Conceptualizing Algorithmic Stigmatization

Abstract

Algorithmic systems have infiltrated many aspects of our society, mundane to high-stakes, and can lead to algorithmic harms known as representational and allocative. In this paper, we consider what stigma theory illuminates about mechanisms leading to algorithmic harms in algorithmic assemblages. We apply the four stigma elements (i.e., labeling, stereotyping, separation, status loss/discrimination) outlined in sociological stigma theories to algorithmic assemblages in two contexts : 1) "risk prediction" algorithms in higher education, and 2) suicidal expression and ideation detection on social media. We contribute the novel theoretical conceptualization of algorithmic stigmatization as a sociotechnical mechanism that leads to a unique kind of algorithmic harm: algorithmic stigma. Theorizing algorithmic stigmatization aids in identifying theoretically-driven points of intervention to mitigate and/or repair algorithmic stigma. While prior theorizations reveal how stigma governs socially and spatially, this work illustrates how stigma governs sociotechnically.