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Cited by 44Law, AI, and Intellectual PropertyIntellectual Property Rights and MediaDigitalization, Law, and Regulation

The DOI matches its DataCite record.

Artificial Intelligence’s Fair Use Crisis

Open access
Sobel, Benjamin L. W.·2017·Columbia University
DOI 10.7916/d8ww90t7
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Abstract

As automation supplants more forms of labor, creative expression still seems like a distinctly human enterprise. This may someday change: by ingesting works of authorship as “training data,” computer programs can teach themselves to write natural prose, compose music, and generate movies. Machine learning is an artificial intelligence (“AI”) technology with immense potential and a commensurate appetite for copyrighted works. In the United States, the copyright law mechanism most likely to facilitate machine learning’s uses of protected data is the fair use doctrine. However, current fair use doctrine threatens either to derail the progress of machine learning or to disenfranchise the human creators whose work makes it possible. This Article addresses the problem in three Parts: using popular machine learning datasets and research as case studies, Part I describes how programs “learn” from corpora of copyrighted works and catalogs the legal risks of this practice. It concludes that fair use may not protect expressive machine learning applications, including the burgeoning field of natural language generation. Part II explains that applying today’s fair use doctrine to expressive machine learning will yield one of two undesirable outcomes: if U.S. courts reject the fair use defense for machine learning, valuable innovation may move to another jurisdiction or halt entirely; alternatively, if courts find the technology to be fair use, sophisticated software may divert rightful ea…

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Metadata

Authors
Sobel, B. L. W.
Affiliations
Harvard University (US)
Corresponding author
Sobel, Benjamin L. W.
ORCID
Sobel, Benjamin L. W.
Venue
Columbia University
Publisher
Columbia University
Published
2017
ISSN
1544-4848 (ISSN-L)
DOI
10.7916/d8ww90t7
OpenAlex
W2756393761
Cited by
44
References
0
Citations by year
2025: 10 · 2024: 15 · 2023: 5 · 2022: 5 · 2021: 3 · 2020: 4 · 2019: 1 · 2018: 1
FWCI
0.98
Type
journal article
Language
en
Open access
Locations
  • The Columbia Journal of Law & the Arts
  • Columbia Academic Commons (Columbia University)
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › Law, AI, and Intellectual Property
  • Social Sciences › Law › Intellectual Property Rights and Media
  • Social Sciences › Law › Digitalization, Law, and Regulation
Keywords
fair use, machine learning, copyright, training data, natural language generation, AI-generated works, copyright infringement, authorship, creative expression

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