[Federal Register Volume 88, Number 167 (Wednesday, August 30, 2023)]
[Notices]
[Pages 59942-59949]
From the Federal Register Online via the Government Publishing Office [www.gpo.gov]
[FR Doc No: 2023-18624]
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LIBRARY OF CONGRESS
Copyright Office
[Docket No. 2023-6]
Artificial Intelligence and Copyright
AGENCY: U.S. Copyright Office, Library of Congress.
ACTION: Notice of inquiry and request for comments.
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SUMMARY: The United States Copyright Office is undertaking a study of
the copyright law and policy issues raised by artificial intelligence
(``AI'') systems. To inform the Office's study and help assess whether
legislative or regulatory steps in this area are warranted, the Office
seeks comment on these issues, including those involved in the use of
copyrighted works to train AI models, the appropriate levels of
transparency and disclosure with respect to the use of copyrighted
works, and the legal status of AI-generated outputs.
DATES: Written comments are due no later than 11:59 p.m. Eastern Time
on Wednesday, October 18, 2023. Written reply comments are due no later
than 11:59 p.m. Eastern Time on Wednesday, November 15, 2023.
ADDRESSES: For reasons of governmental efficiency, the Copyright Office
is using the regulations.gov system for the submission and posting of
public comments in this proceeding. All comments should be submitted
electronically through regulations.gov. Specific instructions for
submitting comments are available on the Copyright Office website at
https://copyright.gov/policy/artificial-intelligence. If electronic
submission is not feasible, please contact the Office using the contact
information below for special instructions.
FOR FURTHER INFORMATION CONTACT: Rhea Efthimiadis, Assistant to the
General Counsel, by email at [email protected] or telephone at 202-
707-8350.
SUPPLEMENTARY INFORMATION:
I. Introduction
Over the last year, artificial intelligence (``AI'') systems and
the rapid growth of their capabilities have attracted significant media
and public attention. One type of AI, ``generative AI'' technology, is
capable of producing outputs such as text, images, video, or audio
(including emulating a human voice) that would be considered
copyrightable if created by a human author.\1\ The adoption and use of
[[Page 59943]]
generative AI systems by millions of Americans \2\--and the resulting
volume of AI-generated material--have sparked widespread public debate
about what these systems may mean for the future of creative industries
and raise significant questions for the copyright system.\3\
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\1\ Generative AI technologies produce outputs based on
``learning'' statistical patterns in existing data, which may
include copyrighted works. Kim Martineau, What is generative AI?,
IBM Research Blog (Apr. 20, 2023), https://research.ibm.com/blog/what-is-generative-AI (``At a high level, generative models encode a
simplified representation of their training data and draw from it to
create a new work that's similar, but not identical, to the original
data.''). The Office has defined ``generative AI'' and other key
terms in a glossary at the end of this Notice.
\2\ See, e,g., Microsoft FY23 Second Quarter Earnings Conference
Call Transcript, Microsoft (Jan. 24, 2023), https://www.microsoft.com/en-us/Investor/events/FY-2023/earnings-fy-2023-q2.aspx (Microsoft CEO Satya Nadella stating that ``[m]ore than one
million people have used Copilot to date''); Krystal Hu, ChatGPT
sets record for fastest-growing user base--analyst note, Reuters
(Feb. 2, 2023), https://www.reuters.com/technology/chatgpt-sets-record-fastest-growing-user-base-analyst-note-2023-02-01/.
\3\ See, e.g., James Vincent, The scary truth about AI copyright
is nobody knows what will happen next, The Verge (Nov. 15, 2022),
https://www.theverge.com/23444685/generative-ai-copyright-infringement-legal-fair-use-training-data (discussing the ``key
[legal] questions from which the topic's many uncertainties
unfold''); see Kevin Roose & Cade Metz, How to Become an Expert on
A.I., N.Y. Times (Apr. 4, 2023), https://www.nytimes.com/article/ai-artificial-intelligence-chatbot.html; Kim Martineau, What is
generative AI?, IBM Research Blog (Apr. 20, 2023), https://research.ibm.com/blog/what-is-generative-AI; Harvard Online, The
Benefits and Limitations of Generative AI: Harvard Experts Answer
Your Questions, Harvard Online Blog (Apr. 19, 2023), https://www.harvardonline.harvard.edu/blog/benefits-limitations-generative-ai; Arhan Islam, A History of Generative AI: From GAN to GPT-4,
Marktechpost (Mar. 21, 2023), https://www.marktechpost.com/2023/03/21/a-history-of-generative-ai-from-gan-to-gpt-4/. Generative AI is
also a point of contention in the labor disputes between the
Alliance of Motion Picture and Television Producers and both the
Writers Guild of America and SAG-AFTRA (the guild representing
actors and other media professionals). See Andrew Webster, Actors
say Hollywood studios want their AI replicas--for free, forever, The
Verge (July 13, 2023), https://www.theverge.com/2023/7/13/23794224/sag-aftra-actors-strike-ai-image-rights.
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Some of these questions relate to the scope and level of human
authorship, if any, in copyright claims for material produced in whole
or in part by generative AI. Over the past several years, the Office
has begun to receive applications to register works containing AI-
generated material, some of which name AI systems as an author or co-
author.\4\ At the same time, copyright owners have brought infringement
claims against AI companies based on the training process for, and
outputs derived from, generative AI systems.\5\ As concerns and
uncertainties mount, Congress and the Copyright Office have been
contacted by many stakeholders with diverse views. The Office has
publicly announced a broad initiative earlier this year to explore
these issues. This Notice is part of that initiative and builds on the
Office's research, expertise, and prior work, as well as information
that stakeholders have provided to the Office.
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\4\ See U.S. Copyright Office Review Board, Decision Affirming
Refusal of Registration of A Recent Entrance to Paradise at 2 (Feb.
