Artificial intelligence (AI) technologies have become the key driver of innovation in society. However, numerous vulnerabilities of AI systems can lead to negative consequences for society, such as biases encoded in the training data and algorithms and lack of transparency. This calls for AI systems to be audited to ensure that the impact on society is understood and mitigated. To enable AI audits, auditability measures need to be implemented. This study provides a systematic review of academic work and regulatory work on AI audits and AI auditability. Results reveal the current understanding of the AI audit scope, audit challenges, and auditability measures. We identify and categorize AI auditability measures for each audit area and specific process to be audited and the party responsible for each process to be audited. Our findings will guide existing efforts to make AI systems auditable across the lifecycle of AI systems.

人工智能（AI）技术已成为社会创新的关键驱动力。然而，人工智能系统的众多漏洞可能会给社会带来负面影响，例如训练数据和算法中编码的偏见以及缺乏透明度。这就需要对人工智能系统进行审计，以确保人们理解并减轻对社会的影响。为了实现人工智能审计，需要实施审计措施。本研究对人工智能审计和人工智能可审计性的学术工作和监管工作进行了系统回顾。结果揭示了当前对人工智能审计范围、审计挑战和可审计性措施的理解。我们针对每个审计领域、待审计的具体流程以及每个待审计流程的责任方，对人工智能可审计性措施进行识别和分类。我们的研究结果将指导现有的努力，使人工智能系统在整个人工智能系统的生命周期中都可审计。