| 摘要: |
| 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, 提出人工智能系统的9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. |
| 关键词: 人工智能系统 可信性 度量评估 |
| DOI：10.13328/j.cnki.jos.006592 |
| 分类号: |
| 基金项目:华东师范大学-华为可信创新实验室项目(15902-412312-19214/013);上海市可信工业互联网软件协同创新中心项目 |
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| Survey on Trustworthiness Measurement for Artificial Intelligence Systems |
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LIU Han1,2, LI Kai-Xuan1,2, CHEN Yi-Xiang1,2
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1.Software Engineering Institute, East China Normal University, Shanghai 200062, China;2.Shanghai Key Laboratory of Trustworthy Computing (East China Normal University), Shanghai 200062, China
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| Abstract: |
| In recent years, artificial intelligence (AI) has rapidly developed. AI systems have penetrated people’s lives and become an indispensable part. However, these systems require a large amount of data to train models, and data disturbances will affect their results. Furthermore, as the business becomes diversified, and the scale gets complex, the trustworthiness of AI systems has attracted wide attention. Firstly, based on the trustworthiness attributes proposed by different organizations and scholars, this study introduces nine trustworthiness attributes of AI systems. Next, in terms of the data, model, and result trustworthiness, the study discusses methods for measuring the data, model, and result trustworthiness of existing AI systems and designs an evidence collection method of AI trustworthiness. Then, it summarizes the trustworthiness measurement theory and methods of AI systems. In addition, combined with attribute-based software trustworthiness measurement methods and blockchain technologies, the study establishes a trustworthiness measurement framework for AI systems, which includes methods of trustworthiness attribute decomposition and evidence acquisition, the federation trustworthiness measurement model, and the blockchain-based trustworthiness measurement structure of AI systems. Finally, it describes the opportunities and challenges of trustworthiness measurement technologies for AI systems. |
| Key words: artificial intelligence system trustworthiness measurement |