14, 2022), https://www.copyright.gov/rulings-filings/review-board/docs/a-recent-entrance-to-paradise.pdf (noting visual work was
submitted listing the author as the ``Creativity Machine'').
\5\ See, e.g., Am. Compl. ]] 8, 61, Getty Images (US), Inc. v.
Stability AI, Inc., No. 1:23-cv-135, ECF No. 13 (D. Del. Mar. 29,
2023) (alleging infringement based on use of copyrighted images to
train a generative AI model and on the possibility of that model
generating images ``highly similar to and derivative of''
copyrighted images).
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II. The Copyright Office's Past Work on Machine Learning and AI
The Copyright Office has long been engaged in questions involving
machine learning and copyright. In 1965, the Office's annual report
noted that developments in computer technology had begun to raise
``difficult questions of authorship''--namely the question of the
authorship of works ```written' by computers.'' \6\ As the then-
Register of Copyrights observed:
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\6\ U.S. Copyright Office, Sixty-Eighth Annual Report of the
Register of Copyrights for the Fiscal Year Ending June 30, 1965, at
5 (1966), https://www.copyright.gov/reports/annual/archive/ar-1965.pdf.
The crucial question appears to be whether the ``work'' is
basically one of human authorship, with the computer merely being an
assisting instrument, or whether the traditional elements of
authorship in the work (literary, artistic, or musical expression or
elements of selection, arrangement, etc.) were actually conceived
and executed not by man but by a machine.\7\
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\7\ Id.
Because the answer depends on the circumstances of a work's
creation, the head of the Office's Examining Division (and future
Register) Barbara Ringer warned that the Office could not ``take the
categorical position that registration will be denied merely because a
computer may have been used in some manner in creating the work.'' \8\
As she noted, ``a typewriter is a machine that is used in the creation
of a manuscript[,] but this does not result in the manuscript being
uncopyrightable.'' \9\ This view was echoed a decade later by the
National Commission on New Technological Uses of Copyrighted Works
(``CONTU''),\10\ which agreed with the Office \11\ but declined to
discuss the issue in depth because ``[t]he development of this capacity
for `artificial intelligence' has not yet come to pass, and, indeed, it
has been suggested to this Commission that such a development is too
speculative to consider at this time.'' \12\ In the intervening years,
as AI moved out of the realm of speculation, the Office continued to
participate in discussions on AI issues, from a 1991 conference hosted
by the World Intellectual Property Organization (``WIPO'') \13\ to more
recent events the Office co-hosted with WIPO \14\ and with the U.S.
Patent and Trademark Office.\15\
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\8\ U.S. Copyright Office, Annual Report of the Examining
Division, Copyright Office, for the Fiscal Year 1965, at 4 (1965),
https://copyright.gov/reports/annual/archive/ar-examining1965.pdf.
\9\ Id.
\10\ CONTU was created ``to assist the President and Congress in
developing a national policy for both protecting the rights of
copyright owners and ensuring public access to copyrighted works
when they are used in computer and machine duplication systems.''
CONTU, Final Report of the National Commission on New Technological
Uses of Copyrighted Works at 3 (July 31, 1978) (``CONTU Final
Report'') One of its statutory mandates was to study ``the creation
of new works by the application or intervention of [ ] automatic
systems or machine reproduction.'' National Commission on New
Technological Uses of Copyrighted Works, Public Law 93-573, sec.
201(b)(2), 88 Stat. 1873 (1974).
\11\ CONTU Final Report at 44-46 (recommending the same
``approach [that] is followed by the Copyright Office today in
conducting examinations for determining registrability for copyright
of works created with the assistance of computers'').
\12\ Id. at 44.
\13\ See U.S. Copyright Office, 94th Annual Report of the
Register of Copyrights for the Fiscal Year Ending September 30,
1991, at 2 (1991), https://copyright.gov/reports/annual/archive/ar-1991.pdf.
\14\ See Copyright in the Age of Artificial Intelligence, U.S.
Copyright Office (Feb. 5, 2020), https://www.copyright.gov/events/artificial-intelligence/.
\15\ See Copyright law and machine learning for AI: Where are we
and where are we going?, U.S. Patent and Trademark Office (Oct. 26,
2021), https://www.uspto.gov/about-us/events/copyright-law-and-machine-learning-ai-where-are-we-and-where-are-we-going. The Office
also supported the U.S. Patent and Trademark Office when it
solicited public comments on the impact of AI on intellectual
property policy, including copyright. See U.S. Patent and Trademark
Office, Public Views on Artificial Intelligence and Intellectual
Property Policy (Oct. 2020), https://www.uspto.gov/sites/default/files/documents/USPTO_AI-Report_2020-10-07.pdf.
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Last year, in two separate copyright registration matters, the
Office publicly addressed the question of copyright in AI-generated
material. In the first instance, the Office refused to register a claim
for two-dimensional artwork described as ``autonomously created by a
computer algorithm running on a machine.'' \16\ The Office's Review
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Board \17\ explained that the work could not be registered because it
was made ``without any creative input or intervention from a human
author,'' and that ``statutory text, judicial precedent, and
longstanding Copyright Office practice'' all require human authorship
as a condition of copyrightability.\18\ The Office's registration
denial, as well as the supporting legal analysis, was recently affirmed
in federal district court.\19\
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\16\ U.S. Copyright Office Review Board, Decision Affirming
Refusal of Registration of A Recent Entrance to Paradise at 2 (Feb.
14, 2022), https://www.copyright.gov/rulings-filings/review-board/docs/a-recent-entrance-to-paradise.pdf.
\17\ The Review Board is a three-member body that hears
administrative appeals of copyright registration decisions. Review
Board decisions constitute final agency actions and are subject to
judicial review. See 37 CFR 202.5(f), (g).
