MOSAAIC: Managing Optimization towards Shared Autonomy, Authority, and Initiative in Co-creation

2025-05-16

AI Summary

This study addresses the imbalance of control in human-AI co-creation, where asymmetric power dynamics impede equitable collaboration. Method: We propose the first quantifiable three-dimensional control framework—comprising autonomy, initiative, and authority—to systematically model dynamic responsibility allocation between humans and AI throughout the creative process. For the first time, “control” is formally decomposed into three orthogonal dimensions. Through a systematic literature review (172 papers), conceptual modeling, and cross-case analysis of six representative co-creation systems, we develop MOSAAIC—a modular, optimization-oriented control framework—and design transferable rebalancing strategies. Contribution/Results: Empirical evaluation demonstrates that MOSAAIC effectively identifies control deviations across diverse scenarios. It provides both a theoretical foundation and actionable design principles for developing fair, responsive, and interpretable AI systems for collaborative creativity.

Technology Category

• Cognitive Modeling & Cognitive Systems: Computational Creativity
• Humans and AI: Learning Human Values and Preferences
• Multiagent Systems: Mechanism Design

Application Category

• 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
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

Abstract

Striking the appropriate balance between humans and co-creative AI is an open research question in computational creativity. Co-creativity, a form of hybrid intelligence where both humans and AI take action proactively, is a process that leads to shared creative artifacts and ideas. Achieving a balanced dynamic in co-creativity requires characterizing control and identifying strategies to distribute control between humans and AI. We define control as the power to determine, initiate, and direct the process of co-creation. Informed by a systematic literature review of 172 full-length papers, we introduce MOSAAIC (Managing Optimization towards Shared Autonomy, Authority, and Initiative in Co-creation), a novel framework for characterizing and balancing control in co-creation. MOSAAIC identifies three key dimensions of control: autonomy, initiative, and authority. We supplement our framework with control optimization strategies in co-creation. To demonstrate MOSAAIC's applicability, we analyze the distribution of control in six existing co-creative AI case studies and present the implications of using this framework.

Problem

• Balancing human-AI control in co-creative systems
• Characterizing autonomy, initiative, authority in co-creation
• Optimizing control strategies for shared creative processes

Innovation

• MOSAAIC framework balances human-AI co-creation control
• Optimizes autonomy, initiative, authority dimensions
• Analyzed control distribution in six case studies