@inproceedings{0b7c618394d4477a880228698a2bd0a1,
title = "Teacher, Trainer, Counsel, Spy: How Generative AI can Bridge or Widen the Gaps in Worker-Centric Digital Phenotyping of Wellbeing",
abstract = "The increasing integration of computing technologies in the workplace has also seen the conceptualization and development of data-driven and algorithmic tools that aim to improve workers' wellbeing and performance. However, both research and practice have revealed several gaps in the effectiveness and deployment of these tools. Meanwhile, the recent advances in generative AI have highlighted the tremendous capabilities of large language models (LLMs) in processing large volumes of data in producing human-interactive natural language content. This paper explores the opportunities for LLMs in facilitating worker-centered design for Wellbeing Assessment Tools (WATs). In particular, we map features of LLMs against known challenges of WAT. We highlight how the LLMs can bridge or even widen the gaps in worker-centeric WAT. This paper aims to inspire new research directions focused on empowering workers and anticipating harms in integrating LLMs with workplace technologies.",
keywords = "LLMs, generative AI, large language models, worker performance, worker wellbeing, workplace",
author = "{Das Swain}, Vedant and Koustuv Saha",
note = "Publisher Copyright: {\textcopyright} 2024 Owner/Author.; 3rd Annual Meeting of the Symposium on Human-Computer Interaction for Work, CHIWORK 2024 ; Conference date: 25-06-2024 Through 27-06-2024",
year = "2024",
month = jun,
day = "25",
doi = "10.1145/3663384.3663401",
language = "English (US)",
series = "ACM International Conference Proceeding Series",
publisher = "Association for Computing Machinery",
booktitle = "CHIWORK 2024 - Proceedings of the 3rd Annual Meeting of the Symposium on Human-Computer Interaction for Work",
address = "United States",
}