提出可审计性评估框架，助力AI教育系统合规可信
Assessing the Auditability of AI-integrating Systems: A Framework and Learning Analytics Case Study
- 构建三要素框架：可验证声明、多类型证据、技术可访问性
- Moodle预测系统因文档不全、监控弱、测试数据缺失而审计难
- 适用于教育类AI系统设计优化，尤其需合规审计的团队
审计有助于提升集成人工智能的学习分析（LA）系统的可信度，未来可能成为法律要求。我们主张审计有效性取决于被审计系统的可审计性，因此系统设计需考虑可审计性。本文提出一个评估AI集成系统可审计性的框架，包含三部分：(1) 关于系统有效性、实用性与伦理性的可验证声明；(2) 支持或反驳声明的证据，涵盖文档、原始数据源和日志等类型；(3) 审计者可通过技术手段（如API、监控工具、可解释AI）获取证据。将该框架应用于Moodle的辍学预测系统及一个原型AI-LA系统，发现Moodle受限于文档不完整、监控能力不足以及缺乏可用测试数据，可审计性较差。该框架能有效评估现用AI-LA系统的可审计性，促进可审计系统的设计与审计质量提升。
原文摘要 · Abstract (English)
Audits contribute to the trustworthiness of Learning Analytics (LA) systems that integrate Artificial Intelligence (AI) and may be legally required in the future. We argue that the efficacy of an audit depends on the auditability of the audited system. Therefore, systems need to be designed with auditability in mind. We present a framework for assessing the auditability of AI-integrating systems that consists of three parts: (1) Verifiable claims about the validity, utility and ethics of the system, (2) Evidence on subjects (data, models or the system) in different types (documentation, raw sources and logs) to back or refute claims, (3) Evidence must be accessible to auditors via technical means (APIs, monitoring tools, explainable AI, etc.). We apply the framework to assess the auditability of Moodle's dropout prediction system and a prototype AI-based LA. We find that Moodle's auditability is limited by incomplete documentation, insufficient monitoring capabilities and a lack of available test data. The framework supports assessing the auditability of AI-based LA systems in use and improves the design of auditable systems and thus of audits.
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