59942 Federal Register / Vol. 88, No. 167 / Wednesday, August 30, 2023 / Notices 

1 Generative AI technologies produce outputs 

based on ‘‘learning’’ statistical patterns in existing 

Labor, 200 Constitution Ave. NW, Room 

S–3323, Washington, DC 20210. 

• Electronic submission: You may 

submit comments and attachments 

electronically at http://

www.regulations.gov. Follow the online 

instructions for submitting comments. 

FOR FURTHER INFORMATION CONTACT: 

Anjanette Suggs, Office of Workers’ 

Compensation Programs, Division of 

Federal Employees Longshore, and 

Harbor Workers’ Compensation, OWCP/ 

DFELHWC, at suggs.anjanette@dol.gov 

(email); (202) 354–9660. 

SUPPLEMENTARY INFORMATION: 

I. Background 

The Office of Workers’ Compensation 

Programs (OWCP) is the agency 

responsible for administration of the 

Longshore and Harbor Workers’ 

Compensation Act (LHWCA), and the 

Federal Employees’ Compensation Act 

(FECA). 33 U.S.C. 939 (LHWCA) and 5 

U.S.C. 8104 and 8111 (FECA) authorizes 

OWCP to pay for approved vocational 

rehabilitation services to eligible 

workers with work-related disabilities. 

In order to decide whether to approve 

a rehabilitation plan, OWCP must 

receive a copy of the plan, supporting 

vocational testing materials and the 

estimated cost to implement the plan, 

broken down to show the fees, supplies, 

tuition and worker maintenance 

payments that are contemplated. OWCP 

also must receive the signatures of the 

worker and the rehabilitation counselor 

to show that the worker agrees to follow 

the proposed plan, and that the 

proposed plan is appropriate. Form 

OWCP–16 is the standard format for the 

collection of this information. The 

regulations implementing these statutes 

allow for the collection of information 

needed for OWCP to determine if a 

rehabilitation plan should be approved 

and payment of any related expenses 

should be authorized. (LHWCA, 702.506 

and 702.507, (FECA, 20 CFR 10.518, 

10.519) 

II. Desired Focus of Comments 

OWCP is soliciting comments 

concerning the proposed information 

collection (ICR) titled, ‘‘Rehabilitation 

Plan and Award’’, OWCP–16. OWCP/ 

DFELHWC is particularly interested in 

comments that: 

• Evaluate whether the collection of 

information is necessary for the proper 

performance of the functions of the 

Agency, including whether the 

information has practical utility; 

• Evaluate the accuracy of OWCP/ 

DFELHWC’s estimate of the burden 

related to the information collection, 

including the validity of the 

methodology and assumptions used in 

the estimate; 

• Suggest methods to enhance the 

quality, utility, and clarity of the 

information to be collected; and 

• Minimize the burden of the 

information collection on those who are 

to respond, including through the use of 

appropriate automated, electronic, 

mechanical, or other technological 

collection techniques or other forms of 

information technology, e.g., permitting 

electronic submission of responses. 

Background documents related to this 

information collection request are 

available at https://regulations.gov and 

at DOL–OWCP/DFELHWC located at 

200 Constitution Avenue NW, Room S– 

3323, Washington, DC 20210. Questions 

about the information collection 

requirements may be directed to the 

person listed in the FOR FURTHER

INFORMATION section of this notice. 

III. Current Actions 

This information collection request 

concerns the Rehabilitation Plan and 

Award, OWCP–16. OWCP/DFELHWC 

has updated the data with respect to the 

number of respondents, responses, 

burden hours, and burden costs 

supporting this information collection 

request from the previous information 

collection request. 

Type of Review: Extension, without 

change, of a currently approved 

collection. 

Agency: Office of Workers’ 

Compensation Programs, Division of 

Federal Employees’ Longshore, and 

Harbor Workers’ Compensation, OWCP/ 

DFELHWC. 

OMB Number: 1240–0045. 

Affected Public: Not-for-profit 

institutions, Businesses or other forprofits. 

Number of Respondents: 3,413. 

Frequency: On occasion. 

Number of Responses: 3,413. 

Annual Burden Hours: 1,707 hours. 

Total Respondent or Recordkeeper 

Cost: $0. 

OWCP Form 16, Rehabilitation Plan 

and Award. 

Comments submitted in response to 

this notice will be summarized in the 

request for Office of Management and 

Budget approval of the proposed 

information collection request; they will 

become a matter of public record and 

will be available at https://

www.reginfo.gov. 

Anjanette Suggs, 

Certifying Officer. 

[FR Doc. 2023–18668 Filed 8–29–23; 8:45 am] 

BILLING CODE 4510–CH–P 

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. 

