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，这些算法是由使用私人数据的多边在线平台选择和运行的。虽然这些平台经常声称他们的算法考虑了双方的利益并保护了隐私，但这些说法并没有明确定义。此外，这些平台的算法也会根据自己的目标进行优化，比如财务或用户增长。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观不一致。该项目遵循的研究议程包括两个主要重点。第一个目标是在平台特定的环境中为数据共享部署差异隐私，以确保对平台参与者的严格隐私保护，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学习增强和安全增强框架，用于推理和部署差异隐私。第二个研究重点是调查不透明优化的不良后果，并提出可以对平台参与者或社会对这种优化结果的期望进行编码的定义。然后分析实现这些约束的算法、系统和政策方法，并定量地评估实施这些约束的影响。这两项研究都推进了重要的社会目标，即通过多边平台实现数据驱动的创新，同时保护参与者的隐私和公平。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值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.