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Artificial Intelligence and the Copyright Dilemma

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ABSTRACT
Authorship of copyrightable works has been a hotly
contested issue in the American legal system for over 200
years. With the recent boom of artificial intelligence, more
and more creative works have been the result of non-human
authors. Computer algorithms and learning machines have
become a new source of creativity. The U.S. Copyright
Office, however, has been slow to acknowledge the
significance of AI in the creative process by denying
copyrights of non-human works and releasing them into the
public domain. This paper addresses the issue of IP
ownership of AI generated works. It argues that giving
authorship to AI programmers and owners is essential to the
future development of the AI industry. The paper proposes
that instead of redefining “authorship” to include non-
humans, it is simply necessary to reinterpret the terms
“employee” and “employer” in the made for hire doctrine of
the U.S. Copyright Act. This reinterpretation would allow
the current IP system to continue promoting “the progress of
science and useful arts” without a lengthy or controversial
overhaul of the rules and guidelines currently set in place.
CONTENTS
Abstract ........................................................................... 431
I.Introduction ............................................................. 433
A.The Social Impact of AI ...................................... 433
B.AI as a Tool of the Human Author ..................... 435
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C.AI as an Independent Actor in the Creative Process
436
II.The Issue of AI Authorship ..................................... 437
A.Current Stance of the U.S. Copyright Office ...... 437
B.Disadvantages of the Current Stance .................. 438
III.Methodology ....................................................... 439
IV.Findings .............................................................. 440
A.Non-humans as Authors ...................................... 440
B.Human Authorship Solution ............................... 442
C.Human Authors: Programmers; Owners; End Users
443
1.The Goal of Human Authorship ..................... 444
2.End Users and Authorship .............................. 444
3.How to Incentivize the Contribution of
Developers .............................................................. 445
D.Reinterpreting the Made for Hire Doctrine’s
Employer and Employee ............................................. 445
V.Significance ............................................................. 447
A.The Legal/Natural Person Dilemma ................... 447
B.The Human Author Requirement ........................ 449
C.Proper Disclosure ................................................ 450
D.Term of Copyright Protection ............................. 450
VI.Recommendations ............................................... 451
A.Previous Recommendations .................................... 451
B.Author’s Recommendations ................................ 452
VII.Conclusion .......................................................... 453
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I.INTRODUCTION
A.The Social Impact of AI
Innovation has been a driver of human progress since
the existence of mankind. Recognizing this, Article I of the
U.S. Constitution states that “Congress shall have the
power . . . to promote the progress of science and useful arts
by securing for limited times to authors and inventors the
exclusive right to their respective writings and discoveries.”1
Over the last two hundred years, a number of amendments
have been made to the U.S. copyright law to accommodate
for changes in societal norms. With the rapid growth in
speed and capability of modern computers, artificial
intelligence has secured a more prominent position as a
driver of innovation. Little has been done, however, to
accommodate for this fact.
Artificial intelligence has recently become a hot
topic. Flashy news stories about self-driving cars, creative
machines, and learning algorithms have made scholars,
policy makers, and consumers more aware of both the
benefits and need for AI. The recent popularization of AI
has also made us aware of the fact that humans are no longer
the only source of creative works. Computers with (and
sometimes without) human assistance are also able to create
artistic or innovative works.2 These computers are
1 U.S. CONST. art. I, § 8, cl. 8.
2 Stephen Thaler, the President and CEO of Imagination Engines Inc.,
has been credited with the creation of computer programs which
generate copyrightable material with and without human assistance.
See Tina Hesman, Stephen Thaler’s Computer Creativity Machine
Simulates the Human Brain, MINDFULLY.ORG (Jan. 24, 2004),
http://www.mindfully.org/Technology/2004/Creativity-Machine-
Thaler24jan04.htm (last visited Sept. 25, 2016).
