RAPID: Revisting Infrastructures of Workplace Accountability amidst 2023 Tech Layoffs

RAPID：在 2023 年科技裁员中重新审视工作场所问责制基础设施

• 批准号: 2327163
• 负责人: Sucheta Ghoshal
• 金额: $ 9.64万
• 依托单位: University of Washington
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-06-01 至 2024-11-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2327163&HistoricalAwards=false
• 关键词: RAPID; Revisting; Infrastructures; Workplace; Accountability

This project seeks to learn about tech workers' experiences, attitudes, and conceptualizations of layoffs to develop principles for co-design of tools and techniques for better workplace accountability towards management software. In early 2023, major technology companies announced layoffs affecting at least 50,000 people. These layoffs came at a time when tech workers are increasingly critical of their employers’ values and practices. As a result, many workers are seeking new means of accountability within and outside the workplace, which could require new principles of software design. A key issue recently has been the role of Human Resources Management (HRM) software in the decision-making that led to these mass layoffs. Media articles reported on the possible role of artificial intelligence (AI) in layoff decisions, based on parameters such as performance reviews and predictions of flight risks. Laid-off employees quickly gathered on online platforms to make sense of the layoffs, including the role of HRM software. While AI-based decision-making is already reportedly common in evaluating performance of gig workers, delivery workers, and other under-paid employees and deciding their futures in the companies, the specific case of software involvement in the management (particularly in the hiring/firing) of a more privileged class of workers is less studied. This project aims to examine the role of existing software technologies in managerial decision-making, while simultaneously developing design principles for better worker-centered accountability tools and techniques applicable for a wide range of techworkers. The research is supported as a RAPID, because it will quickly collect data of scientific value that unexpectedly became available, while tech workers' experiences and attitudes are still fresh in their minds. It will use survey, interview, and asynchronous remote community methods to collect answers to two important questions: (1) What are some perceptions, meaning-making processes, and folk theories - held by laid-off employees - around the role of technologies (such as HRM software) in the 2023 technology layoffs? (2) How can we leverage these grounded folk theories of layoff technologies towards developing counter-strategies and infrastructures of accountability in the workplace? Interviews with ex-employees will focus on algorithmic decision-making in firing and other processes. The results will help shape theories of how tech layoffs impact both employed and recently laid off workers, and contribute to understandings of the sociotechnical infrastructures of workplace accountability by illuminating the interdependencies between workers, management, and information technologies. Findings and analysis will strive to inform labor policies surrounding workers' rights and protections against algorithmic biases.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.

该项目旨在了解技术工人的经验、态度和裁员的概念，以制定工具和技术共同设计的原则，以更好地履行工作场所对管理软件的责任。 2023 年初，各大科技公司宣布裁员，影响至少 5 万人。这些裁员发生之际，科技工作者对雇主的价值观和做法越来越持批评态度。因此，许多员工正在工作场所内外寻求新的问责方式，这可能需要新的软件设计原则。最近的一个关键问题是人力资源管理 (HRM) 软件在导致大规模裁员的决策中的作用。媒体文章根据绩效评估和飞行风险预测等参数，报道了人工智能 (AI) 在裁员决策中可能发挥的作用。被解雇的员工很快聚集在网络平台上，了解裁员的意义，包括人力资源管理软件的作用。据报道，基于人工智能的决策在评估零工工人、送货工人和其他低薪员工的绩效以及决定他们在公司的未来方面已经很常见，但软件参与管理（特别是雇用/解雇）更特权阶层工人的具体案例却很少被研究。该项目旨在研究现有软件技术在管理决策中的作用，同时开发设计原则，以更好地以工人为中心的问责工具和技术适用于广泛的技术工人。这项研究得到了 RAPID 的支持，因为它将快速收集意外获得的具有科学价值的数据，而技术工作者的经验和态度仍然记忆犹新。它将使用调查、访谈和异步远程社区的方法来收集两个重要问题的答案：（1）下岗员工对技术（例如人力资源管理软件）在2023年技术裁员中的作用有哪些看法、意义构建过程和民间理论？ (2) 我们如何利用这些扎根的民间裁员技术理论来制定工作场所的反策略和问责基础设施？对前员工的采访将重点关注解雇和其他过程中的算法决策。研究结果将有助于形成关于技术裁员如何影响就业和最近失业工人的理论，并通过阐明工人、管理层和信息技术之间的相互依赖关系，有助于理解工作场所问责制的社会技术基础设施。调查结果和分析将努力为围绕工人权利和针对算法偏见的保护的劳工政策提供信息。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。