策略可行性。
- 相比基线，风险调整后收益提升7.95%，风险暴露降低23.22%。
- 适合金融、医疗等需高可信决策的行业，尤其关注合规与风险控制。
企业战略决策支持需要既准确又具备不确定性感知、风险校准、可解释性和治理合规性的AI系统。本文提出TRUST-ESD框架，通过预测效用估计、置信区间校准、基于CVaR的下行风险评分、风险记忆检索、策略即代码治理、可解释性与人工监督，评估可行的反事实策略。不同于仅依赖最大期望效用的方法，TRUST-ESD推荐在价值、可靠性、风险暴露和合规性之间取得平衡的策略。实验结果表明，相较于强基线，该框架在风险调整效用上提升7.95%，风险暴露降低23.22%，CVaR下降23.78%，校准误差减少13.89%，解释保真度提高10.90%，治理合规性提升9.76%，同时保持竞争性预测准确性。消融与案例研究进一步证实，不确定性校准、下行风险评分、风险记忆、可解释性与治理验证共同促进可信的企业决策。
原文摘要 · Abstract (English)
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%, reduces CVaR by 23.78%, lowers calibration error by 13.89%, improves 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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。