Information avoidance and uncomfortable knowledge: conceptualizing responses to the wicked problem of GenAI

Abstract

This conceptual article examines wicked problems as information problems that have the potential to (1) contribute to information avoidance and (2) produce uncomfortable knowledge. It aims to bridge a gap in information science by integrating these three constructs to explain polarized individual and institutional responses, using the example of generative AI (GenAI). Drawing on interdisciplinary literature from planning, social science and human information behavior, the study develops a unifying macro–meso–micro framework. GenAI is used as an illustrative case to demonstrate how wicked problem characteristics generate uncertainty, which in turn has the potential to trigger avoidance behaviors across levels and produce uncomfortable knowledge. At the micro level, individuals avoid information as an adaptive response to affective discomfort and uncertainty. At the meso level, institutions respond through strategies such as denial, dismissal, diversion and displacement. Using the example of GenAI, these dynamics reinforce polarized, all-or-nothing responses, despite its inherent complexity. As a conceptual study, the framework requires empirical testing across contexts to validate relationships among levels. This article offers a novel conceptual integration of wicked problems, information avoidance and uncomfortable knowledge within a single framework. It advances understanding of information avoidance as a strategic, multi-level response to complex information environments.