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/artificialintelligence. 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 meft@

copyright.gov 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 


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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. 

2See, 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 fastestgrowing user base—analyst note, Reuters (Feb. 2, 

2023), https://www.reuters.com/technology/chatgptsets-record-fastest-growing-user-base-analyst-note2023-02-01/. 

3See, 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-aicopyright-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-intelligencechatbot.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-ofgenerative-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. 

4See 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/reviewboard/docs/a-recent-entrance-to-paradise.pdf 

(noting visual work was submitted listing the 

author as the ‘‘Creativity Machine’’). 

5See, 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). 

6U.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/ar1965.pdf. 

7 Id. 

8U.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. 

10CONTU 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). 

11CONTU 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. 

13See 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. 

14See Copyright in the Age of Artificial 

Intelligence, U.S. Copyright Office (Feb. 5, 2020), 

https://www.copyright.gov/events/artificialintelligence/. 

15See 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-andmachine-learning-ai-where-are-we-and-where-arewe-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. 

16U.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. 

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

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. 

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: 

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

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

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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17The 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). 

18U.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. 

19Mem. Op., Thaler v. Perlmutter, No. 22–cv– 

1564, ECF No. 24 (D.D.C. Aug. 18, 2023). 

20U.S. Copyright Office, Cancellation Decision re: 

Zarya of the Dawn (VAu001480196) at 1 (Feb. 21, 

2023), https://www.copyright.gov/docs/zarya-of-thedawn.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). 

23See 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-NationalCommission-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). 

24See, 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/virtualai-townhall-featuring-us-copyright-office (event that 

featured two senior attorneys from the Office). 

25Copyright Office Launches New Artificial 

Intelligence Initiative, U.S. Copyright Office (Mar. 

16, 2023), https://www.copyright.gov/newsnet/ 

2023/1004.html. 

26Copyright 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. 

30Spring 2023 AI Listening Sessions, U.S. 

Copyright Office, https://www.copyright.gov/ai/ 

listening-sessions.html. 

31The 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. 

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

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 AIgenerated 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

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. 

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 nonhumans.’’ 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

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

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 AIgenerated 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 AIgenerated 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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32The transcript and recording of the 

international webinar are available at https://

www.copyright.gov/events/international-aicopyright-webinar/. 

33Additionally, 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-toThe-MLC-Letter-on-AI-Created-Works.pdf. 

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 nongenerative one. The Office’s consideration of 

training is framed broadly in order to encompass 

these and other situations. 

35See 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 AIgenerated work). 

36For example, the Office has received questions 

about how to apply its guidance that applicants 

disclose more than de minimis amounts of AIgenerated 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’’). 

37Some 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.). 

38See 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/fullreport.pdf (discussing how such interests are 

generally protected under state right of publicity 

laws). 

39 17 U.S.C. 701(b)(1), (b)(4). 

developments and highlighted possible 

areas of convergence and divergence.32

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 AIgenerated material. 

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 nongenerative 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. 

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 AIgenerated material,36 this Notice 

explores the broader policy questions 

related to copyrightability. 

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? 

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

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. 

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 


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40For 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. 

41 141 S. Ct. 1183 (2021). 

42 143 S. Ct. 1258 (2023). 

43See Pre-training, Fine-tuning, and Foundation 

Models, GenLaw: Glossary (June 1, 2023), https://

genlaw.github.io/glossary.html (explaining that pretraining 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’’). 

44For 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). 

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? 

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 noncopyrighted 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? 

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? 

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 


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46 Id. at 107(4). 47For 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. 

48For 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/openaidata-scrape-block-ai; Melissa Heikkila ̈, 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-ofstable-diffusion/. 

49Collective 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. 

50A 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. 

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/houseletter.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/senateletter.pdf. 

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? 

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

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

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? 

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? 

10.4. Is an extended collective 

licensing scheme 51 a feasible or 

desirable approach? 

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 


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52U.S. Const. art. I, sec. 8, cl. 8. 

53Some 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). 

54Under 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.’’ 

55See 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’’). 

56See 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’’). 

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? 

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

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 AIgenerated output. 

Labeling or Identification 

28. Should the law require AIgenerated 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 AIgenerated 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? 

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. 

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. 

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 


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57See National Artificial Intelligence Initiative 

Act of 2020, 15 U.S.C. 9401(11). 

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. 

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 

NUCLEAR REGULATORY 

COMMISSION 

[NRC–2022–0212] 

Information Collection: NRC Form 5, 

Occupational Dose Record for a 

Monitoring Period 

AGENCY: Nuclear Regulatory 

Commission. 

ACTION: Renewal of existing information 

collection; request for comment. 