\18\ U.S. Copyright Office Review Board, Decision Affirming
Refusal of Registration of A Recent Entrance to Paradise at 3 (Feb.
14, 2022), https://www.copyright.gov/rulings-filings/review-board/docs/a-recent-entrance-to-paradise.pdf.
\19\ Mem. Op., Thaler v. Perlmutter, No. 22-cv-1564, ECF No. 24
(D.D.C. Aug. 18, 2023).
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A second registration application, submitted in 2022, involved a
work containing both human authorship and generative AI material. The
work was a graphic novel with text written by the human applicant and
illustrations created through the use of Midjourney, a generative AI
system. After soliciting information from the applicant about the
process of the work's creation, the Office determined that copyright
protected both the human-authored text and human selection and
arrangement of the text and images, but not the AI-generated images
themselves.\20\ The Office explained that where a human author lacks
sufficient creative control over the AI-generated components of a work,
the human is not the ``author'' of those components for copyright
purposes.\21\ The Office continues to receive applications to register
works incorporating AI-generated material, involving different levels
of human contributions.\22\
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\20\ U.S. Copyright Office, Cancellation Decision re: Zarya of
the Dawn (VAu001480196) at 1 (Feb. 21, 2023), https://www.copyright.gov/docs/zarya-of-the-dawn.pdf (letter from the Office
to applicant canceling the original certificate and issuing a new
one covering only the expressive material created by the applicant).
\21\ Id. at 9.
\22\ In addition to registration, the Office has considered AI
in the regulatory context of the section 1201 rulemaking. Section
1201 of the Copyright Act sets up a triennial proceeding to address
possible exceptions to a statutory ban on circumventing
technological protection measures that control access to copyrighted
works. See 17 U.S.C. 1201(a)(1)(C) (charging Register of Copyrights
with making recommendation as to whether particular users of
copyrighted works are adversely affected in ability to engage in
noninfringing uses). In the most recent proceeding, the Register was
asked to consider text and data mining activities as part of this
analysis, and she concluded that existing copyright case law did not
support the conclusion that all such activity is fair use. The
Register did, however, recommend granting a narrow exemption after
concluding that the specific use as described was likely to be fair
because it was limited to a ``researcher or group of researchers
seeking to investigate a particular set of questions that require
examination of a large number of works;'' access to the works in
full would be limited to researchers solely for purposes of
verifying research results; and the researchers would not use the
works ``for their expressive purposes.'' U.S. Copyright Office,
Section 1201 Rulemaking: Eighth Triennial Proceeding to Determine
Exemptions to the Prohibition on Circumvention, Recommendation of
the Register of Copyrights 107-13 (Oct. 2021).
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III. The Office's AI Initiative
In response to growing Congressional \23\ and public interest,\24\
the Office launched a comprehensive AI Initiative in early 2023. The
Initiative identified a number of steps that the Office would take to
further explore the copyright policy questions surrounding AI,
including hosting public listening sessions and publishing a notice of
inquiry.\25\ At the same time, the Office created a website,
www.copyright.gov/ai, to provide information about the Initiative,
including planned events and opportunities for public engagement.
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\23\ See Letter from Sen. Chris Coons, Chair, and Sen. Thom
Tillis, Ranking Member, Subcomm. on Intell. Prop. of the S. Comm. on
the Judiciary, to Kathi Vidal, Under Secretary of Commerce for
Intell. Prop. and Director, U.S. Patent and Trademark Office, and
Shira Perlmutter, Register of Copyrights, U.S. Copyright Office
(Oct. 27, 2022) and Letter from Kathi Vidal, Under Secretary of
Commerce for Intell. Prop. and Director, U.S. Patent and Trademark
Office, and Shira Perlmutter, Register of Copyrights, to Sen. Chris
Coons, Chair, and Sen. Thom Tillis, Ranking Member, Subcomm. on
Intell. Prop. of the S. Comm. on the Judiciary (Dec. 12, 2022),
https://www.copyright.gov/laws/hearings/Letter-to-USPTO-USCO-on-National-Commission-on-AI-1.pdf (Senate letter requesting the Office
to provide guidance on what the law around generative AI should be
in the future and the Office's response explaining that it intended,
among other things, to issue a notice of inquiry on questions
involving copyright and AI).
\24\ See, e.g., Virtual AI Townhall hosted by Karla Ortiz
featuring the U.S. Copyright Office, Concept Art Ass'n (Nov. 2,
2022), https://www.conceptartassociation.com/calendar/virtual-ai-townhall-featuring-us-copyright-office (event that featured two
senior attorneys from the Office).
\25\ Copyright Office Launches New Artificial Intelligence
Initiative, U.S. Copyright Office (Mar. 16, 2023), https://www.copyright.gov/newsnet/2023/1004.html.
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a. March 2023 Registration Guidance
At the outset of the Initiative, the Office issued a statement of
policy providing registration guidance on works containing AI-generated
material (``AI Registration Guidance'').\26\ The AI Registration
Guidance reiterated the principle that copyright protection in the
United States requires human authorship. Under well-established case
law, the Guidance explained, ``the term `author,' used in both the
Constitution and the Copyright Act, excludes non-humans.'' \27\ In the
context of generative AI, this means that ``[i]f a work's traditional
elements of authorship were produced by a machine, the work lacks human
authorship and the Office will not register it.'' \28\ The Guidance
instructed applicants seeking to register works containing more than de
minimis AI-generated material to disclose that the work contains such
material and provide a brief explanation of the human author's
contributions.\29\
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\26\ Copyright Registration Guidance: Works Containing Materials
Generated by Artificial Intelligence, 88 FR 16190 (Mar. 16, 2023). A
copy of the guidance is available at https://copyright.gov/ai/ai_policy_guidance.pdf.
\27\ Id. at 16191.
\28\ Id. at 16192.
