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

这项研究将开发评估方法和方法创新，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在将我们推向数据驱动的工作场所。雇主现在可以从数字通信和可穿戴传感器中收集有关工人的详细信息，从而带来改善工作场所安全和提高绩效等好处。然而，这些技术也有可能对工人产生负面影响：具有侵入性，阻碍工人自我管理，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）研究人员无法访问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早期工作测量形式的发展中可用的档案数据来应对这些挑战，这些数据构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，并制定方法和方法，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。这项研究还将为人机交互方法和设计社会影响技术的课程设计提供信息，并为同一主题的代表性不足的学者举办暑期学校。这项研究回答了三个问题：（1）在过去，工作场所数据是如何被用来为美国工人辩护的，在一个面临类似挑战的行业中，今天的数据驱动的工作场所？什么是有效的和无效的？(2)如何使用数据来代表工人的观点的决策和挑战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)如何将这些设计技术和过去的见解用于当今的工作场所分析，并为数据驱动的工作场所中的员工提供支持？ 该项目通过结合历史和设计研究来回答这些问题，围绕国际女装工人联盟（ILGWU）进行的管理工程项目案例研究进行组织。档案数据将被收集和分析，以确定使用数据来倡导工人的战略，并评估这些战略对受动荡的经济气候，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关于数据系统在当代有组织劳动中的使用的采访研究，该项目将确定过去工会战略、机会、和挑战与数据中HCI研究的当代问题有关-该奖项反映了NSF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.