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 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 harm 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. Our first contribution is to enumerate the challenges 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 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 4x 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.

翻译：社交媒体平台对信息和机会的访问进行筛选，因此在塑造当今公共话语中扮演着关键角色。这些平台用于筛选内容的算法不透明，引发了社会问题。先前的研究采用黑箱方法表明，这些算法可能导致偏见或歧视性结果。然而，现有的审计方法存在根本性局限，因为它们独立于平台运行。对潜在危害的担忧促使美国和欧盟提出立法，要求采用一种新的审计形式，即经过审查的外部研究人员获得对社交媒体平台的特权访问权限。不幸的是，迄今为止尚未有具体的技术方案提供此类审计，因为大规模审计可能导致用户私人数据和平台专有算法的泄露。我们提出了一种新的平台支持审计方法，能够满足拟议立法的目标。我们的第一个贡献是列举了现有审计方法在规模化实施这些政策时面临的挑战。其次，我们建议对相关性估计器的有限特权访问是使外部研究人员能够实现可推广的平台支持审计的关键。第三，我们提出了一种审计框架，通过保护用户隐私和平台商业利益不受风险影响，证明平台支持审计无需危及用户隐私或泄露平台的商业利益。针对特定的公平性度量指标，我们证明确保隐私仅会使准确审计所需的样本数量增加一个较小的常数因子（上界为6.34倍，典型参数下为4倍）。我们的技术贡献与正在进行的法律和政策努力相结合，通过跨越隐私与透明度之间的障碍，能够实现对社交媒体平台如何影响个人和社会的公共监督。