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A Survey on Human-AI Collaboration with Large Foundation Models

Created by
  • Haebom

Author

Vanshika Vats, Marzia Binta Nizam, Minghao Liu, Ziyuan Wang, Richard Ho, Mohnish Sai Prasad, Vincent Titterton, Sai Venkat Malreddy, Riya Aggarwal, Yanwen Xu, Lei Ding, Jay Mehta, Nathan Grinnell, Li Liu, Sijia Zhong, Devanathan Nallur Gandamani, Xinyi Tang, Rohan Ghosalkar, Celeste Shen, Rachel Shen, Nafisa Hussain, Kesav Ravichandran, James Davis

Outline

This paper explores the growing importance of human-AI (HAI) collaboration in problem-solving and decision-making processes, driven by the rapid advancement of artificial intelligence (AI) and the emergence of large-scale foundational models (LFMs). While LFMs' ability to understand and predict complex patterns through the massive data available has significantly expanded the potential of HAI collaboration, addressing ongoing challenges related to safety, fairness, and control is also crucial. This paper analyzes the integration of LFMs and HAI across four dimensions: human-driven model development, collaborative design principles, ethics and governance frameworks, and applications in high-risk domains, highlighting both opportunities and risks. It emphasizes that successful HAI systems are not automatically built with robust models alone, but rather are the product of careful human-centered design. It aims to provide insights into current and future research aimed at transforming the potential of LFMs into societally beneficial and trustworthy partnerships.

Takeaways, Limitations

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Takeaways:
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It highlights the potential and importance of HAI collaboration using LFMs.
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It highlights the importance of human-centered design and suggests directions for building a successful HAI system.
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It emphasizes the need to consider ethical and governance aspects such as safety, fairness, and control.
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It suggests research directions to utilize the potential of LFMs for the benefit of society.
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Limitations:
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Details on the design and implementation of a specific HAI system are lacking.
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It is possible that other important aspects were not considered beyond the four analytical frameworks presented.
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It does not specifically address the safety, fairness, and control issues of LFMs.
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The presentation of future research directions is rather general, and in-depth discussions of specific research topics are lacking.
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