CAREER: Towards Privacy and Fairness in Multi-Sided Platforms

个人的生活和社会结果越来越多地受到不透明的机器学习算法的影响，这些算法由使用私人数据的多边在线平台选择和运行。尽管这些平台经常声称他们的算法考虑了各方的利益并保护了隐私，但这些说法并没有明确定义。此外，这些平台的算法还会针对自己的目标进行优化，例如财务或用户增长。由此产生的算法决策系统及其结果可能与平台参与者的利益和社会价值不一致。该项目遵循由两个主要推动力组成的研究议程。第一个目标是在特定平台的情况下为数据共享部署不同的隐私，以确保平台参与者获得严格的隐私保护，同时使平台能够实现其目标。我们利用特定于平台的功能来开发增强学习和增强安全性的框架，用于推理和部署差异隐私。第二个研究重点调查了不透明优化的不良后果，并提出了可以编码平台参与者或社会对这种优化结果的期望的定义。然后，它分析了实现这些约束的算法、系统和政策方法，并定量评估了实施这些约束的影响。这两项研究都推进了通过多边平台实现数据驱动创新的社会重要目标，同时保护了参与者的隐私和公平。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

Individuals’ lives and societal outcomes are increasingly mediated by opaque machine learning algorithms chosen and run by multi-sided online platforms using private data. Although the platforms often claim that their algorithms take into consideration the interests of the sides and preserve privacy, these claims are not well-defined. Furthermore, the platforms’ algorithms also optimize for their own objectives, such as financial or user growth. The resulting algorithmic decision-making systems and their outcomes may be at odds with the interests of platform participants and societal values.The project is following a research agenda consisting of two main thrusts. The first aims to enable the deployment of differential privacy for data sharing in platform-specific contexts, so as to ensure rigorous privacy protections for platform participants while enabling the platform to pursue its objectives. We take advantage of platform-specific capabilities to develop learning-augmented and security-augmented frameworks for reasoning about and deploying differential privacy. The second research thrust investigates undesirable consequences of opaque optimizations and proposes definitions that could encode platform participants’ or societal desiderata regarding the outcomes of such optimization. It then analyzes algorithmic, systems, and policy approaches for achieving them and quantitatively evaluates the impact of enforcing such constraints.Both research thrusts advance the societally important goals of enabling data-driven innovation by multi-sided platforms while preserving privacy and fairness for their participants.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.