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

Alayt Issak, Jeba Rezwana, Casper Harteveld

TL;DR

The paper tackles the challenge of balancing human and AI control in co-creative systems by introducing MOSAAIC, a 3D framework (Autonomy, Initiative, Authority) derived from a systematic literature review of 172 papers. It defines precise control dimensions, presents two balancing strategies (AI-controlled adaptation and human-controlled configuration), and validates the framework through six diverse co-creative case studies. The findings reveal prevalent patterns of shared autonomy in most systems but also highlight cases with strong human-led control and the need for dynamic authority shifts. MOSAAIC offers a general, domain-agnostic tool for analyzing, benchmarking, and guiding the design of balanced co-creative AI, with practical implications for future GenAI-enabled workflows.

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.

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

TL;DR

The paper tackles the challenge of balancing human and AI control in co-creative systems by introducing MOSAAIC, a 3D framework (Autonomy, Initiative, Authority) derived from a systematic literature review of 172 papers. It defines precise control dimensions, presents two balancing strategies (AI-controlled adaptation and human-controlled configuration), and validates the framework through six diverse co-creative case studies. The findings reveal prevalent patterns of shared autonomy in most systems but also highlight cases with strong human-led control and the need for dynamic authority shifts. MOSAAIC offers a general, domain-agnostic tool for analyzing, benchmarking, and guiding the design of balanced co-creative AI, with practical implications for future GenAI-enabled workflows.

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.
Paper Structure (24 sections, 5 figures, 1 table)

This paper contains 24 sections, 5 figures, 1 table.

Table of Contents

  1. Introduction
  2. Background
  3. Frameworks: In Search of Control
  4. Roles: Defining Control
  5. Methodology for Systematic Literature Review
  6. Identifying Keywords
  7. Data Collection
  8. Data Analysis
  9. Framework for Managing Optimization towards Shared Autonomy, Authority, and Initiative in Co-creation (MOSAAIC)
  10. Three Dimensions of Control
  11. Autonomy
  12. Initiative
  13. Authority
  14. Strategies for Balancing Control
  15. AI-Controlled Adaption
  16. ...and 9 more sections

Figures (5)

  • Figure 1: Identified key phrases for Bucket 1 and 2
  • Figure 2: Three dimensions of control in co-creativity
  • Figure 3: Literature review procedure
  • Figure 4: 2D plane of autonomy and initiative from the 3D model of MOSAAIC
  • Figure 5: Strategies for balancing control in co-creativity