TRUST-ESD: A Risk-Calibrated and Governance-Aware AI Framework for Enterprise Strategic Decision Support Under Uncertainty· TRUST-ESD: 企业战略决策支持下的风险校准治理感知AI框架
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 b
TRUST-ESD：一种风险校准且治理感知的企业决策支持AI框架。
- 核心方法
- TRUST-ESD通过预测效用估计、符合性不确定性校准、CVaR风险评分、基于历史的风险记忆检索、代码化政策治理、可解释性及人工监督，评估可行的反事实策略。
- 适合谁读
- 研究者 / 工程师 / 产品经理
- 要解决的问题
- 企业战略决策支持需要AI系统在不确定情况下不仅准确，还需具备风险校准、可解释性及合规性。
- 关键实验
- 实验结果显示TRUST-ESD提高了风险调整效用。
- 主要贡献
- 推荐的策略能够平衡价值、可靠性、风险暴露和合规性，而不仅仅是最大预测效用。
- 意义与局限
- 意义在于提高企业决策支持系统的鲁棒性和可靠性，影响企业风险管理与合规决策；局限可能在于复杂度较高及实施成本。