Managing the Epistemic Fairness Paradox in AI-Augmented Research
TL;DR
This work offers seven provocations, one adaptive cycle per stakeholder region of the paradox, designed to embed the continuous surfacing and provisional resolution of epistemic friction into the field’s core institutions, so that AI serves as an engine for pluralistic discovery rather than a homogenizing force.
Abstract
The integration of artificial intelligence (AI) into Information Systems (IS) research is driving unprecedented individual productivity while introducing systemic strains: methodological homogenization, workflow opacity, and citation polarization. We argue these pathologies are not transient technological glitches but symptoms of an epistemic fairness paradox: the AI capabilities that maximize fluent, high-volume throughput strain the methodological pluralism and contextual rigor required to study sociotechnical phenomena. Drawing upon the FAIR design theory [13], we translate the architecture of organizational AI fairness to the decentralized epistemic ecosystem and conceptualize the challenge as a single paradox spanning three dimensions of tension (principles, goals, and foci) and three coupled stakeholders: the researcher, the intermediary, and the ecosystem. Because the paradox is endogenous to a rapidly evolving, stochastic, and increasingly agentic technology, static policy will fail. We offer seven provocations, one adaptive cycle per stakeholder region of the paradox, designed to embed the continuous surfacing and provisional resolution of epistemic friction into the field’s core institutions, so that AI serves as an engine for pluralistic discovery rather than a homogenizing force.