Human-AI Co-Creation

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16 papers in the last four weeks, up 220% on the four weeks before. 0.2% of all new papers.

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Measuring Human Contribution in AI-Assisted Content Generation
Aug 27, 2024Yueqi Xie, Tao Qi, Jingwei Yi +7Human-AI Co-Creation
Princeton University · Tsinghua University · University of Science and Technology of China +4
Generative AI Assisted Workflows in Architectural Conceptual Design: Performance, Creative Self-Efficacy, and Cognitive Load
Date pendingYao Xiao, Han Jiang, Rachel Hurley +1Generative AI in Education Human-AI Co-Creation
Department of Civil, Environmental, and Architectural Engineering, Worcester Polytechnic Institute, Worcester, MA, US
Tinker Tales: A Tangible Dialogue System for Child-AI Co-Creative Storytelling
Date pendingNayoung Choi, Jiseung Hong, Peace Cyebukayire +2Interactive Storytelling Conversational Agents
Department of Computer Science, Emory University · Department of Computer Science, Carnegie Mellon University · School of Nursing, Emory University
"What Are You Really Trying to Do?": Co-Creating Life Goals from Everyday Computer Use
Date pendingShardul Sapkota, Matthew Jörke, Zane Sabbagh +3User Modeling Human-AI Co-Creation
Stanford University Stanford, USA

Aug 27, 2024, cs.CYJ/K move · Enter open · S save

Measuring Human Contribution in AI-Assisted Content Generation

Yueqi Xie, Tao Qi, Jingwei Yi, Xiyuan Yang+6

Princeton University · Tsinghua University · University of Science and Technology of China+4

Human-AI Co-Creation

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With the growing prevalence of generative artificial intelligence (AI), an increasing amount of content is no longer exclusively generated by humans but by generative AI models with human guidance. This shift presents notable challenges for the delineation of originality due to the varying degrees of human contribution in AI-assisted works. This study raises the research question of measuring human contribution in AI-assisted content generation and introduces a framework to address this question that is grounded in information theory. By calculating mutual information between human input and AI-assisted output relative to self-information of AI-assisted output, we quantify the proportional information contribution of humans in content generation. Our experimental results demonstrate that the proposed measure effectively discriminates between varying degrees of human contribution across multiple creative domains. We hope that this work lays a foundation for measuring human contributions in AI-assisted content generation in the era of generative AI.

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Latest papers 94

Measuring Human Contribution in AI-Assisted Content Generation

Generative AI Assisted Workflows in Architectural Conceptual Design: Performance, Creative Self-Efficacy, and Cognitive Load

Tinker Tales: A Tangible Dialogue System for Child-AI Co-Creative Storytelling

"What Are You Really Trying to Do?": Co-Creating Life Goals from Everyday Computer Use