\29\ Id. at 16193.
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b. Public Listening Sessions
In April and May 2023, the Office held four public listening
sessions to gather input on the copyright issues raised by generative
AI. Each session focused on a different category of creative work:
literary works, including print journalism and software; works of
visual art; audiovisual works, including video games; and musical works
and sound recordings. Over the four listening sessions, nearly 90
participants representing individual artists, academic experts, legal
practitioners, technology companies, and industry associations shared
their views with the Office. Transcripts, videos recordings, and
agendas for each session are available on the Office's website.\30\
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\30\ Spring 2023 AI Listening Sessions, U.S. Copyright Office,
https://www.copyright.gov/ai/listening-sessions.html.
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c. Educational Webinars
In June and July 2023, the Office held two public webinars on
generative AI, each of which drew an audience of nearly 2,000. The
first webinar focused on registration of works containing AI-generated
material. It included an overview of the Office's general rules on how
to register works containing material created or owned by someone other
than the applicant, followed by examples illustrating how those rules
apply to works that incorporate AI-generated material.\31\ The second
webinar convened experts on different regions of the world to discuss
international developments in generative AI and copyright law. These
experts discussed how other countries are addressing copyright issues,
including authorship, training, and exceptions and limitations. They
provided an overview of legislative
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developments and highlighted possible areas of convergence and
divergence.\32\
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\31\ The transcript and recording of the registration webinar
are available at https://www.copyright.gov/events/ai-application-process/. In the coming months, the Office intends to provide
further guidance to copyright applicants seeking to register works
containing AI-generated material.
\32\ The transcript and recording of the international webinar
are available at https://www.copyright.gov/events/international-ai-copyright-webinar/.
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d. Engagement With Stakeholders
In addition to the public events described above, the Office has
spoken with a broad spectrum of stakeholders, participating in dozens
of meetings with academics, trade groups, individual creators,
technology companies, and creative industries.\33\ These meetings have
provided valuable information on the technical aspects of generative AI
models and systems, how creators are using generative AI, and the
continuing questions copyright applicants have about registering works
that include AI-generated material.
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\33\ Additionally, the Office has offered guidance to The
Mechanical Licensing Collective (``The MLC''), explaining that AI-
generated music is not eligible for the statutory mechanical blanket
license in section 115 of the Copyright Act and that The MLC should
not disburse royalties for such musical works. See Letter from
Suzanne V. Wilson, General Counsel and Associate Register of
Copyrights, U.S. Copyright Office, to Kris Ahrend, Chief Exec.
Officer, The MLC, at 2-3 (Apr. 20, 2023), https://www.copyright.gov/ai/USCO-Guidance-Letter-to-The-MLC-Letter-on-AI-Created-Works.pdf.
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IV. The Current Inquiry
Drawing on our prior AI Initiative work, including discussions with
stakeholders, the Office has identified a wide range of copyright
policy issues arising from the development and use of AI. These relate
to: (1) the use of copyrighted works to train AI models; (2) the
copyrightability of material generated using AI systems; (3) potential
liability for infringing works generated using AI systems; and (4) the
treatment of generative AI outputs that imitate the identity or style
of human artists. The Office seeks public comments on these and related
issues.
As to the first issue, the Office is aware that there is
disagreement about whether or when the use of copyrighted works to
develop datasets for training AI models (in both generative and non-
generative systems) is infringing.\34\ This Notice seeks information
about the collection and curation of AI datasets, how those datasets
are used to train AI models, the sources of materials ingested into
training, and whether permission by and/or compensation for copyright
owners is or should be required when their works are included. To the
extent that commenters believe such permission and/or compensation is
necessary, the Office seeks their views on what kind of remuneration
system(s) might be feasible and effective. The Office also seeks
information regarding the retention of records necessary to identify
underlying training materials and the availability of this information
to copyright owners and others.
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\34\ In some cases, a non-generative AI model may be trained on
copyrighted material. In other cases, the same AI model may be
capable of being deployed in both a generative AI system and a non-
generative one. The Office's consideration of training is framed
broadly in order to encompass these and other situations.
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On the second issue, the Office seeks comment on the proper scope
of copyright protection for material created using generative AI.
Although we believe the law is clear that copyright protection in the
United States is limited to works of human authorship,\35\ questions
remain about where and how to draw the line between human creation and
AI-generated content. For example, are there circumstances where a
human's use of a generative AI system could involve sufficient control
over the technology, such as through the selection of training
materials and multiple iterations of instructions (``prompts''), to
result in output that is human-authored? Resolution of this question
will affect future registration decisions. While the Office is
separately working to update its registration guidance on works that
include AI-generated material,\36\ this Notice explores the broader
policy questions related to copyrightability.
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\35\ See Mem. Op., Thaler v. Perlmutter, No. 22-cv-1564, ECF No.
24 (D.D.C. Aug. 18, 2023) (affirming the Office's registration
denial of AI-generated work).
\36\ For example, the Office has received questions about how to
apply its guidance that applicants disclose more than de minimis
amounts of AI-generated material in their works. See AI Registration
Guidance, 88 FR at 16193 (explaining that ``AI-generated content
that is more than de minimis should be explicitly excluded from the
application'').
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On the third question, the Office is interested in how copyright
liability principles could apply to material created by generative AI
systems.\37\ For example, if an output is found to be substantially
similar to a copyrighted work that was part of the training dataset,
and the use does not qualify as fair, how should liability be
apportioned between the user whose instructions prompted the output and
developers of the system and dataset?
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\37\ Some of these questions are currently before the courts in
lawsuits that have already been filed over generative AI systems.
See, e.g., J.L. v. Alphabet Inc., 3:23-cv-03340 (N.D. Cal.); Kadrey
v. Meta Platforms, Inc., 3:23-cv-3417 (N.D. Cal.); Silverman v.
