Artificial Intelligence and Copyright, 59942-59949 [2023-18624]

Agencies

• Library of Congress
• Copyright Office

[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

[[Page 59944]]

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), 
https://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