审计推荐系统--基于风险情景的DSA实施
Auditing Recommender Systems -- Putting the DSA into practice with a risk-scenario-based approach
摘要：
今天的在线平台严重依赖推荐系统向用户提供内容;社交媒体就是一个很好的例子。反过来，推荐系统在很大程度上依赖于人工智能算法来决定谁可以看到什么。虽然社交媒体平台提供的内容与使用它们的用户一样多种多样，但事实表明，平台可能会对个人、团体和社会造成严重伤害。研究表明，这些负面影响包括从个人心理健康恶化到推动全社会两极分化，使民主国家处于危险之中。为了更好地保护人们免受这些伤害，欧盟的数字服务法案（DSA）要求平台，特别是那些拥有大量用户的平台，使其算法系统更加透明，并遵守尽职调查义务。这些要求是减轻在线平台构成的系统性风险的重要立法步骤。然而，DSA缺乏具体的指导方针来实施可行的审计程序，使审计员能够对这些平台负责。这种空白可能会助长“洗黑钱”的蔓延，也就是说，平台利用审计来合法化他们的做法并忽视责任。为了填补这一空白，我们提出了一个基于风险情景的审计过程。我们详细解释了根据DSA推荐系统的审计和评估应该是什么样子。我们的方法还考虑了平台的不断发展的性质，并强调其推荐系统组件的可观察性。由此产生的审计促进了内部（同一系统在不同时间的审计之间）和外部（不同平台的审计之间）的可比性，同时还提供了对平台本身实施的缓解措施的评估。
英文摘要：
Today's online platforms rely heavily on recommendation systems to serve content to their users; social media is a prime example. In turn, recommendation systems largely depend on artificial intelligence algorithms to decide who gets to see what. While the content social media platforms deliver is as varied as the users who engage with them, it has been shown that platforms can contribute to serious harm to individuals, groups and societies. Studies have suggested that these negative impacts range from worsening an individual's mental health to driving society-wide polarisation capable of putting democracies at risk. To better safeguard people from these harms, the European Union's Digital Services Act (DSA) requires platforms, especially those with large numbers of users, to make their algorithmic systems more transparent and follow due diligence obligations. These requirements constitute an important legislative step towards mitigating the systemic risks posed by online platforms. However, the DSA lacks concrete guidelines to operationalise a viable audit process that would allow auditors to hold these platforms accountable. This void could foster the spread of 'audit-washing', that is, platforms exploiting audits to legitimise their practices and neglect responsibility. To fill this gap, we propose a risk-scenario-based audit process. We explain in detail what audits and assessments of recommender systems according to the DSA should look like. Our approach also considers the evolving nature of platforms and emphasises the observability of their recommender systems' components. The resulting audit facilitates internal (among audits of the same system at different moments in time) and external comparability (among audits of different platforms) while also affording the evaluation of mitigation measures implemented by the platforms themselves.
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