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Poster, Performed: Understanding Pub...

Kasai, Wylie Z.

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Poster, Performed: Understanding Public Opinions of Authorship in Generative Artificial Intelligence Models via Analogy.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Poster, Performed: Understanding Public Opinions of Authorship in Generative Artificial Intelligence Models via Analogy./
作者:	Kasai, Wylie Z.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2024,
面頁冊數:	61 p.
附註:	Source: Masters Abstracts International, Volume: 85-12.
Contained By:	Masters Abstracts International85-12.
標題:	Java. -
電子資源:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=31182160
ISBN:	9798383034385

Poster, Performed: Understanding Public Opinions of Authorship in Generative Artificial Intelligence Models via Analogy.
Kasai, Wylie Z.

Poster, Performed: Understanding Public Opinions of Authorship in Generative Artificial Intelligence Models via Analogy. - Ann Arbor : ProQuest Dissertations & Theses, 2024 - 61 p.

Source: Masters Abstracts International, Volume: 85-12.

Thesis (M.S.)--Dartmouth College, 2024.

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, Performedis 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.

ISBN: 9798383034385Subjects--Topical Terms:

517732
Java.

Poster, Performed: Understanding Public Opinions of Authorship in Generative Artificial Intelligence Models via Analogy.
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