Having your Privacy Cake and Eating it Too: Platform-supported Auditing of Social Media Algorithms for Public Interest
Having your Privacy Cake and Eating it Too: Platform-supported Auditing of Social Media Algorithms for Public Interest
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拥有隐私蛋糕并把它吃掉：平台支持的社交媒体算法审计以维护公共利益
DOI：
10.1145/3579610
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发表时间：
2023
影响因子：
--
通讯作者：
Heidemann, John
中科院分区：
文献类型：
--
作者：
Imana, Basileal;Korolova, Aleksandra;Heidemann, John
社交媒体平台管理着获取信息和机会的途径，因此在塑造当今的公共话语方面发挥着关键作用。这些平台用来管理内容的算法的不透明性引发了社会问题。之前的研究使用由专家领导的黑箱方法或由日常用户驱动的协作审计来表明这些算法可能导致有偏见或歧视性的结果。然而，现有的审计方法面临着根本的限制，因为它们的功能独立于平台。由于担心潜在的有害后果，美国和欧盟都提出了一项立法提案，要求采用一种新的审计形式，即经过审查的外部研究人员有权访问社交媒体平台。不幸的是，到目前为止，还没有具体的技术建议来提供这种审计，因为大规模审计可能会泄露用户的私人数据和平台的专有算法。我们提出了一种新的平台支持审计方法，可以满足拟议立法的目标。我们工作的第一个贡献是列举了现有审计方法在大规模实施这些策略时所面临的挑战和局限性。其次，我们建议对相关性评估器的有限特权访问是外部研究人员对社交媒体平台进行可推广的平台支持审计的关键。第三，我们提出了一个可以防范这些风险的审计框架，表明平台支持的审计不需要冒用户隐私风险，也不需要泄露平台的商业利益。对于一个特定的公平指标，我们表明，确保隐私只会在准确审计所需的样本数量上增加一个小的常数因子（上限为6.34倍，典型参数为4倍）。我们的技术贡献，加上持续的法律和政策努力，可以通过跨越隐私与透明度的障碍，使公众能够监督社交媒体平台如何影响个人和社会。
Social media platforms curate access to information and opportunities, and so play a critical role in shaping public discourse today. The opaque nature of the algorithms these platforms use to curate content raises societal questions. Prior studies have used black-box methods led by experts or collaborative audits driven by everyday users to show that these algorithms can lead to biased or discriminatory outcomes. However, existing auditing methods face fundamental limitations because they function independent of the platforms. Concerns of potential harmful outcomes have prompted proposal of legislation in both the U.S. and the E.U. to mandate a new form of auditing where vetted external researchers get privileged access to social media platforms. Unfortunately, to date there have been no concrete technical proposals to provide such auditing, because auditing at scale risks disclosure of users' private data and platforms' proprietary algorithms. We propose a new method for platform-supported auditing that can meet the goals of the proposed legislation. The first contribution of our work is to enumerate the challenges and the limitations of existing auditing methods to implement these policies at scale. Second, we suggest that limited, privileged access to relevance estimators is the key to enabling generalizable platform-supported auditing of social media platforms by external researchers. Third, we show platform-supported auditing need not risk user privacy nor disclosure of platforms' business interests by proposing an auditing framework that protects against these risks. For a particular fairness metric, we show that ensuring privacy imposes only a small constant factor increase (6.34x as an upper bound, and 4× for typical parameters) in the number of samples required for accurate auditing. Our technical contributions, combined with ongoing legal and policy efforts, can enable public oversight into how social media platforms affect individuals and society by moving past the privacy-vs-transparency hurdle.
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