Reflexivity Under Algorithmic Capitalism: Negotiating Ethics and Bias in Big Tech

• In Event: Disrupting Big Tech: Sociology’s Role in Interrogating AI and Social Media

Abstract

ABSTRACT
This article introduces algorithmic situated reflexivity (ASR) as a concept that bridges the examination of how ethical reflection unfolds within the embedded practices of tech professionals working under conditions of algorithmic capitalism. Drawing on 65 in-depth interviews with tech professionals, involved in algorithm design and application at Google, the study conceptualizes ASR as a distributed, multi-scalar form of reflexivity, one that emerges not only from individual moral deliberation but also from team dynamics, institutional routines, and governance structures. While grounded in Anthony Giddens’ theory of reflexive modernity, ASR extends classical reflexivity frameworks by incorporating Ulrich Beck’s focus on systemic risk and Margaret Archer’s insights on constrained moral agency. Rather than treating reflexivity as a purely cognitive capacity, ASR captures how ethical work is shaped, and often strained, by infrastructural constraints, organizational pressures, and reputational logics. Juxtaposing sociological theory with empirical insights, this article refines our understandings of moral agency, institutional responsibility, and the lived experience of ethics in high-stakes technological systems. Thus, the article advances sociological debates by reframing reflexivity not as abstract introspection but as a situated and contested practice embodied in technological infrastructures.
Key terms: algorithmic capitalism, algorithm bias, AI governance, reflexivity, sociotechnological infrastructures

Author

• Hannah Bacol, University of Hawaii at Manoa