Location
Hilton Waikoloa Village, Hawaii
Event Website
https://hicss.hawaii.edu/
Start Date
7-1-2025 12:00 AM
End Date
10-1-2025 12:00 AM
Description
The tendency for AI to refer to itself—for instance using first-person pronouns and referring to (in)capabilities—raises questions about the interpretation and effects of machines’ self-referential language in human-machine communication. AI vary in their tendencies to identify themselves as machines or to mask that ontological category in the course of interactions. To examine how self-referential ontological-category cues (i.e., “As a large language model …”) influence judgments of contextualized agents and their responses, a 2×2×2 experiment was conducted. Participants (N = 800) evaluated an exchange between an inconspicuous user and ChatGPT, manipulated to represent three variables: Machine cue present/absent × natural/technical topic × creative/logical framing. Experimental findings point to a weak interaction effect of the cue and the topic suggesting a mild “stay in your lane” effect. Findings have implications for whether and in what context machines may be more or less favorably evaluated when their machine status is cued.
Recommended Citation
Banks, Jaime, "As a Large Language Model: Ontological-Category Cue Effects on Agent and Message Evaluations" (2025). Hawaii International Conference on System Sciences 2025 (HICSS-58). 4.
https://aisel.aisnet.org/hicss-58/dsm/conversation/4
As a Large Language Model: Ontological-Category Cue Effects on Agent and Message Evaluations
Hilton Waikoloa Village, Hawaii
The tendency for AI to refer to itself—for instance using first-person pronouns and referring to (in)capabilities—raises questions about the interpretation and effects of machines’ self-referential language in human-machine communication. AI vary in their tendencies to identify themselves as machines or to mask that ontological category in the course of interactions. To examine how self-referential ontological-category cues (i.e., “As a large language model …”) influence judgments of contextualized agents and their responses, a 2×2×2 experiment was conducted. Participants (N = 800) evaluated an exchange between an inconspicuous user and ChatGPT, manipulated to represent three variables: Machine cue present/absent × natural/technical topic × creative/logical framing. Experimental findings point to a weak interaction effect of the cue and the topic suggesting a mild “stay in your lane” effect. Findings have implications for whether and in what context machines may be more or less favorably evaluated when their machine status is cued.
https://aisel.aisnet.org/hicss-58/dsm/conversation/4