When engaging in strategic decision-making, we are frequently confronted with
overwhelming information and data. The situation can be further complicated
when certain pieces of evidence contradict each other or become paradoxical.
The primary challenge is how to determine which information can be trusted when
we adopt Artificial Intelligence (AI) systems for decision-making. This issue
is known as deciding what to decide or Trustworthy AI. However, the AI system
itself is often considered an opaque black box. We propose a new approach to
address this issue by introducing a novel framework of Trustworthy AI (TAI)
encompassing three crucial components of AI: representation space, loss
function, and optimizer. Each component is loosely coupled with four TAI
properties. Altogether, the framework consists of twelve TAI properties. We aim
to use this framework to conduct the TAI experiments by quantitive and
qualitative research methods to satisfy TAI properties for the decision-making
context. The framework allows us to formulate an optimal prediction model
trained by the given dataset for applying the strategic investment decision of
credit default swaps (CDS) in the technology sector. Finally, we provide our
view of the future direction of TAI research

利用创新的可信人工智能（TAI）框架，通过结合 AI 的三个关键组成部分：表示空间、损失函数和优化器，以及十二个 TAI 属性，来解决决策制定中信息可信性的难题。该框架被用于实验，在科技行业的战略性投资决策中，应用给定数据集训练出的最优预测模型。研究者还展望了 TAI 研究的未来方向。