Analysis of Copyright Implementation in Getty Images and Stability AI as a Case Study of Generative AI

Al intelligence has introduced complex challenges to copyright law, as demonstrated by the high-profile dispute between Getty Images and Stability AI. Getty Images, a leading stock photography provider, alleges that Stability AI unlawfully used millions of its copyrighted images to train the Stable Diffusion model without authorization or compensation. This case has become a focal point in the debate over whether the use of copyrighted material for AI training constitutes fair use or infringement. The controversy also raises broader questions about derivative works, the appearance of watermarks in generated outputs, and the potential misuse of trademarks. From the perspective of creative industries, the case underscores the risk of uncompensated exploitation of intellectual property, while for technology developers it highlights the need for large datasets to sustain innovation. Different jurisdictions adopt contrasting approaches, with the United States relying on fair use analysis and the European Union emphasizing data mining exceptions and opt-out rights. The outcome of this case will shape the future balance between protecting creators and fostering technological progress, potentially leading to new licensing frameworks for AI training. Ultimately, this dispute illustrates the urgent need for legal adaptation in respon se to emerging generative technologies.

Authors

Publication Details

Journal
International Journal of Social Science and Human Research
Published
2025-09-25
DOI
https://doi.org/10.47191/ijsshr/v8-i9-49
Primary Topic
Law, AI, and Intellectual Property
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Analysis of Copyright Implementation in Getty Images and Stability AI as a Case Study of Generative AI

Budi Santoso, Alifia Lintang Kanaya
International Journal of Social Science and Human Research
Law, AI, and Intellectual Property
article

Analysis of Copyright Implementation in Getty Images and Stability AI as a Case Study of Generative AI

Budi Santoso, Alifia Lintang Kanaya
article en

Abstract

Al intelligence has introduced complex challenges to copyright law, as demonstrated by the high-profile dispute between Getty Images and Stability AI. Getty Images, a leading stock photography provider, alleges that Stability AI unlawfully used millions of its copyrighted images to train the Stable Diffusion model without authorization or compensation. This case has become a focal point in the debate over whether the use of copyrighted material for AI training constitutes fair use or infringement. The controversy also raises broader questions about derivative works, the appearance of watermarks in generated outputs, and the potential misuse of trademarks. From the perspective of creative industries, the case underscores the risk of uncompensated exploitation of intellectual property, while for technology developers it highlights the need for large datasets to sustain innovation. Different jurisdictions adopt contrasting approaches, with the United States relying on fair use analysis and the European Union emphasizing data mining exceptions and opt-out rights. The outcome of this case will shape the future balance between protecting creators and fostering technological progress, potentially leading to new licensing frameworks for AI training. Ultimately, this dispute illustrates the urgent need for legal adaptation in respon se to emerging generative technologies.

International Journal of Social Science and Human ResearchVol. 08(09)
Openalex Percentile: Top 86%
Law, AI, and Intellectual Property
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.