FW-HTF-RM: Bridging AI Inequality in Digitally-Mediated Gig Work

这一人类-技术前沿-研究：中等(FW-HTF-RM)奖项的未来工作支持研究，以研究和缓解人工智能(AI)导致的数字中介零工工作中平台和工人之间日益严重的不平等。该项目专门针对基于应用的拼车，这是一个新兴行业，截至2023年，美国有超过150万司机。在拼车方面，人们经常观察和报道对收入差距和工作场所歧视等不平等问题的担忧。这种新出现的人工智能不平等是由两个方面驱动的：技术鸿沟和数据鸿沟。技术鸿沟与零工工作平台如何使用先进的人工智能系统来分配资源、调度任务和确定工人工资有关，而工人缺乏类似的技术接入。数据分割是指平台从所有员工和客户那里收集和整合大量数据，以帮助他们的运营，而其他各方仍然没有类似的数据访问。该项目将首先衡量和表征拼车平台中的这种人工智能不平等。根据得出的见解，研究团队将为司机设计、创建和部署一个支持人工智能的数据驱动决策支持系统，以帮助他们以最大利益规划自己的工作，长期目标是弥合拼车平台中的人工智能不平等。这一项目的成果也将惠及算法管理的其他按需零工工作领域，如在线自由职业和数据注释。该项目汇集了几个学科，包括人机交互、机器学习、劳动经济学和劳动社会学。研究人员团队的结构是为了实现多个趋同目标。首先，该项目寻求使用数据驱动的方法来定量衡量平台和工人之间的人工智能不平等。其次，该项目将从社会学的角度，使用定性和定量的混合方法来表征人工智能不平等以及司机对算法管理的反应。利用这些发现，研究团队将开发一个自下而上的智能个人助理网络，帮助司机计划工作并做出符合他们最佳利益的决定。司机及其助手的网络共享数据，从而能够对任务需求和供应、客户和员工行为以及定价变化进行预测性建模。最后，通过实地部署，研究小组将研究所研究系统的采用情况，并衡量其实际影响。该项目由人类-技术前沿交叉部门工作未来计划资助，旨在通过推进与人类工人和谐运作的智能工作技术的设计，促进对工作环境中相互依赖的人类-技术伙伴关系的更深层次的基本了解。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。

This Future of Work at the Human-Technology Frontier - Research: Medium (FW-HTF-RM) award supports research to study and mitigate the growing inequality between platforms and workers in digitally-mediated gig work caused by artificial intelligence (AI). The project specifically targets app-based ridesharing, a newly emerging industry with more than 1.5 million drivers in the United States as of 2023. In ridesharing, concerns of inequality such as income disparities and workplace discrimination are frequently observed and reported. This emerging AI inequality is driven by two facets: a technology divide and a data divide. The technology divide pertains to how gig work platforms use advanced AI systems to allocate resources, dispatch tasks, and determine worker pay, while workers lack comparable technological access. The data divide refers to the platforms' collection and consolidation of vast data from all workers and customers to aid their operations, while other parties remain without similar data access. The project will first measure and characterize such AI inequality in rideshare platforms. Based on the derived insights, the research team will design, create, and deploy an AI-enabled data-driven decision-making support system for drivers to help them plan for their work in their best interests, with a long-term goal of bridging AI inequality in rideshare platforms. Outcomes from this project will also benefit other domains of on-demand gig work with algorithmic management, such as online freelancing and data annotation.This project brings together several disciplines, including human-computer interaction, machine learning, labor economics, and sociology of labor. The investigator team is structured to achieve multiple convergent goals. First, the project seeks to quantitatively measure AI inequality between platforms and workers using a data-driven approach. Second, the project will characterize AI inequality and how drivers react to algorithmic management using a mix of qualitative and quantitative methods from a sociological perspective. Utilizing the findings, the research team will develop a bottom-up network of intelligent personal assistants that help drivers plan for their work and make decisions in their best interests. A network of drivers and their assistants share data, which enables the predictive modeling of task demand and supply, customer and worker behaviors, and pricing changes. Lastly, through a field deployment, the research team will study the adoption of the researched system and measure its real-world impacts. This project has been funded by the Future of Work at the Human-Technology Frontier cross-directorate program to promote deeper basic understanding of the interdependent human- technology partnership in work contexts by advancing design of intelligent work technologies that operate in harmony with human workers.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.