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：社交媒体平台策划着信息与机遇的获取途径，因此在当今塑造公共话语方面扮演着关键角色。这些平台用于策划内容的算法具有不透明性，引发了诸多社会问题。先前的研究已采用黑箱方法证明，这些算法可能导致偏见或歧视性结果。然而，现有的审计方法因独立于平台运行而面临根本性局限。对潜在危害的担忧促使美国和欧盟提出立法建议，要求建立一种新型审计机制，让经过审查的外部研究人员获得对社交媒体平台的优先访问权限。遗憾的是，迄今为止尚无具体的技术方案来实现此类审计，因为大规模审计可能泄露用户隐私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，能够实现拟议立法的目标。我们的第一项贡献是列举出现有审计方法在规模化实施这些政策时所面临的挑战。其次，我们提出，对相关性估计器进行有限且优先的访问，是实现外部研究人员可推广的平台支持审计的关键。第三，我们通过提出一个能防范隐私泄露和平台商业利益披露风险的审计框架，论证了平台支持审计无需牺牲用户隐私或泄露平台商业利益。针对某一特定公平性指标，我们证明，确保隐私仅会使精准审计所需样本量增加一个较小的常数因子（上限为6.34倍，典型参数下为4倍）。我们的技术贡献，结合正在进行的法律与政策努力，能够通过跨越隐私与透明度之间的障碍，实现公众对社交媒体平台如何影响个人与社会的监督。