HAISP: A Dataset of Human-AI Songwriting Processes From the AI Song Contest
Year: 2024
Type: article
Abstract: The advent of accessible artificial intelligence (AI) tools and systems has begun a new era for creative expression, challenging us to gain a better understanding of human-AI collaboration and creativity. In this paper, we introduce Human–AI Songwriting Processes Dataset (HAISP), consisting of 34 coded submissions from the 2023 AI Song Contest. This dataset offers a resource for exploring the complex dynamics of AI-supported songwriting processes, facilitating investigations into the possibilities and challenges posed by AI in creative endeavors. Overall, HAISP is anticipated to contribute to advancing understanding of human-AI co-creation from the users' perspective. We suggest potential use cases for the dataset, including examining AI tools used in songwriting and exploring users' ethical considerations and creative approaches. This could help inform academic research and practical applications in music composition and related fields.
Authors: Lidia J. Morris, Rebecca Leger, Michele Newman, John Ashley Burgoyne, Ryan Groves, Natasha Mangal, Jin Ha Lee
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W6949328348
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