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When making strategic decisions, we are often confronted with overwhelminginformation to process. The situation can be further complicated when somepieces of evidence are contradicted each other or paradoxical. The challengethen becomes how to determine which information is useful and which ones shouldbe eliminated. This process is known as meta-decision. Likewise, when it comesto using Artificial Intelligence (AI) systems for strategic decision-making,placing trust in the AI itself becomes a meta-decision, given that many AIsystems 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 noveltaxonomy or framework of TAI, which encompasses three crucial domains:articulate, authentic, and basic for different levels of trust. To underpinthese 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 acomprehensive survey and explore different TAI approaches from a strategicdecision-making perspective.

Survey of Trustworthy AI: A Meta Decision of AI