When making strategic decisions, we are often confronted with overwhelming information to process. The situation can be further complicated when some pieces of evidence are contradicted each other or paradoxical. The challenge then becomes how to determine which information is useful and which ones should be eliminated. This process is known as meta-decision. Likewise, when it comes to using Artificial Intelligence (AI) systems for strategic decision-making, placing trust in the AI itself becomes a meta-decision, given that many AI systems are viewed as opaque "black boxes" that process large amounts of data. Trusting an opaque system involves deciding on the level of Trustworthy AI (TAI). We propose a new approach to address this issue by introducing a novel taxonomy or framework of TAI, which encompasses three crucial domains: articulate, authentic, and basic for different levels of trust. To underpin these domains, we create ten dimensions to measure trust: explainability/transparency, fairness/diversity, generalizability, privacy, data governance, safety/robustness, accountability, reproducibility, reliability, and sustainability. We aim to use this taxonomy to conduct a comprehensive survey and explore different TAI approaches from a strategic decision-making perspective.

翻译：在进行战略决策时，我们常常需要处理海量信息。当某些证据相互矛盾或存在悖论时，情况会进一步复杂化。此时面临的挑战是如何确定哪些信息有用、哪些应被剔除——这一过程被称为元决策。同样，当使用人工智能系统进行战略决策时，由于许多AI系统被视为处理大量数据的不透明"黑箱"，对AI本身的信任便成为一种元决策。信任不透明系统涉及对可信人工智能（TAI）水平的判定。我们提出一种解决该问题的新方法，通过引入TAI的新型分类框架，该框架涵盖三个关键领域：可解释域、真实域与基础域，分别对应不同信任层级。为支撑这些领域，我们构建了十个信任度量维度：可解释性/透明度、公平性/多样性、泛化能力、隐私性、数据治理、安全性/鲁棒性、问责性、可复现性、可靠性与可持续性。我们旨在运用该分类框架，从战略决策视角对现有TAI方法进行全面综述与探索。