TY - JOUR

T1 - Can Workplace Tracking Ever Empower? Collective Sensemaking for the Responsible Use of Sensor Data at Work

AU - Holten Møller, Naja

AU - Neff, Gina

AU - Simonsen, Jakob Grue

AU - Villumsen, Jonas Christoffer

AU - Bjørn, Pernille

PY - 2021/7

Y1 - 2021/7

N2 - 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.

AB - 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.

KW - data mining

KW - ethnography

KW - sensemaking with data

KW - sensor tracking

KW - worker contestability

UR - http://www.scopus.com/inward/record.url?scp=85110447000&partnerID=8YFLogxK

U2 - 10.1145/3463931

DO - 10.1145/3463931

M3 - Journal article

AN - SCOPUS:85110447000

VL - 5

JO - Proceedings of the ACM on Human-Computer Interaction

JF - Proceedings of the ACM on Human-Computer Interaction

SN - 2573-0142

IS - GROUP

M1 - 219

ER -