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

CHS：小型：协作研究：重建数据驱动的工作场所

• 批准号: 1908327
• 负责人: Phoebe Sengers
• 金额: $ 18.38万
• 依托单位: Cornell University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-10-01 至 2022-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1908327&HistoricalAwards=false
• 关键词: CHS; Small; Collaborative; Research; Reconstructing

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.

这项研究将开发评估启发式方法和方法论创新，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带入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工人的详细详细信息，从而带来改善工作场所安全和绩效改进等收益。然而，这些相同的技术也可能对工人产生负面影响：侵入性，阻碍工人的自我管理和加剧工作环境不佳。 设计工作场所分析以更好地为工人工作的研究面临两个主要挑战：（1）给定复杂的组织动态，很难立即预测工人设计决策的长期成果； （2）研究人员无法访问目前正在工作场所中部署的专有系统的信息和数据。该项目通过收集和分析从早期的工作形式测量形式开发的可用档案数据来解决这些挑战，这些数据构成了当今使用的系统的先例。这些数据将用于了解数据倡导的长期影响，并开发启发式方法和方法，这些方法将为当代技术从业人员设计和倡导数据驱动的工作场所提供指导。这项研究还将为课程设计提供有关HCI方法的课程和针对社会影响的技术的课程，以及一所针对同一主题的代表性不足的学者的暑期学校。这项研究回答了三个问题：（1）在过去的行业挑战中，如何使用工作场所数据来为我们的工人提供倡导我们的工人，以对数据进行类似的挑战？什么有效且无效？ （2）使用数据代表工人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学挑战？ （3）这些设计技术和见解如何在当今的工作场所分析和倡导数据驱动的工作场所的工作场所中使用？ 该项目通过结合历史和设计研究来回答这些问题，该研究围绕国际女士服装工人工会（ILGWU）进行的管理研究项目的案例研究组织。将收集和分析档案数据，以确定使用数据倡导工人的策略，并评估这些策略对受到动荡的经济环境，外包和自动化威胁的行业中工作的长期影响。 通过分析工作场所的历史数据驱动的设计方法以及与当代工会组织者在当代有组织的劳动中使用数据系统的访谈研究，该项目将确定与HCI在数据驱动的工作场所中的HCI研究相关的联盟策略，机会和挑战如何以及以哪些方式与当代研究相关。更广泛的影响审查标准。

项目成果

Seamless Visions, Seamful Realities: Anticipating Rural Infrastructural Fragility in Early Design of Digital Agriculture

无缝的愿景，无缝的现实：在数字农业的早期设计中预见到农村基础设施的脆弱性

• DOI: 10.1145/3491102.3517579
• 发表时间: 2022
• 期刊: CHI '22: CHI Conference on Human Factors in Computing Systems
• 作者: Rubambiza, Gloire;Sengers, Phoebe;Weatherspoon, Hakim
• 通讯作者: Weatherspoon, Hakim

The Tools of Management: Adapting Historical Union Tactics to Platform-Mediated Labor

管理工具：使历史工会策略适应平台中介劳动

• DOI: 10.1145/3359310
• 发表时间: 2019
• 期刊: Proceedings of the ACM on Human-Computer Interaction
• 作者: Khovanskaya, Vera;Dombrowski, Lynn;Rzeszotarski, Jeffrey;Sengers, Phoebe
• 通讯作者: Sengers, Phoebe

Speculation and the Design of Development

投机与开发设计

• DOI: 10.1145/3449195
• 发表时间: 2021
• 期刊: Proceedings of the ACM on Human-Computer Interaction
• 作者: Sengers, Phoebe;Williams, Kaiton;Khovanskaya, Vera
• 通讯作者: Khovanskaya, Vera

Legibility and the Legacy of Racialized Dispossession in Digital Agriculture

数字农业中种族化剥夺的易读性和遗产

• DOI: 10.1145/3479867
• 发表时间: 2021
• 期刊: Proceedings of the ACM on Human-Computer Interaction
• 作者: Liu, Jen;Sengers, Phoebe
• 通讯作者: Sengers, Phoebe

Bottom-Up Organizing with Tools from On High: Understanding the Data Practices of Labor Organizers

使用自上而下的工具进行自下而上的组织：了解劳工组织者的数据实践

• DOI: 10.1145/3313831.3376185
• 发表时间: 2020
• 期刊: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems
• 作者: Khovanskaya, Vera;Sengers, Phoebe;Dombrowski, Lynn
• 通讯作者: Dombrowski, Lynn