Abstracted Away: Resisting Alienation and Ungrounded Abstraction in AI Research Communities

Abstract

Logics of abstraction in computational AI research often push important forms of knowledge and reflection aside: dominant standards of legitimacy become separated from lived experience of harm; the stated goals of work misalign with the practices that operationalize them; and career demands crowd out critical reflection. Even as prior efforts—both academic and community-oriented—have sought to recontextualize and challenge common practices, recurring exposure to sociotechnical harms and epistemic injustice has persisted. As three early-career critical AI researchers, we have experienced this as alienation: feeling like outsiders in our research communities. This process of alienation has involved having some aspects of our backgrounds overlooked and others tokenized. We argue that our alienation occurred through mechanisms that mirror abstraction by creating distance from relevant material realities. Beyond abstraction's role in computational AI research as a foundational practice that structures complex computational tasks, we have encountered it as a social norm in computational research spaces, which we illustrate through an autoethnographic inquiry into our alienation. We narrate three vignettes that describe how we encountered and resisted alienation in our research communities. By analyzing themes across these accounts, we construct an interpretive framework of alienation that categorizes its preconditions, mechanisms, and harms. Finally, we identify affect abstraction, one of the mechanisms of alienation we describe, as a high-leverage mechanism that we can resist by staying attuned to our affective responses, and collective action as a way to reduce risk and isolation when engaging in resistance. To assist others with similar reflection, we present our framework as a hermeneutic resource. Engaging critically in self-reflection and meaning-making is a necessary step toward challenging exclusionary disciplinary norms and cultivating more inclusive forms of AI research.