Date of Award
2024
Document Type
Thesis (Master's)
Department or Program
Computer Science
First Advisor
Lorie Loeb
Second Advisor
Tricia Treacy
Third Advisor
Tim Tregubov
Abstract
Over the last decade, generative artificial intelligence models have advanced significantly and provided the public with several tools to create new works of art. However, the true authorship of these works has been debated due to their training on web-scraped data. Serving as an analogy to these larger models, Poster, Performed is an interactive artificial intelligence exhibition project that uses image assets submitted by the public to create poster compositions with custom image processing algorithms. During the course of a four-day exhibition, visitors were asked to identify the exhibition’s primary artist from five options: (1) participants who submitted image assets, (2) the programmer, (3) the artificial intelligence software, (4) the exhibition’s design team, and (5) the printers that output the posters. Survey data revealed that the participants who submitted image assets and the exhibition’s project team were the project’s most salient artists, each tied for the most responses. Within the analogy to state-of-the-art models, this finding implies that artworks produced with these generative tools would be best credited to the users who prompted the works and the original authors of the content used for model training.
Recommended Citation
Kasai, Wylie Z., "Poster, Performed: Understanding Public Opinions of Authorship in Generative Artificial Intelligence Models via Analogy" (2024). Dartmouth College Master’s Theses. 123.
https://digitalcommons.dartmouth.edu/masters_theses/123
Included in
Artificial Intelligence and Robotics Commons, Graphics and Human Computer Interfaces Commons, Interactive Arts Commons, Interdisciplinary Arts and Media Commons, Printmaking Commons
