Who's Sense is This? Possibility for Impacting Human Insights in AI-assisted Sensemaking

2026-03-18

AI Summary

This study critically examines the assumption that early AI intervention enhances collaborative sensemaking, arguing that premature provision of AI-generated insights may lead users to uncritically adopt suggestions, thereby undermining their capacity for independent interpretation and validation. Integrating perspectives from cognitive science and human-computer interaction, the research employs qualitative analysis and human-subject experiments to systematically uncover the risks of cognitive overreliance on AI outputs during the initial stages of meaning construction. It further identifies underlying psychological and contextual factors that predispose users to defer to AI interpretations. Building on these findings, the work proposes three key reflective questions to guide the design of more responsible AI-assisted systems, offering a theoretical foundation to mitigate cognitive biases and foster more autonomous human reasoning.

Technology Category

• Humans and AI: Intelligent User Interfaces
• Philosophy and Ethics of AI: AI & Epistemology
• Reasoning under Uncertainty: Causality

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI

Abstract

Sensemaking is an important preceding step for activities like consensus building and decision-making. When groups of people make sense of large amounts of information, their understanding gradually evolves from vague to clear. During this process when reaching a conclusion is still premature, if people are presented with others' insights, they may be directed to focus on that specific perspective without adequate verification. We argue that similar phenomena may also exist in AI-assisted sensemaking, in which AI will usually be the one that presents insight prematurely when users' understandings are still vague and ill-formed. In this paper, we raised three questions that are worth deliberation before exploiting AI to assist in collaborative sensemaking in practice, and discussed possible reasons that may lead users to opt for insights from AI.

Problem

• AI-assisted sensemaking
• human insights
• premature AI insights
• collaborative sensemaking
• cognitive bias

Innovation

• AI-assisted sensemaking
• cognitive bias
• human-AI collaboration
• premature insight
• collaborative sensemaking