Bias in the Machine? A Solution to the Isolationist Problem Through a Sociotechnical Understanding of Bias in AI Ethics

Abstract

Dominant approaches to bias in artificial intelligence (AI) are structured by what I identify as the isolationist problem: the tendency to treat bias as a discrete, technically addressable flaw within the AI development pipeline, rather than as a relational phenomenon embedded in social, institutional, and political arrangements. This problem is sustained by two mutually reinforcing orientations: technocentrism, which reframes ethical challenges as engineering problems amenable to computational resolution, and the bias-centric conception of fairness, which reduces fairness to statistical mitigation and obscures its contested, context-dependent character. Together, these orientations produce ontological, epistemic, and practical forms of narrowing ethical imagination and channel intervention into technically tractable but socially limited responses. Against this, I propose sociotechnical sensitivity as both an analytical orientation and a normative commitment: a sustained attentiveness to the ways in which AI systems are constitutively embedded in social relations, institutional arrangements, cultural norms, and power structures. The paper’s central argumentative shift is to change the narrative from bias mitigation to bias management—treating bias not as a defect to be corrected but as an ongoing condition to be governed. These arguments are developed through an extended analysis of the well-known case of the COMPAS algorithm, a recidivism risk-assessment prediction tool, illustrating three practical axes of bias management: contextualisation, institutionalisation, and iteration. The gap between sociotechnically sensitive AI ethics and its realisation is ultimately a matter of governance design and political will, not merely a problem of missing methods or tools.