Data In-Furs: A Narrative Critique of Data-Driven Solutions for Countering Social Marginalization

• In Event: Technology, Inequality, and Social Justice - I

Abstract

The motivation for this work stems from observations of algorithmic bias, which can further or create new avenues for social marginalization. As record of algorithmic bias grows it is increasingly concerning that those harmed are those that have been historically marginalized. However, government and economic forces continue to position big data as the solution to tackling society’s greatest challenges, many of which are directed at helping the exact populations who are the victims of data harms, which presents a misalignment between the social will to create a just and equitable society and the impact of technologies furthering social inequalities. Such a misalignment offers the entry for this presentation.

This presentation will address findings from ethnographic research with data activists and the peripheral institutions they interact with to discuss how technologies are narratively positioned to counter social marginalization. Taking theoretical influence from people of color critiques of queer theory, the presentation focuses discussion on the racialized attachments (i.e., Sharon Holland) and queer orientations (i.e., Sara Ahmed) embedded in the narratives of big data solutions for countering social marginalization. Particularly highlighted is a contrast between institutional rhetoric (e.g., access, inclusion) and activist rhetoric (e.g., taking, white supremacy) to bring attention to a current lack of space for speaking about diverse histories when engaging with big-data solutions to social inequities. Conclusion is focused on concepts of miscegenation and sadism to suggest frames for decolonizing technologies oriented towards helping marginalized groups.

Author

• Janine Slaker, Michigan State University