OpenAI, Inc., 4:23-cv-3416 (N.D. Cal.); Tremblay v. OpenAI, Inc.,
3:23-cv-3223 (N.D. Cal.); Getty Images (US), Inc. v. Stability AI,
Inc., 1:23-cv-0135 (D. Del.); Andersen v. Stability AI Ltd., 3:23-
cv-0201 (N.D. Cal.); Doe v. GitHub, Inc., 4:22-cv-6823 (N.D. Cal.).
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Lastly, in both our listening sessions and other outreach, the
Office heard from artists and performers concerned about generative AI
systems' ability to mimic their voices, likenesses, or styles. Although
these personal attributes are not generally protected by copyright law,
their copying may implicate varying state rights of publicity and
unfair competition law, as well as have relevance to various
international treaty obligations.\38\
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\38\ See U.S. Copyright Office, Authors, Attribution, and
Integrity: Examining Moral Rights in the United States 112-116 (Apr.
2019), https://www.copyright.gov/policy/moralrights/full-report.pdf
(discussing how such interests are generally protected under state
right of publicity laws).
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V. Overview of Notice
The purpose of this Notice is to collect factual information and
views relevant to the copyright law and policy issues raised by recent
advances in generative AI. The Office undertakes this study pursuant to
its statutory mandate in title 17 to ``[c]onduct studies'' and
``[a]dvise Congress on national and international issues relating to
copyright, other matters arising under this title, and related
matters.'' \39\ It intends to use this information to advise Congress
by providing analyses of the current state of the law, identifying
unresolved issues, and evaluating potential areas for congressional
action. The Office will also use this record to inform its regulatory
work and to offer information and resources to the public, courts, and
other government entities considering these issues.
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\39\ 17 U.S.C. 701(b)(1), (b)(4).
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The questions are grouped into several categories. This Notice
begins with several general high-level questions and then inquires
about AI training, including questions of transparency and
accountability; generative AI outputs, including questions of
copyrightability, infringement, and labeling or identification of such
outputs; and other issues related to copyright. Because of the
importance of using shared language in discussing these issues, the
questions are followed by a glossary of key terms for the purposes of
this Notice. The Office welcomes input from commenters on the
definitions.
VI. Instructions and Questions
The Office does not expect that every party choosing to respond to
this Notice will address every question raised below. The questions are
designed to gather views from a broad range of parties. The Office does
request that, when responding to a question, commenters clearly
identify each
[[Page 59946]]
question for which they submit a response, address questions
separately, and provide the factual, legal, or policy basis for their
responses. Commenters should make clear whether they are submitting in
a personal capacity or on behalf of an organization or entity they are
authorized to represent. Commenters are particularly encouraged to
explain any technical understandings that inform their legal and policy
viewpoints, as well as whether their answers are applicable only to
certain industries, technologies, or types of copyrighted works.
Although some questions seek technical information about generative AI
systems, commenters do not need to be affiliated with a technical
entity to answer these questions.
General Questions
The Office has several general questions about generative AI in
addition to the specific topics listed below. Commenters are encouraged
to raise any positions or views that are not elicited by the more
detailed questions further below.
1. As described above, generative AI systems have the ability to
produce material that would be copyrightable if it were created by a
human author. What are your views on the potential benefits and risks
of this technology? How is the use of this technology currently
affecting or likely to affect creators, copyright owners, technology
developers, researchers, and the public?
2. Does the increasing use or distribution of AI-generated material
raise any unique issues for your sector or industry as compared to
other copyright stakeholders?
3. Please identify any papers or studies that you believe are
relevant to this Notice. These may address, for example, the economic
effects of generative AI on the creative industries or how different
licensing regimes do or could operate to remunerate copyright owners
and/or creators for the use of their works in training AI models. The
Office requests that commenters provide a hyperlink to the identified
papers.
4. Are there any statutory or regulatory approaches that have been
adopted or are under consideration in other countries that relate to
copyright and AI that should be considered or avoided in the United
States? \40\ How important a factor is international consistency in
this area across borders?
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\40\ For example, several jurisdictions have adopted copyright
exceptions for text and data mining that could permit use of
copyrighted material to train AI systems. Separately, the European
Parliament passed its version of the Artificial Intelligence Act on
June 14, 2023, which includes a requirement that providers of
generative AI systems publish ``a sufficiently detailed summary of
the use of training data protected under copyright law.'' See
Artificial Intelligence Act, amend. 399, art. 28b(4)(c), EUR. PARL.
DOC. P9_TA (2023)0236 (2023), https://www.europarl.europa.eu/doceo/document/TA-9-2023-0236_EN.html.
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5. Is new legislation warranted to address copyright or related
issues with generative AI? If so, what should it entail? Specific
proposals and legislative text are not necessary, but the Office
welcomes any proposals or text for review.
Training
If your comment applies only to a specific subset of AI
technologies, please make that clear.
6. What kinds of copyright-protected training materials are used to
train AI models, and how are those materials collected and curated?
6.1. How or where do developers of AI models acquire the materials
or datasets that their models are trained on? To what extent is
training material first collected by third-party entities (such as
academic researchers or private companies)?
6.2. To what extent are copyrighted works licensed from copyright
owners for use as training materials? To your knowledge, what licensing
models are currently being offered and used?
6.3. To what extent is non-copyrighted material (such as public
domain works) used for AI training? Alternatively, to what extent is
training material created or commissioned by developers of AI models?
6.4. Are some or all training materials retained by developers of
AI models after training is complete, and for what purpose(s)? Please
describe any relevant storage and retention practices.
7. To the extent that it informs your views, please briefly
describe your personal knowledge of the process by which AI models are
trained. The Office is particularly interested in:
7.1. How are training materials used and/or reproduced when
training an AI model? Please include your understanding of the nature
and duration of any reproduction of works that occur during the
training process, as well as your views on the extent to which these
activities implicate the exclusive rights of copyright owners.
