CHS: Small: Collaborative Research: Reconstructing the data-driven workplace

这项研究将发展评估启发式和方法创新，以更好地将工人的观点纳入数据驱动的工作场所系统的研究和设计中。传感技术和数据收集的进步正将我们推向一个数据驱动的工作场所。现在，雇主可以通过数字通信和可穿戴传感器收集有关员工的详细、细致的信息，从而提高工作场所的安全性和绩效。然而，这些技术也可能对工人产生负面影响：具有侵入性，阻碍工人的自我管理，并加剧恶劣的工作环境。设计工作场所分析以更好地为员工服务的研究面临两个关键挑战：(1)考虑到复杂的组织动态，很难立即预测设计决策对员工的长期影响；(2)研究人员无法获得目前在工作场所部署的专有系统的信息和数据。该项目通过收集和分析来自早期工作度量形式的开发的可用档案数据来解决这些挑战，这些工作度量形式形成了今天使用的系统的先例。这些数据将用于了解数据倡导的长期影响，并开发启发式和方法，为当代技术从业者设计和倡导数据驱动的工作场所提供指导。这项研究还将为HCI方法和社会影响设计技术课程的课程设计提供信息，并为同一主题的代表性不足的学者开设暑期学校。这项研究回答了三个问题：(1)在一个面临与今天数据驱动的工作场所类似的挑战的行业中，过去如何使用工作场所数据来倡导美国工人？什么是有效的，什么是无效的？(2)使用数据来代表员工观点的决策和挑战如何映射到影响当今工作场所测量分析设计的类似方法挑战？(3)如何将这些过去的设计技术和见解应用于当今的工作场所分析和数据驱动工作场所的员工倡导？该项目通过结合历史和设计研究来回答这些问题，并围绕国际女装工人联盟（ILGWU）开展的管理工程项目的案例研究进行组织。将收集和分析档案数据，以确定利用数据为工人辩护的策略，并评估这些策略对受不稳定的经济气候、外包和自动化威胁的行业工作的长期影响。通过对工作场所历史数据驱动设计方法的分析，以及对当代工会组织者在当代有组织劳工中使用数据系统的访谈研究，该项目将确定过去的工会战略、机遇和挑战如何以及以何种方式与数据驱动工作场所的HCI研究中的当代问题相关。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。

This research will develop evaluation heuristics and methodological innovations to better incorporate worker perspectives into the research and design of data-driven workplace systems. Advances in sensing technology and data collection are moving us toward a data-driven workplace. Employers can now gather detailed, granular information about workers from digital communication and wearable sensors, leading to benefits such as improved workplace safety and performance improvements. Yet, these same technologies also have potential for negative impacts on workers: to be invasive, hinder worker self-management, and exacerbate poor working environments. Research into designing workplace analytics to work better for workers faces two key challenges: (1) given complex organizational dynamics, it is hard to immediately predict the long-term outcomes of design decisions on workers; and (2) researchers lack access to information about and data from proprietary systems currently being deployed in the workplace. This project addresses these challenges by collecting and analyzing available archival data from the development of earlier forms of work measurement that form the precedent for systems used today. This data will be used to understand the long-term impact of data advocacy, and to develop heuristics and methods that will provide guidance for contemporary technology practitioners in design of and advocacy for the data-driven workplace. This research will also inform curriculum design for courses on HCI methods and designing technology for social impact, and a summer school for underrepresented scholars on the same topic.This research answers three questions: (1) How has workplace data been used to advocate for US workers in the past, in an industry facing analogous challenges to data-driven workplaces today? What was effective and ineffective? (2) How do the decisions and challenges of using data to represent worker perspectives map onto analogous methodological challenges that impact the design of workplace measurement analytics today? (3) How can these design techniques and insights from the past be used in present-day workplace analytics and advocacy for workers in data-driven workplaces? This project answers these questions by combining historical and design research, organized around a case study of management-engineering projects conducted by the International Ladies Garment Workers Union (ILGWU). Archival data will be gathered and analyzed to identify strategies to use data to advocate for workers and to evaluate the long-term impact of those strategies on work in an industry threatened by a volatile economic climate, outsourcing, and automation. Through an analysis of historical data-driven design methods for the workplace and an interview study with contemporary union organizers on the use of data systems in contemporary organized labor, the project will identify how and in what ways past union strategies, opportunities, and challenges are relevant to contemporary issues in HCI research in data-driven workplaces.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.