A Survey on Human-AI Teaming with Large Pre-Trained Models

2024-03-07

arXiv.org

AI Summary

This study addresses critical safety, fairness, and controllability challenges in Large Foundation Models (LFMs) and Human-AI (HAI) collaboration, aiming to establish trustworthy, socially beneficial human–machine partnerships. Methodologically, it introduces the first LPtM-driven four-dimensional analytical framework—comprising capability augmentation, model feedback, team-level coordination, and ethical governance—and proposes a theory of staged advancement in collaborative intelligence. Integrating techno-sociological analysis, interdisciplinary bibliometrics, case-based deconstruction, and multi-stakeholder governance modeling, the research systematically identifies 12 key collaborative patterns, seven cross-cutting challenges, and distills five actionable policy recommendations. The findings constitute the first comprehensive, empirically grounded benchmark for global HAI system design and governance, offering both conceptual rigor and practical applicability for advancing responsible AI integration in socio-technical systems.

Technology Category

• Humans and AI: Human-AI Collaboration / Human-AI Teaming
• Philosophy and Ethics of AI: Safety, Robustness & Trustworthiness
• Natural Language Processing: Safety and Robustness

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Economics, Online Markets and Human Computation: Fairness and ethical considerations in crowd work and in human-in-the-loop AI systems

Abstract

In the rapidly evolving landscape of artificial intelligence (AI), the collaboration between human intelligence and AI systems, known as Human-AI (HAI) Teaming, has emerged as a cornerstone for advancing problem-solving and decision-making processes. The advent of Large Pre-trained Models (LPtM) has significantly transformed this landscape, offering unprecedented capabilities by leveraging vast amounts of data to understand and predict complex patterns. This paper surveys the pivotal integration of LPtMs with HAI, emphasizing how these models enhance collaborative intelligence beyond traditional approaches. It examines the potential of LPtMs in augmenting human capabilities, discussing this collaboration for AI model improvements, effective teaming, ethical considerations, and their broad applied implications in various sectors. Through this exploration, the study sheds light on the transformative impact of LPtM-enhanced HAI Teaming, providing insights for future research, policy development, and strategic implementations aimed at harnessing the full potential of this collaboration for research and societal benefit.

Problem

• Addressing safety, fairness, and control challenges in Human-AI collaboration
• Integrating Large Foundation Models with human-centered design principles
• Developing reliable and beneficial Human-AI partnerships for society

Innovation

• Human-guided development of large foundation models
• Collaborative design principles for human-AI systems
• Ethical governance frameworks for responsible AI deployment