A Paradigm for Creative Ownership

Paper Title

A Paradigm for Creative Ownership

Publication Info

• Topic area: Creative ownership in human-AI collaboration and HCI research.
• Keywords: Creative ownership, human-AI collaboration, generative AI, HCI, framework, creativity, psychological ownership, system design, co-creation, attribution.

Background and Problem

• Problem / challenge: Current studies on creative ownership lack a consistent definition, often conflating it with adjacent concepts or leaving it undefined, making findings difficult to compare across studies.
• Significance: As generative AI becomes integral to creative processes, understanding and supporting creative ownership is critical for fostering meaningful human-AI collaboration and ensuring creators feel connected to their work.
• Motivation and related work: Prior research has explored psychological ownership, creativity support, and attribution in HCI, but these efforts either lack a focus on creative ownership or fail to provide a unified framework. This paper addresses the gap by proposing a structured framework tailored to creative contexts.

Solution

• Proposed approach: A nine-subdimension framework for creative ownership, organized into three categories: Person, Process, and System, along with an interactive web-based visualization tool.
• Novelty:
  1. Introduction of a comprehensive framework for creative ownership with nine subdimensions.
  2. Development of an interactive web tool for eliciting, comparing, and visualizing ownership across projects.
  3. Empirical validation of the framework through interviews with 21 creative professionals.
• Procedure and key techniques:
  • Conducted a narrative literature review across psychology, philosophy, and HCI to derive the framework.
  • Organized the framework into three categories: Person (Embodiment, Occupancy, Recognition), Process (Control, Intentionality, Effort), and System (Production, Abstraction, Interdependence).
  • Validated the framework through semi-structured interviews with creative professionals, comparing their ownership perceptions before and after using the framework.

Results

• Concrete findings:
  • High-ownership projects scored consistently higher across all nine subdimensions compared to low-ownership projects.
  • Low-ownership projects exhibited greater variance, reflecting diverse pathways to diminished ownership.
  • Participants reported that the framework broadened their understanding of ownership and helped articulate previously tacit experiences.
• Advantage over baselines:
  • The framework captures a broader range of ownership dimensions compared to existing tools like the Creativity Support Index, which focuses on creativity but not ownership.
  • It introduces underexplored dimensions such as Occupancy and Production, expanding the analytic space for ownership research.
• Experiments / evaluation:
  • Semi-structured interviews with 21 creative professionals from diverse fields.
  • Participants rated high- and low-ownership projects using the framework and reflected on its relevance.
  • Quantitative analysis showed clear differentiation between high- and low-ownership cases across all dimensions.
• Limitations and future work:
  • The framework is not exhaustive, and alternative taxonomies could emphasize different aspects.
  • Participants selected their own projects, introducing potential biases.
  • Future work could explore interactions between dimensions, conduct within-domain studies, and evaluate the framework in AI-supported creative contexts.

Summary

This paper introduces a nine-subdimension framework for creative ownership, validated through interviews with 21 creative professionals. The framework organizes ownership into Person, Process, and System dimensions, offering a comprehensive vocabulary for understanding and designing for ownership in creative contexts. Empirical findings demonstrate its ability to differentiate high- and low-ownership projects and broaden participants' reflections on ownership. The framework has practical implications for HCI research, system design, and attribution studies, enabling researchers and designers to intentionally support ownership in human-AI collaboration. Future work will explore dimension interactions, domain-specific applications, and longitudinal impacts of AI tools on creative ownership.