用行为模拟法增强平台算法审计的深度与独立性
Beyond the Checkbox: Strengthening DSA Compliance Through Social Media Algorithmic Auditing
- 通过模拟用户行为测试算法响应，实现对AI系统的实证评估
- 发现现有审计方法存在技术深度不足和标准不一的问题
- 适合监管机构、合规团队及算法安全研究者参考
根据《数字服务法案》（DSA），在线平台算法需满足算法透明度、用户保护与隐私等合规要求。为验证合规性，法案要求平台接受独立审计。然而，当前审计实践及其有效性尚不明确。本文从监管与技术视角出发，分析了三类关键算法条款的审计报告：对未成年人的画像限制、推荐系统透明度、以及使用敏感数据的定向广告限制。结果表明，审计方法存在显著不一致且缺乏技术深度。为此，我们提出采用算法审计——即通过模拟用户行为、观察算法响应并分析其表现，以提升合规评估的深度、规模与独立性。
原文摘要 · Abstract (English)
Algorithms of online platforms are required under the Digital Services Act (DSA) to comply with specific obligations concerning algorithmic transparency, user protection and privacy. To verify compliance with these requirements, DSA mandates platforms to undergo independent audits. Little is known about current auditing practices and their effectiveness in ensuring such compliance. To this end, we bridge regulatory and technical perspectives by critically examining selected audit reports across three critical algorithmic-related provisions: restrictions on profiling minors, transparency in recommender systems, and limitations on targeted advertising using sensitive data. Our analysis shows significant inconsistencies in methodologies and lack of technical depth when evaluating AI-powered systems. To enhance the depth, scale, and independence of compliance assessments, we propose to employ algorithmic auditing -- a process of behavioural assessment of AI algorithms by means of simulating user behaviour, observing algorithm responses and analysing them for audited phenomena.
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