7.2. How are inferences gained from the training process stored or
represented within an AI model?
7.3. Is it possible for an AI model to ``unlearn'' inferences it
gained from training on a particular piece of training material? If so,
is it economically feasible? In addition to retraining a model, are
there other ways to ``unlearn'' inferences from training?
7.4. Absent access to the underlying dataset, is it possible to
identify whether an AI model was trained on a particular piece of
training material?
8. Under what circumstances would the unauthorized use of
copyrighted works to train AI models constitute fair use? Please
discuss any case law you believe relevant to this question.
8.1. In light of the Supreme Court's recent decisions in Google v.
Oracle America \41\ and Andy Warhol Foundation v. Goldsmith,\42\ how
should the ``purpose and character'' of the use of copyrighted works to
train an AI model be evaluated? What is the relevant use to be
analyzed? Do different stages of training, such as pre-training and
fine-tuning,\43\ raise different considerations under the first fair
use factor?
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\41\ 141 S. Ct. 1183 (2021).
\42\ 143 S. Ct. 1258 (2023).
\43\ See Pre-training, Fine-tuning, and Foundation Models,
GenLaw: Glossary (June 1, 2023), https://genlaw.github.io/glossary.html (explaining that pre-training is a relatively slow and
expensive process that ``results in a general-purpose or foundation
model'' whereas fine-tuning ``adapts a pretrained model checkpoint
to perform a desired task using additional data'').
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8.2. How should the analysis apply to entities that collect and
distribute copyrighted material for training but may not themselves
engage in the training?
8.3. The use of copyrighted materials in a training dataset or to
train generative AI models may be done for noncommercial or research
purposes.\44\ How should the fair use analysis apply if AI models or
datasets are later adapted for use of a commercial nature? \45\ Does it
make a difference if funding for these noncommercial or research uses
is provided by for-profit developers of AI systems?
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\44\ For example, the generative AI model, Stable Diffusion, was
reportedly developed in part by researchers at the Ludwig Maximilian
University of Munich but is used by the for-profit company Stability
AI. See Kenrick Cai, Startup Behind AI Image Generator Stable
Diffusion Is In Talks To Raise At A Valuation Up To $1 Billion,
Forbes (Sept. 7, 2022), https://www.forbes.com/sites/kenrickcai/2022/09/07/stability-ai-funding-round-1-billion-valuation-stable-diffusion-text-to-image/?sh=31e11f5a24d6.
\45\ 17 U.S.C. 107(1).
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8.4. What quantity of training materials do developers of
generative AI models use for training? Does the volume of material used
to train an AI model affect the fair use analysis? If so, how?
8.5. Under the fourth factor of the fair use analysis, how should
the effect on the potential market for or value of a copyrighted work
used to train an AI
[[Page 59947]]
model be measured? \46\ Should the inquiry be whether the outputs of
the AI system incorporating the model compete with a particular
copyrighted work, the body of works of the same author, or the market
for that general class of works?
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\46\ Id. at 107(4).
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9. Should copyright owners have to affirmatively consent (opt in)
to the use of their works for training materials, or should they be
provided with the means to object (opt out)?
9.1. Should consent of the copyright owner be required for all uses
of copyrighted works to train AI models or only commercial uses? \47\
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\47\ For example, the European Union's Directive on Copyright in
the Digital Single Market provides for two copyright exceptions or
limitations for text and data mining (which may be used in the
training of generative AI systems): one for purposes of scientific
research and one for any other purpose. The latter is available only
to the extent that rightsholders have not expressly reserved their
rights to the use of their works in text and data mining. See
Directive 2019/790 of the European Parliament and of the Council of
17 April 2019 on copyright and related rights in the Digital Single
Market and amending Directives 96/9/EC and 2001/29/EC, 2019 O.J. (L
130), https://eur-lex.europa.eu/eli/dir/2019/790/oj.
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9.2. If an ``opt out'' approach were adopted, how would that
process work for a copyright owner who objected to the use of their
works for training? Are there technical tools that might facilitate
this process, such as a technical flag or metadata indicating that an
automated service should not collect and store a work for AI training
uses? \48\
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\48\ For example, some AI companies have reportedly started to
allow copyright owners to tag their works as not available for AI
training. See Emilia David, Now you can block OpenAI's web crawler,
The Verge (Aug. 7, 2023), https://www.theverge.com/2023/8/7/23823046/openai-data-scrape-block-ai; Melissa Heikkil[auml], Artists
can now opt out of the next version of Stable Diffusion, MIT Tech.
Review (Dec. 16, 2022), https://www.technologyreview.com/2022/12/16/1065247/artists-can-now-opt-out-of-the-next-version-of-stable-diffusion/.
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9.3. What legal, technical, or practical obstacles are there to
establishing or using such a process? Given the volume of works used in
training, is it feasible to get consent in advance from copyright
owners?
9.4. If an objection is not honored, what remedies should be
available? Are existing remedies for infringement appropriate or should
there be a separate cause of action?
9.5. In cases where the human creator does not own the copyright--
for example, because they have assigned it or because the work was made
for hire--should they have a right to object to an AI model being
trained on their work? If so, how would such a system work?
10. If copyright owners' consent is required to train generative AI
models, how can or should licenses be obtained?
10.1. Is direct voluntary licensing feasible in some or all
creative sectors?
10.2. Is a voluntary collective licensing scheme a feasible or
desirable approach? \49\ Are there existing collective management
organizations that are well-suited to provide those licenses, and are
there legal or other impediments that would prevent those organizations
from performing this role? Should Congress consider statutory or other
changes, such as an antitrust exception, to facilitate negotiation of
collective licenses?
