摘 要：
身为当今高科技时代中的重要兴起领域，人工智能已经在众多领域内得到了广泛应用，其中包括审计领域。以往的审计工作由人类手动进行，费时费力且有可能出现人为失误。对于这个问题，人工智能审计系统的应用具有重要的价值。然而，审计工作中最根本的是审计证据的采集并基于此进行分析以形成审计结论，因此，需要一个证据链可信度验证的框架以确保在采集、分析审计证据的过程中不出现偏误，为审计结论的形成提供准确可信的基础。本研究提出一套对人工智能审计系统的证据链可信度进行验证的框架，旨在通过验证审计证据链的合法性、完整性和真实性，确保产生的审计结论的可信度。这个框架对于人工智能审计系统的业务流程、数据处理环节以及分析结果进行全方位的、细致的检测，发现可能影响结论的问题并及时处理、纠正，最终提供真实、可靠的审计结果。研究结果表明，应用此验证框架，人工智能审计系统在保证运行效率的基础上，实现审计质量的提升，为组织和企业的审计决策提供更强有力的支持。
关键词：人工智能审计系统；证据链；可信度验证；审计结论；审计质量
Abstract:
As an important emerging field in today's high-tech era, artificial intelligence has been widely applied across various domains, including auditing. Traditionally, audit work was performed manually by humans, which is time-consuming, labor-intensive, and prone to human error. The application of artificial intelligence audit systems holds significant value in addressing this issue. However, the fundamental aspect of auditing involves the collection of audit evidence and the analysis thereof to form audit conclusions. Thus, a framework for verifying the credibility of the evidence chain is needed to ensure that biases do not occur during the collection and analysis of audit evidence, providing an accurate and reliable foundation for forming audit conclusions. This study proposes a framework to verify the credibility of the evidence chain in artificial intelligence audit systems, aiming to ensure the credibility of the generated audit conclusions by validating the legality, completeness, and authenticity of the audit evidence chain. This framework conducts comprehensive and detailed inspections of the business processes, data processing phases, and analytical results of AI audit systems, identifying potential issues that may affect conclusions and addressing and correcting them promptly, ultimately delivering truthful and reliable audit results. Research findings indicate that applying this verification framework enhances audit quality while ensuring operational efficiency in artificial intelligence audit systems, providing stronger support for the auditing decisions of organizations and enterprises.
Keywords: Artificial intelligence audit systems; Evidence chain; Credibility verification; Audit conclusions; Audit quality
--
