论文标题
使用匿名人群数据来衡量Airbnb客人接受率的差异
Measuring Discrepancies in Airbnb Guest Acceptance Rates Using Anonymized Demographic Data
论文作者
论文摘要
为了使技术系统和平台更加公平，组织必须能够衡量潜在不平等的规模以及拟议解决方案的功效。在本文中，我们介绍了一个系统，该系统可以测量平台用户体验中的差异，这些系统可归因于使用匿名数据感知的种族（经验差距）。这允许在这方面取得进展，同时限制任何潜在的隐私风险。具体而言，该系统强制执行P敏感K匿名性的隐私模型来进行测量，而无需存储或访问用户标识符和感知的种族之间的1：1映射。我们在Airbnb招待会体验的背景下测试该系统。我们基于仿真的功率分析表明，该系统可以用与非匿名数据相当的精度来衡量所提出的范围范围干预措施的功效。我们的工作确定，使用匿名数据的经验差距的衡量是可行的，可用于指导制定政策，以促进Airbnb以及其他技术平台的用户的公平成果。
In order to make technological systems and platforms more equitable, organizations must be able to measure the scale of potential inequities as well as the efficacy of proposed solutions. In this paper, we present a system that measures discrepancies in platform user experience that are attributable to perceived race (experience gaps) using anonymized data. This allows for progress to be made in this area while limiting any potential privacy risk. Specifically, the system enforces the privacy model of p-sensitive k-anonymity to conduct measurement without ever storing or having access to a 1:1 mapping between user identifiers and perceived race. We test this system in the context of the Airbnb guest booking experience. Our simulation-based power analysis shows that the system can measure the efficacy of proposed platform-wide interventions with comparable precision to non-anonymized data. Our work establishes that measurement of experience gaps with anonymized data is feasible and can be used to guide the development of policies to promote equitable outcomes for users of Airbnb as well as other technology platforms.
