近年来，人工智能技术突飞猛进，人工智能系统已经渗透到人们生活中，成为人们生活中不可或缺的一部分.然而，人工智能系统需要数据训练模型，数据扰动会对其结果造成影响.并且随着人工智能系统业务多样化，规模复杂化，人工智能系统的可信性愈发受到人们的关注.首先，在梳理不同组织和学者提出的人工智能系统可信属性基础上，提出了人工智能系统的九个可信属性; 接着, 从数据可信性、模型可信性和结果可信性分别介绍现有的人工智能系统数据、模型、结果可信性度量方法，设计了人工智能系统可信证据收集方法.其次, 总结当前人工智能系统的可信度量评估理论与方法.然后, 结合基于属性的软件可信评估方法与区块链技术, 建立了一个人工智能系统可信度量评估框架，包括可信属性分解及可信证据获取方法、联邦式可信度量模型与以及基于区块链的人工智能系统可信度量评估架构。最后，讨论人工智能系统可信度量技术面临的机遇和挑战.
In recent years, artificial intelligence has been rapidly advancing. Artificial intelligence system has penetrated our life and has become an indispensable part of our life. However, artificial intelligence systems require a large amount of data to train models, and data disturbances will affect their results. What's more, with the business form changing, the scale becoming more complex, the trustworthiness of the artificial intelligence systems has been getting more and more attention. Firstly, based on summarizing the trustworthiness attributes proposed by various organizations and scholars, we introduce the nine trustworthiness attributes of artificial intelligence. Next, we present the existing AI systems measurement method for the data, model, and result trustworthiness, and propose a artificial intelligence trustworthy evidence collection method. Then, we discuss the trustworthiness measurement model of AI systems. Combined with existing attributes-based software trustworthiness measurement methods and blockchain technology, we propose an artificial intelligence system trustworthiness measurement framework, including the decomposition of trustworthiness attributes and evidence acquisition method, the federation trustworthiness measurement model, and the blockchain-based artificial intelligence trustworthiness measurement structure. Finally, we analyzed the opportunities and challenges of trustworthiness measurement technology for artificial intelligence systems.