学习平台上集体的统计合谋
Statistical Collusion by Collectives on Learning Platforms
AI总结：
本文研究集体如何通过协调提交修改数据来影响学习平台，提出一个理论和算法框架用于先验评估集体效果并开发可实施协调算法，并在产品评估领域验证。
摘要：
随着平台越来越依赖学习算法，集体可能形成并寻求影响这些平台以使其符合自身利益的方式。这可以通过协调提交修改后的数据来实现。为了评估此类行为的潜在影响，理解集体必须执行的计算以这种方式影响平台至关重要。特别是，集体在采取行动前需要对集体的效果进行先验评估，因为他们在修改数据时可能面临潜在风险。此外，他们需要基于可从观测数据推断出的量来开发可实施的协调算法。我们开发了一个框架，对这些问题的理论和算法处理进行了阐述，并在产品评估领域展示了实验结果。
英文摘要：
As platforms increasingly rely on learning algorithms, collectives may form and seek ways to influence these platforms to align with their own interests. This can be achieved by coordinated submission of altered data. To evaluate the potential impact of such behavior, it is essential to understand the computations that collectives must perform to impact platforms in this way. In particular, collectives need to make a priori assessments of the effect of the collective before taking action, as they may face potential risks when modifying their data. Moreover they need to develop implementable coordination algorithms based on quantities that can be inferred from observed data. We develop a framework that provides a theoretical and algorithmic treatment of these issues and present experimental results in a product evaluation domain.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
