Trust and Reliance in Evolving Human-AI Workflows (TREW)
Authors: 
Zahra Ashktorab, Gagan Bansal, Zana Buçinca, Kenneth Holstein, Jessica Hullman, Alison Marie Smith-Renner, Tongshuang Wu, Wenjuan ZhangAuthors Info & Claims
Zahra Ashktorab, Gagan Bansal, Zana Buçinca, Kenneth Holstein, Jessica Hullman, Alison Marie Smith-Renner, Tongshuang Wu, Wenjuan ZhangAuthors Info & ClaimsArticle No.: 496, Pages 1 - 6
Abstract
State-of-the-art AIs, including Large Language Models (LLMs) like GPT-4, now possess capabilities once unique to humans, such as coding, idea generation, and planning. Advanced AIs are now integrated into a plethora of platforms and tools, including GitHub Copilot, Bing Chat, Bard, ChatGPT, and Advanced Data Analytics. In contrast to conventional, specialized AIs that typically offer singular solutions, these LLMs redefine human-AI dynamics, with a growing trend toward humans viewing them as collaborative counterparts. This shift leads to enhanced dialogues, negotiations, and task delegation between humans and AI. With these rapid advancements, the nature of human roles in the AI collaboration spectrum is evolving. While our previous workshops CHI TRAIT 2022 and 2023 delved into the trust and reliance concerning traditional AIs, the pressing question now is: how should we measure trust and reliance with these emerging AI technologies? As these systems witness widespread adoption, there’s also a need to assess their impact on human skill development. Does AI assistance amplify human skill progression, or does it inadvertently inhibit it? Considering the multifaceted challenges and solutions that revolve around human-AI interactions, we invite experts from diverse fields, including HCI, AI, ML, psychology, and social science. Our aim is to bridge communication gaps and facilitate rich collaborations across these domains.
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- Trust and Reliance in Evolving Human-AI Workflows (TREW)
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May 2024
4761 pages
ISBN:9798400703317
DOI:10.1145/3613905
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Published: 11 May 2024
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