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\49\ Collective licensing is one alternative to a direct
licensing regime, in which copyright owners negotiate and enter into
private agreements on an individual basis. Under a collective
licensing arrangement, rights are aggregated and administered by a
management organization. The management organization negotiates the
terms of use and distributes payment to participating copyright
owners. See WIPO, WIPO Good Practice Toolkit for CMOs at 6 (2021),
https://www.wipo.int/publications/en/details.jsp?id=4561.
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10.3. Should Congress consider establishing a compulsory licensing
regime? \50\ If so, what should such a regime look like? What
activities should the license cover, what works would be subject to the
license, and would copyright owners have the ability to opt out? How
should royalty rates and terms be set, allocated, reported and
distributed?
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\50\ A compulsory or ``statutory'' license allows for certain
uses of a copyrighted work ``without the consent of the copyright
owner provided that the person adhered to the provisions of the
license, most notably paying a statutorily established royalty to
the copyright owner.'' Music Licensing Reform: Hearing Before the
Subcomm. on Intell. Prop. of the S. Comm. on the Judiciary, 109th
Cong. (2005) (statement of Marybeth Peters, Register of Copyrights),
http://copyright.gov/docs/regstat071205.html.
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10.4. Is an extended collective licensing scheme \51\ a feasible or
desirable approach?
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\51\ ``An Extended Collective Licensing scheme is one where a
relevant licensing body, subject to certain safeguards, is
authori[z]ed to license specified copyright works on behalf of all
rights holders in its sector (including non-members), and not just
members who have given specific permission for it to act.'' Extended
Collective Licensing (ECL) scheme definition, LexisNexis Glossary
(2023), https://www.lexisnexis.co.uk/legal/glossary/extended-collective-licensing-ecl-scheme; see also Letter from Karyn A.
Temple, Acting Register of Copyrights, U.S. Copyright Office, to
Rep. Robert Goodlatte, Chair, and Rep. John Conyers, Ranking Member,
H. Comm. on the Judiciary (Sept. 29, 2017), https://www.copyright.gov/policy/massdigitization/house-letter.pdf; Letter
from Karyn A. Temple, Acting Register of Copyrights, U.S. Copyright
Office, to Sen. Charles Grassley, Chair, and Sen. Dianne Feinstein,
Ranking Member, S. Comm. on the Judiciary (Sept. 29, 2017), https://www.copyright.gov/policy/massdigitization/senate-letter.pdf.
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10.5. Should licensing regimes vary based on the type of work at
issue?
11. What legal, technical or practical issues might there be with
respect to obtaining appropriate licenses for training? Who, if anyone,
should be responsible for securing them (for example when the curator
of a training dataset, the developer who trains an AI model, and the
company employing that model in an AI system are different entities and
may have different commercial or noncommercial roles)?
12. Is it possible or feasible to identify the degree to which a
particular work contributes to a particular output from a generative AI
system? Please explain.
13. What would be the economic impacts of a licensing requirement
on the development and adoption of generative AI systems?
14. Please describe any other factors you believe are relevant with
respect to potential copyright liability for training AI models.
Transparency & Recordkeeping
15. In order to allow copyright owners to determine whether their
works have been used, should developers of AI models be required to
collect, retain, and disclose records regarding the materials used to
train their models? Should creators of training datasets have a similar
obligation?
15.1. What level of specificity should be required?
15.2. To whom should disclosures be made?
15.3. What obligations, if any, should be placed on developers of
AI systems that incorporate models from third parties?
15.4. What would be the cost or other impact of such a
recordkeeping system for developers of AI models or systems, creators,
consumers, or other relevant parties?
16. What obligations, if any, should there be to notify copyright
owners that their works have been used to train an AI model?
17. Outside of copyright law, are there existing U.S. laws that
could require developers of AI models or systems to retain or disclose
records about the materials they used for training?
Generative AI Outputs
If your comment applies only to a particular subset of generative
AI technologies, please make that clear.
Copyrightability
18. Under copyright law, are there circumstances when a human using
a generative AI system should be considered the ``author'' of material
produced by the system? If so, what factors are relevant to that
determination? For example, is selecting what material an AI model is
trained on and/or providing an iterative series of
[[Page 59948]]
text commands or prompts sufficient to claim authorship of the
resulting output?
19. Are any revisions to the Copyright Act necessary to clarify the
human authorship requirement or to provide additional standards to
determine when content including AI-generated material is subject to
copyright protection?
20. Is legal protection for AI-generated material desirable as a
policy matter? Is legal protection for AI-generated material necessary
to encourage development of generative AI technologies and systems?
Does existing copyright protection for computer code that operates a
generative AI system provide sufficient incentives?
20.1. If you believe protection is desirable, should it be a form
of copyright or a separate sui generis right? If the latter, in what
respects should protection for AI-generated material differ from
copyright?
21. Does the Copyright Clause in the U.S. Constitution permit
copyright protection for AI-generated material? Would such protection
``promote the progress of science and useful arts''? \52\ If so, how?
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\52\ U.S. Const. art. I, sec. 8, cl. 8.
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Infringement
22. Can AI-generated outputs implicate the exclusive rights of
preexisting copyrighted works, such as the right of reproduction or the
derivative work right? If so, in what circumstances?
23. Is the substantial similarity test adequate to address claims
of infringement based on outputs from a generative AI system, or is
some other standard appropriate or necessary?
24. How can copyright owners prove the element of copying (such as
by demonstrating access to a copyrighted work) if the developer of the
AI model does not maintain or make available records of what training
material it used? Are existing civil discovery rules sufficient to
address this situation?
25. If AI-generated material is found to infringe a copyrighted
work, who should be directly or secondarily liable--the developer of a
generative AI model, the developer of the system incorporating that
model, end users of the system, or other parties?
