RAPID: Revisting Infrastructures of Workplace Accountability amidst 2023 Tech Layoffs

该项目旨在了解技术工作者的裁员经验、态度和概念化，以制定共同设计工具和技术的原则，从而更好地承担工作场所对管理软件的责任。2023年初，主要科技公司宣布裁员，影响到至少5万人。这些裁员发生在科技工人对雇主的价值观和做法越来越挑剔的时候。因此，许多员工在工作场所内外寻求新的问责手段，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)软件在导致这些大规模裁员的决策中的作用。媒体文章报道了人工智能(AI)在裁员决策中可能发挥的作用，这些决策基于绩效评估和逃亡风险预测等参数。下岗员工迅速聚集在在线平台上，以了解裁员的意义，包括人力资源管理软件的作用。尽管据报道，基于人工智能的决策在评估零工工人、送货员和其他低薪员工的表现并决定他们在公司的未来方面已经很常见，但关于软件参与更特权工人阶层的管理(尤其是在招聘/解雇)的具体案例研究较少。该项目旨在审查现有软件技术在管理决策中的作用，同时制定更好的以工人为中心的问责工具和技术的设计原则，适用于广泛的技术工作者。这项研究被认为是一项快速的研究，因为它将迅速收集出人意料地获得的具有科学价值的数据，而技术工作者的经历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)关于技术(如人力资源管理软件)在2023年技术裁员中的作用，下岗员工持有的一些看法、意义形成过程和民间理论是什么？(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问责的对策和基础设施？对前雇员的采访将集中在解雇和其他过程中的算法决策。这些结果将有助于形成关于科技裁员如何影响在职和最近下岗工人的理论，并通过阐明工人、管理层和信息技术之间的相互依存关系，有助于理解工作场所问责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的劳工政策和针对算法偏差的保护提供信息。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。

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.