Ai shapes how people share and understand visual information
Sensemaking as Artifact: Accumulated Influence in AI-Mediated Information Environments
Human-Computer Interaction
Summary
When people view images or other visual information, AI can now help them create new versions that show what they think or learn. These new AI-made versions can then be seen by others, affecting how they understand and think about the original information. The authors show how this changes the way people communicate visually and highlights challenges like tracking where ideas come from and understanding information that's already been changed by AI. They suggest studying how this ongoing change affects what people trust and pay attention to over time.
What this means in practice
- •For visual communication designers: Design new ways to show how AI transformations affect visual messages and improve user understanding of mediated images.
- •For social media content moderators: Develop guidelines for identifying and managing AI-mediated visual content that inherits and changes meaning over multiple shares.
A position paper. It proposes an approach and reports no results.
Authors
Manling Yang, Remco Chang
Abstract
Generative AI is changing what can happen after a source artifact reaches its audience. A viewer's interpretation can now be externalized into a derivative artifact, allowing private sensemaking to become part of subsequent communication. Once such a derivative artifact circulates, it can enter subsequent viewers' information environments and shape the conditions under which their later sensemaking occurs. In this paper, we examine how this shift changes visual information communication. We first consider the viewer's immediate interaction with a source artifact and generative AI. We then examine what becomes consequential when the viewer's sensemaking takes communicative form, including communicative commitment, the legibility of transformations and source relationships, and the literacy required to interpret already-mediated information. Finally, we broaden the unit of analysis to consider how repeated and distributed AI mediation may accumulate over time, shaping what subsequent viewers notice, consider plausible, trust, and carry into subsequent sensemaking. We argue that understanding these longer-term forms of influence is a research direction for AI-mediated visual communication.