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 the 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.
Stelmaszak, M., Wagner, E. L., & DuPont, N. N. (2024). Recognition in personal data: data warping, recognition concessions, and social justice. MIS Quarterly, 48(4), 1611-1636.
