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：在制定战略决策时，我们常常面临海量信息需要处理。当部分证据相互矛盾或存在悖论时，情况可能进一步复杂化。此时的挑战在于如何判断哪些信息有用、哪些应予以剔除——这个过程被称为元决策。同样，当使用人工智能系统进行战略决策时，鉴于许多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。信任不透明系统需要对可信人工智能的层级进行判断。我们提出了一种新方法来解决这一问题，通过引入一个新颖的TAI分类框架，该框架涵盖三个关键领域：清晰表达、真实可靠与基础层面，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/透明性、公平性/多样性、泛化能力、隐私性、数据治理、安全性/鲁棒性、可问责性、可复现性、可靠性与可持续性。我们旨在运用该分类法开展全面综述，从战略决策视角探索不同的TAI方法。