Recognition in Personal Data: Data Warping, Recognition Concessions, and Social Justice

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48.4.12

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Publication History

Received: April 1, 2022
Revised: 
February 24, 2023; August 30, 2023; October 30, 2023
Accepted: December 4, 2023
Published as Accepted Author Version: July 18, 2024
Published as Articles in Advance: September 23, 2024
Published in Issue: December 1, 2024

https://doi.org/10.25300/MISQ/2023/18088 


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Creative Commons Attribution 4.0 International License.

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Abstract

Data-related harm and injustice are commonly viewed through instrumental, procedural, distributional, or representational theories of social justice. These theories do not account for the social injustice that occurs through the lack of recognition of individuals when data are first conceptualized. We explore recognition of individuals in data conceptualization by drawing from information systems (IS) literature on data artifacts that acknowledges the fact that data are comprised of semantics and formats. Guided by recognition theory, we studied a project to expand sexual orientation and gender identity (SOGI) data collection at a public, U.S.-based LGBTQ+ -welcoming university. We found that while the actors involved worked towards recognizing in SOGI data the fact that identities are layered, non-binary, plural, and fluid, the data themselves still misrecognized individuals due to data warping. We argue that data warping occurs because of recognition concessions between social recognition through data semantics and systems recognition through data formats. Such concessions are both necessary for some recognition but accessory to misrecognition. Our findings have implications for recognition theory, data justice, and information systems research, as well as for personal data in practice.

Additional Details
Author Marta Stelmaszak, Erica L. Wagner, and Nicolle Nixon DuPont
Year 2024
Volume 48
Issue 4
Keywords Personal data, data conceptualization, data artifacts, gender, recognition, social justice, SOGI
Page Numbers 1611-1636
Copyright © 2024 MISQ. All rights reserved.