Can Workplace Tracking Ever Empower? Collective Sensemaking for the Responsible Use of Sensor Data at Work

Abstract

People are increasingly subject to the tracking of data about them at their workplaces. Sensor tracking is used by organizations to generate data on the movement and interaction of their employees to monitor and manage workers, and yet this data also poses significant risks to individual employees who may face harms from such data, and from data errors, to their job security or pay as a result of such analyses. Working with a large hospital, we developed a set of intervention strategies to enable what we call "collective sensemaking" describing worker contestation of sensor tracking data. We did this by participating in the sensor data science team, analyzing data on badges that employees wore over a two-week period, and then bringing the results back to the employees through a series of participatory workshops. We found three key aspects of collective sensemaking important for understanding data from the perspectives of stakeholders: 1) data shadows for tempering possibilities for design with the realities of data tracking; 2) data transducers for converting our assumptions about sensor tracking, and 3) data power for eliciting worker inclusivity and participation. We argue that researchers face what Dourish (2019) called the "legitimacy trap" when designing with large datasets and that research about work should commit to complementing data-driven studies with in-depth insights to make them useful for all stakeholders as a corrective to the underlying power imbalance that tracked workers face.

DISCERN: Designing Decision Support Interfaces to Investigate the Complexities of Workplace Social Decision-Making With Line Managers

“…Additionally, we find that line managers currently lack the means to capture and articulate their decision-making rationale to their teams. While decision support systems do not enforce full transparency, they create the potential records, paving the way for accountability and contestability [29]. Lastly, decision support tools can normatively encourage decision-makers to expicitly articulate what they value, which has the potential to reduce deviations from stated values [49].…”

“…First, our focus on line managers meant that workers' perspectives were only represented incidentally, through the lens of line managers, who are workers of the organization themselves. Future work should center the concerns of workers through mutli-stakeholder co-design processes [29,31]. Second, the needs and challenges of line managers in a technology organization are unlikely to represent the challenges of line managers in other kinds of work, such as service work [61], and we caution against generalizing these insights to other contexts.…”

Data-Driven Futures of International Refugee Law

“…Furthermore, computational methods extract specific text and their associated legal meaning arises from 'multiple and complex contents' (Baden et al 2022). Simple quantification of observations do not in themselves make them more (or less) true (Passi and Jackson 2017;Møller et al 2021). Hence, asking qualitative 'small' questions to quantitative 'large' datasets is equally important for data sense making.…”

The Effects of AI on the Working Lives of Women

“…A study by the European Parliament's Special Committee on Artificial Intelligence in a Digital Age (AIDA) found that some participants consider algorithms to provide objective and neutral ways of measuring employee performance and eliminating the possibility of individual biases (Deshpande et al, 2021). However, this depends on what and how the systems measure, according to what standards, and how workers are helped to make sense of the feedback (Holten Møller et al, 2021).…”