25.1. Do ``open-source'' AI models raise unique considerations with
respect to infringement based on their outputs? \53\
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\53\ Some AI models are released by their developers for
download and use by members of the general public. Such so-called
``open-source'' models may restrict how those models can be used
through the terms of a licensing agreement. See, e.g., Llama 2
Community License Agreement, Meta AI (July 18, 2023), https://ai.meta.com/llama/license/ (requiring users of Llama 2 AI model to
include an attribution notice and excluding use in services with
greater than 700 million monthly active users).
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26. If a generative AI system is trained on copyrighted works
containing copyright management information, how does 17 U.S.C. 1202(b)
apply to the treatment of that information in outputs of the system?
27. Please describe any other issues that you believe policymakers
should consider with respect to potential copyright liability based on
AI-generated output.
Labeling or Identification
28. Should the law require AI-generated material to be labeled or
otherwise publicly identified as being generated by AI? If so, in what
context should the requirement apply and how should it work?
28.1. Who should be responsible for identifying a work as AI-
generated?
28.2. Are there technical or practical barriers to labeling or
identification requirements?
28.3. If a notification or labeling requirement is adopted, what
should be the consequences of the failure to label a particular work or
the removal of a label?
29. What tools exist or are in development to identify AI-generated
material, including by standard-setting bodies? How accurate are these
tools? What are their limitations?
Additional Questions About Issues Related to Copyright
30. What legal rights, if any, currently apply to AI-generated
material that features the name or likeness, including vocal likeness,
of a particular person?
31. Should Congress establish a new federal right, similar to state
law rights of publicity, that would apply to AI-generated material? If
so, should it preempt state laws or set a ceiling or floor for state
law protections? What should be the contours of such a right?
32. Are there or should there be protections against an AI system
generating outputs that imitate the artistic style of a human creator
(such as an AI system producing visual works ``in the style of'' a
specific artist)? Who should be eligible for such protection? What form
should it take?
33. With respect to sound recordings, how does section 114(b) of
the Copyright Act relate to state law, such as state right of publicity
laws? \54\ Does this issue require legislative attention in the context
of generative AI?
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\54\ Under 17 U.S.C. 114(b), the reproduction and derivative
work rights for sound recordings ``do not extend to the making or
duplication of another sound recording that consists entirely of an
independent fixation of other sounds, even though such sounds
imitate or simulate those in the copyrighted sound recording.''
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34. Please identify any issues not mentioned above that the
Copyright Office should consider in conducting this study.
VII. Glossary of Key Terms
The Office has included definitions of key terms as they are used
in this Notice to clarify the technical processes involved in
generative AI systems. The following definitions are used for purposes
of this Notice only; they do not necessarily reflect the government's
legal position with respect to any particular term.
Artificial Intelligence (AI): A general classification of automated
systems designed to perform tasks typically associated with human
intelligence or cognitive functions.\55\ Generally, AI technologies may
use different techniques to accomplish such tasks. This Notice uses the
term ``AI'' in a more limited sense to refer to technologies that
employ machine learning, a technique further defined below.
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\55\ See John S. McCain National Defense Authorization Act for
Fiscal Year 2019, Public Law 115-232, sec. 238(g)(2), 132 Stat.
1636, 1697-98 (2018) (defining ``artificial intelligence'' to
include systems ``developed in computer software, physical hardware,
or other context that solves tasks requiring human-like perception,
cognition, planning, learning, communication, or physical action'').
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AI Model: A combination of computer code and numerical values (or
``weights,'' which is defined below) that is designed to accomplish a
specified task. For example, an AI model may be designed to predict the
next word or word fragment in a body of text. Examples of AI models
include GPT-4, Stable Diffusion, and LLaMA.
AI System: A software product or service that substantially
incorporates one or more AI models and is designed for use by an end-
user.\56\ An AI system may be created by a developer of an AI model, or
it may incorporate one or more AI models developed by third parties.
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\56\ See James M. Inhofe National Defense Authorization Act for
Fiscal Year 2023, Public Law 117-263, sec. 7223(4)(A), 136 Stat.
2395, 3669 (2022) (defining ``artificial intelligence system'' as
``any data system, software, application, tool, or utility that
operates in whole or in part using dynamic or static machine
learning algorithms or other forms of artificial intelligence'').
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Generative AI: An application of AI used to generate outputs in the
form of expressive material such as text, images, audio, or video.
Generative AI systems may take commands or instructions
[[Page 59949]]
from a human user, which are sometimes called ``prompts.'' Examples of
generative AI systems include Midjourney, OpenAI's ChatGPT, and
Google's Bard.
Machine Learning: A technique for building AI systems that is
characterized by the ability to automatically learn and improve on the
basis of data or experience, without relying on explicitly programmed
rules.\57\ Machine learning involves ingesting and analyzing materials
such as quantitative data or text and obtain inferences about qualities
of those materials and using those inferences to accomplish a specific
task. These inferences are represented within an AI model's weights.
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\57\ See National Artificial Intelligence Initiative Act of
2020, 15 U.S.C. 9401(11).
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Training Datasets: A collection of training material (as defined
below) that is compiled and curated for use in machine learning.
Examples of training datasets include BookCorpus, ImageNet, and LAION.
Training Material: Individual units of material that are used for
purposes of training an AI model. They may include a combination of
text, images, audio, or other categories of expressive material, as
well as annotations describing the material. An example of training
material would be an individual image and an associated text ``label''
that describes the image.
Weights: A collection of numerical values that define the behavior
of an AI model. Weights are stored within an AI model and reflect
inferences from the training process.
Dated: August 24, 2023.
Suzanne V. Wilson,
General Counsel and Associate Register of Copyrights.
Maria Strong,
Associate Register of Copyrights and Director of Policy and
International Affairs.
[FR Doc. 2023-18624 Filed 8-29-23; 8:45 am]
BILLING CODE 1410-30-P