SUMMARY: The U.S. Nuclear Regulatory 

Commission (NRC) invites public 

comment on the renewal of Office of 

Management and Budget (OMB) 

approval for an existing collection of 

information. The information collection 

is entitled, ‘‘NRC Form 5, Occupational 

Dose Record for a Monitoring Period.’’ 

DATES: Submit comments by October 30, 

2023. Comments received after this date 

will be considered if it is practical to do 

so, but the Commission is able to ensure 

consideration only for comments 

received on or before this date. 

ADDRESSES: You may submit comments 

by any of the following methods; 

however, the NRC encourages electronic 

comment submission through the 

Federal rulemaking website: 

• Federal Rulemaking Website: Go to 

https://www.regulations.gov and search 

for Docket ID NRC–2022–0212. Address 

questions about Docket IDs in 

Regulations.gov to Stacy Schumann; 

telephone: 301–415–0624; email: 

Stacy.Schumann@nrc.gov. For technical 

questions, contact the individual(s) 

listed in the FOR FURTHER INFORMATION

CONTACT section of this document. 

• Mail comments to: David C. 

Cullison, Office of the Chief Information 

Officer, Mail Stop: T–6 A10M, U.S. 

Nuclear Regulatory Commission, 

Washington, DC 20555–0001. 

For additional direction on obtaining 

information and submitting comments, 

see ‘‘Obtaining Information and 

Submitting Comments’’ in the 

SUPPLEMENTARY INFORMATION section of 

this document. 

FOR FURTHER INFORMATION CONTACT: 

David C. Cullison, Office of the Chief 

Information Officer, U.S. Nuclear 

Regulatory Commission, Washington, 

DC 20555–0001; telephone: 301–415– 

2084; email: Infocollects.Resource@

nrc.gov. 

SUPPLEMENTARY INFORMATION: 

I. Obtaining Information and 

Submitting Comments 

A. Obtaining Information 

Please refer to Docket ID NRC–2022– 

0212 when contacting the NRC about 

the availability of information for this 

action. You may obtain publicly 

available information related to this 

action by any of the following methods: 

• Federal Rulemaking Website: Go to 

https://www.regulations.gov and search 

for Docket ID NRC–2022–0212. 

• NRC’s Agencywide Documents 

Access and Management System 

(ADAMS): You may obtain publicly 

available documents online in the 

ADAMS Public Documents collection at 

https://www.nrc.gov/reading-rm/ 

adams.html. To begin the search, select 

‘‘Begin Web-based ADAMS Search.’’ For 

problems with ADAMS, please contact 

the NRC’s Public Document Room (PDR) 

reference staff at 1–800–397–4209, 301– 

415–4737, or by email to 

PDR.Resource@nrc.gov. The supporting 

statement and NRC Form 5 are available 

in ADAMS under Accession Nos. 

ML23082A250 and ML23082A254. 

• NRC’s PDR: The PDR, where you 

may examine and order copies of 

publicly available documents, is open 

by appointment. To make an 

appointment to visit the PDR, please 

send an email to PDR.Resource@nrc.gov 

or call 1–800–397–4209 or 301–415– 

4737, between 8 a.m. and 4 p.m. eastern 

time (ET), Monday through Friday, 

except Federal holidays. 

• NRC’s Clearance Officer: A copy of 

the collection of information and related 

instructions may be obtained without 

charge by contacting the NRC’s 

Clearance Officer, David C. Cullison, 

Office of the Chief Information Officer, 

U.S. Nuclear Regulatory Commission, 

Washington, DC 20555–0001; telephone: 

301–415–2084; email: 

Infocollects.Resource@nrc.gov. 

B. Submitting Comments 

The NRC encourages electronic 

comment submission through the 

Federal rulemaking website (https://

www.regulations.gov). Please include 

Docket ID NRC–2022–0212, in your 

comment submission. 

The NRC cautions you not to include 

identifying or contact information in 

comment submissions that you do not 

want to be publicly disclosed in your 

comment submission. All comment 

submissions are posted at https://

www.regulations.gov and entered into 

ADAMS. Comment submissions are not 

routinely edited to remove identifying 

or contact information. 

If you are requesting or aggregating 

comments from other persons for 

submission to the NRC, then you should 

inform those persons not to include 

identifying or contact information that 

they do not want to be publicly 

disclosed in their comment submission. 

Your request should state that comment 

submissions are not routinely edited to 

remove such information before making 

the comment submissions available to 

the public or entering the comment into 

ADAMS. 

II. Background 

In accordance with the Paperwork 

Reduction Act of 1995 (44 U.S.C. 

Chapter 35), the NRC is requesting 

public comment on its intention to 

request the OMB’s approval for the 


lotter on DSK11XQN23PROD with NOTICES1