Human and AI teamwork shows four main collaboration styles
Human-AI Collaboration: From Paradoxes to Patterns
Artificial IntelligenceHuman-Computer InteractionMultiagent Systems
Summary
Working together, humans and AI systems can often do better than either alone. This paper looks closely at how they share control and take action during their teamwork. It finds that there are four common ways they collaborate: giving instructions, delegating tasks, providing help, and creating together. The authors also use a special way of thinking called paradox analysis to understand the tricky tensions inside each way of working. This helps explain why these styles work and how to design better human-AI collaboration.
What this means in practice
- •For software product managers: Design product features that support clear collaboration styles between users and AI to improve overall user effectiveness.
- •For customer support teams: Structure workflows to optimize human and AI roles for tasks like instruction or assistance to boost service quality and response time.
A position paper. It proposes an approach and reports no results.
Authors
Michael Weiss
Abstract
Evidence shows that humans and AI systems perform better together, by collaborating, than alone. This paper examines two key design dimensions of human-AI collaboration (autonomy and initiative) and explores the collaboration patterns that they generate. Documenting these patterns starts with identifying the underlying problems and solutions, followed by examining the internal tensions within the problems. The paper uses a paradox perspective to analyze those tensions. It describes a process for surfacing the tensions and mapping the underlying paradoxes. It also illustrates how the pattern descriptions can be derived from mapping these paradoxes. Finally, the paper documents four human-AI collaboration patterns: Instruction, Delegation, Assistance, and Co-creation.