Temporal Aspects of Human-AI Collaborations for Work
Authors: 
Michal Rinott, 
Orit ShaerAuthors Info & Claims
Michal Rinott, Orit ShaerAuthors Info & ClaimsArticle No.: 8, Pages 1 - 7
Abstract
In this position paper we highlight the importance of temporal aspects in human-AI collaboration for work. We focus on chat-based interactions powered by Large Language Models (LLMs), because of their increasing availability and extensive use for knowledge-based and creative work. We present four areas in which the different temporal characteristics of humans and of LLMs influence the interaction between them: interaction pace, temporal context, continuity over time, and rhythm and attunement. In each area we discuss these differences and their pitfalls and potentials, point to current choices made by the designers of LLM interfaces, make initial speculations about possible design opportunities, and suggest open research questions in the context of work. Finally, we propose a number of LLM agent personas that integrate different temporal characteristics into a coherent interaction model in a work-based collaboration.
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Published In
June 2024
297 pages
ISBN:9798400710179
DOI:10.1145/3663384
Copyright © 2024 Owner/Author.
This work is licensed under a Creative Commons Attribution-NoDerivatives International 4.0 License.
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Association for Computing Machinery
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Published: 25 June 2024
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CHIWORK 2024
CHIWORK 2024: Annual Symposium on Human-Computer Interaction for Work
June 25 - 27, 2024
Newcastle upon Tyne, United Kingdom
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