Reimagining Copyright: Analyzing Intellectual Property Rights in Generative AI

Generative Artificial Intelligence (Generative AI) is completely turning the workforce upside down. This can be mainly attributed to the efficiency it brings to the organisation and educational institutions. With rapid digital developments observed across the globe, Generative AI is currently shaping the way people work. Mundane and repetitive tasks are now handled by AI applications such as Chat GPT which is helping organisations to cut costs directly. However, it is also important to understand the impact of Generative AI on the creative economy sector. Idea-expression dichotomy and the substantial similarity test for infringement are the two major aspects of the Copyright legal doctrines that are challenged by AI-generated content. The question which is raised is regarding the ownership of the training data set used in the Generative AI model. This will decide the degree of ownership of the content produced using the Generative AI model. The training data set is essentially used to draw comparisons with its existing information to create new content based on the human input received. In theory, copyright law does not put a restriction on ideas but more on how it is expressed. Due to this, legal battles are fought by many content creators with AI companies on the ownership of the content produced. The Generative AI models have the training data in the form of a mathematical model which helps to recreate any picture, sound or text based on the human input received. So, it is very important to understand the current landscape of AIgenerated content and its implications on Intellectual Property Rights.

Authors

Institutions

Publication Details

Journal
Journal of Information and Knowledge
Published
2024-12-03
DOI
https://doi.org/10.17821/srels/2024/v61i6/171646
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

Reimagining Copyright: Analyzing Intellectual Property Rights in Generative AI

Patil Vivek
Journal of Information and Knowledge
Law, AI, and Intellectual Property
article

Reimagining Copyright: Analyzing Intellectual Property Rights in Generative AI

Patil Vivek
article en

Abstract

Generative Artificial Intelligence (Generative AI) is completely turning the workforce upside down. This can be mainly attributed to the efficiency it brings to the organisation and educational institutions. With rapid digital developments observed across the globe, Generative AI is currently shaping the way people work. Mundane and repetitive tasks are now handled by AI applications such as Chat GPT which is helping organisations to cut costs directly. However, it is also important to understand the impact of Generative AI on the creative economy sector. Idea-expression dichotomy and the substantial similarity test for infringement are the two major aspects of the Copyright legal doctrines that are challenged by AI-generated content. The question which is raised is regarding the ownership of the training data set used in the Generative AI model. This will decide the degree of ownership of the content produced using the Generative AI model. The training data set is essentially used to draw comparisons with its existing information to create new content based on the human input received. In theory, copyright law does not put a restriction on ideas but more on how it is expressed. Due to this, legal battles are fought by many content creators with AI companies on the ownership of the content produced. The Generative AI models have the training data in the form of a mathematical model which helps to recreate any picture, sound or text based on the human input received. So, it is very important to understand the current landscape of AIgenerated content and its implications on Intellectual Property Rights.

Journal of Information and Knowledge
IMS Unison University (IN)
Peace, Justice and strong institutions
Openalex Percentile: Top 80%
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.