Who's Sense is This? Possibility for Impacting Human Insights in AI-assisted Sensemaking
This work highlights a potential problem in AI-assisted collaborative sensemaking for groups processing information, but it is incremental as it builds on known human cognitive biases without introducing new methods or data.
The paper examines how AI systems might prematurely present insights during collaborative sensemaking, potentially biasing users toward unverified perspectives before their understanding is fully formed, and raises three questions about this risk without providing experimental results or numerical data.
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.