TY - GEN

T1 - Investigating DataWork Across Domains

AU - Pine, Kathleen

AU - Bossen, Claus

AU - Holten Møller, Naja

AU - Miceli, Milagros

AU - Lu, Alex Jiahong

AU - Chen, Yunan

AU - Horgan, Leah

AU - Su, Zhaoyuan

AU - Neff, Gina

AU - Mazmanian, Melissa

N1 - Publisher Copyright: © 2022 Owner/Author.

PY - 2022

Y1 - 2022

N2 - In the wake of the hype around big data, artificial intelligence, and "data-drivenness,"much attention has been paid to developing novel tools to capitalize upon the deluge of data being recorded and gathered automatically through IT systems. While much of this literature tends to overlook the data itself - sometimes even characterizing it as "data exhaust"that is readily available to be fed into algorithms, which will unlock the insights held within it - a growing body of literature has recently been directed at the (often intensive and skillful) work that goes into creating, collecting, managing, curating, analyzing, interpreting, and communicating data. These investigations detail the practices and processes involved in making data useful and meaningful so that aims of becoming data-driven' or data-informed' can become real. Further, In some cases, increased demands for data work have led to the formation of new occupations, whereas at other times data work has been added to the task portfolios of existing occupations and professions, occasionally affecting their core identity. Thus, the evolving forms of data work are requiring individual and organizational resources, new and re-tooled practices and tools, development of new competences and skills, and creation of new functions and roles. While differences exist across the global North and the global South experience of data work, such factors of data production remain paramount even as they exist largely for the benefit of the data-driven system [21, 32]. This one-day workshop will investigate existing and emerging tasks of data work. Further, participants will seek to understand data work as it impacts: individual data workers; occupations tasked with data work (existing and emerging); organizations (e.g. changing their skill-mix and infrastructuring to support data work); and teaching institutions (grappling with incorporation of data work into educational programs). Participants are required to submit a position paper or a case study drawn from their research to be reviewed and accepted by the organizing committee (submissions should be up to four pages in length). Upon acceptance, participants will read each other's paper, prepare to shortly present and respond to comments by two discussants and other participants. Subsequently, the workshop will focus on developing a set of core processes and tasks as well as an outline of a research agenda for a CHI-perspective on data work in the coming years.

AB - In the wake of the hype around big data, artificial intelligence, and "data-drivenness,"much attention has been paid to developing novel tools to capitalize upon the deluge of data being recorded and gathered automatically through IT systems. While much of this literature tends to overlook the data itself - sometimes even characterizing it as "data exhaust"that is readily available to be fed into algorithms, which will unlock the insights held within it - a growing body of literature has recently been directed at the (often intensive and skillful) work that goes into creating, collecting, managing, curating, analyzing, interpreting, and communicating data. These investigations detail the practices and processes involved in making data useful and meaningful so that aims of becoming data-driven' or data-informed' can become real. Further, In some cases, increased demands for data work have led to the formation of new occupations, whereas at other times data work has been added to the task portfolios of existing occupations and professions, occasionally affecting their core identity. Thus, the evolving forms of data work are requiring individual and organizational resources, new and re-tooled practices and tools, development of new competences and skills, and creation of new functions and roles. While differences exist across the global North and the global South experience of data work, such factors of data production remain paramount even as they exist largely for the benefit of the data-driven system [21, 32]. This one-day workshop will investigate existing and emerging tasks of data work. Further, participants will seek to understand data work as it impacts: individual data workers; occupations tasked with data work (existing and emerging); organizations (e.g. changing their skill-mix and infrastructuring to support data work); and teaching institutions (grappling with incorporation of data work into educational programs). Participants are required to submit a position paper or a case study drawn from their research to be reviewed and accepted by the organizing committee (submissions should be up to four pages in length). Upon acceptance, participants will read each other's paper, prepare to shortly present and respond to comments by two discussants and other participants. Subsequently, the workshop will focus on developing a set of core processes and tasks as well as an outline of a research agenda for a CHI-perspective on data work in the coming years.

KW - Data Work

KW - Data-Driven

KW - Datafication

KW - Labor

KW - Occupations

UR - http://www.scopus.com/inward/record.url?scp=85129721034&partnerID=8YFLogxK

U2 - 10.1145/3491101.3503724

DO - 10.1145/3491101.3503724

M3 - Article in proceedings

AN - SCOPUS:85129721034

SP - 1

EP - 6

BT - CHI 2022 - Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems

PB - Association for Computing Machinery, Inc.

T2 - 2022 CHI Conference on Human Factors in Computing Systems, CHI EA 2022

Y2 - 30 April 2022 through 5 May 2022

ER -