数据驱动的人工智能（AI）算法中的不透明性问题已成为制约其广泛应用的障碍，尤其是在涉及健康、安全和巨大经济价值的敏感领域，如化学工程（CE）。为了促进人工智能在CE中的可靠应用，本文讨论了人工智能应用中的透明度概念，该概念基于可解释的人工智能（XAI）概念和CE领域的关键特征进行定义。本文还从因果关系（即人工智能的预测和输入之间的相关性）、可解释性（即工作流程的操作原理）和信息性（即研究系统的理论见解）等方面强调了可靠人工智能的要求。文中对相关技术和最先进的应用程序进行了评估，以突出在CE中建立可靠的人工智能应用程序的重要性。此外，还提供了一个全面的透明度分析案例研究作为示例，以增进理解。总的来说，本工作主要针对化学工程师，对这一主题进行了深入的讨论，以提高人们对负责任地应用人工智能的认识。有了这个重要的缺失环节，人工智能有望成为一个新颖而强大的工具，可以极大地帮助化学工程师解决CE中的瓶颈挑战。

The issue of opacity within data-driven artificial intelligence (AI) algorithms has become an impediment to these algorithms’ extensive utilization, especially within sensitive domains concerning health, safety, and high profitability, such as chemical engineering (CE). In order to promote reliable AI utilization in CE, this review discusses the concept of transparency within AI utilizations, which is defined based on both explainable AI (XAI) concepts and key features from within the CE field. This review also highlights the requirements of reliable AI from the aspects of causality (i.e., the correlations between the predictions and inputs of an AI), explainability (i.e., the operational rationales of the workflows), and informativeness (i.e., the mechanistic insights of the investigating systems). Related techniques are evaluated together with state-of-the-art applications to highlight the significance of establishing reliable AI applications in CE. Furthermore, a comprehensive transparency analysis case study is provided as an example to enhance understanding. Overall, this work provides a thorough discussion of this subject matter in a way that-for the first time-is particularly geared toward chemical engineers in order to raise awareness of responsible AI utilization. With this vital missing link, AI is anticipated to serve as a novel and powerful tool that can tremendously aid chemical engineers in solving bottleneck challenges in CE.