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occasionally called “creativity machines.”3 At times, they
are programmed in such a way that they exhibit learned skills
which their creators do not posses. Creative works produced
as a result of these learned skills are a topic of debate, as they
fall into a legal grey area.4
Creativity machines are just one type of AI. Their
contribution to society, however, is significant, as they are
able to generate new ideas through the use of software which
mimics the configuration of human neural networks. These
networks are comprised of a number of switches which can
work together to assess information and create novel works
which differ from prior art.5 This process is often both
automatic and independent from human intervention. The
results may vary significantly, and are often unique works of
different levels of complexity and artistic value.6 As
computers become faster and more capable, creativity
machines and other forms of AI will likely take center stage
in the creative process, becoming the main drivers of
creativity and innovation.
3 Stephen Thaler, Creativity Machine® Paradigm, in ENCYCLOPEDIA OF
CREATIVITY, INVENTION, INNOVATION, AND ENTREPRENEURSHIP 451
(Elias G. Carayannis ed., 2013).
4 The U.S. Copyright Act does not directly address the matter of works
independently created by computer programs, thus leaving the subject
open to interpretation by the courts, scholars, and the U.S. Copyright
Office. For more information on autonomously machine generated
works, see U.S. COPYRIGHT OFFICE, COMPENDIUM OF U.S. COPYRIGHT
OFFICE PRACTICES § 313.2 (3rd ed. 2014).
5 Hesman, supra note 2.
6 Stephen Thaler, Neural Networks That Autonomously Create and
Discover, IMAGINATION ENGINES, INC., http://www.imagination-
engines.com/iei_pcai.php [https://perma.cc/52TZ-GPNB] (last visited
Sep. 25, 2016).
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B.AI as a Tool of the Human Author
This paper divides AI generated works into two main
categories. The first category is represented by works
generated by AI programs with the direct guidance,
assistance or input of human beings. In this category, AI is
used as a tool to achieve a determined or predicted goal or
outcome. An example may be the creation of a painting by
an artist who has selected the colors, tool type (brush size
and stroke style) and has to some extent input his
requirements into the AI algorithm used to create the work.
Although the artist cannot exactly predict the final version
of the generated painting, he has directly contributed to its
creation and has some expectations as to what it may look
like. Under U.S. copyright law, an author of such a work
may have legal claims over the resulting creation if he cites
the AI program as a tool or medium used in the creative
process.7
The 1884 Supreme Court case of Burrow-Giles
Lithographic Co. v. Sarony first extended copyright
protection to photography.8 The camera used to capture the
image of writer Oscar Wilde by photographer Napoleon
Sarony was considered by the court as a tool which aided the
“author” in creating “an original work of art.”9 Much has
changed in the world of photography since the days of
Sarony. Most cameras used today are fully digital and
posses both a computer processor and software which makes
photography a virtually automatic process. The 1884
Supreme Court ruling, however, is still used as a legal
precedent justifying the issuance of copyright to millions of
photographs taken each day. Since the image created by a
7 Cf. Burrow-Giles Lithographic Co. v. Sarony, 111 U.S. 53 (1884).
8 Id. at 60
9 Id. Legal protection for all photographs was eventually made a part of
the U.S. Copyright Act. 17 U.S.C. § 106A (2012).
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digital camera or smart phone is actually computer-
generated, it may very well be compared to the creation of
an art work using an AI program. Both processes are nearly
automatic and it could be argued that an AI machine, just
like a camera, is simply a tool employed by an author to
express his or her idea in a tangible form.10
C.AI as an Independent Actor in the Creative
Process
The second category of works, which this paper
focuses on in detail, deals with autonomously generated AI
creations. The computer programs responsible for
autonomously generating works are the result of human
ingenuity, their source code may be copyrighted as a literary
work under the U.S. Copyright Act.11 The artworks
generated by such programs, however, are not copyrightable
if not directly influenced by human authors.12 One example
given by the U.S. Copyright Office is a “weaving process
that randomly produces irregular shapes in the fabric without
any discernible pattern.”13 Since chance, rather than the
programmer of this “weaving machine”, is directly
responsible for its work, the resulting patterns would not be
protected by U.S. copyright. Randomness, just like
autonomously learned behavior is something that cannot be
attributed to the human programmer of an AI machine. As
10 See 17 U.S.C. § 102(a). “Copyright protection subsists, in accordance
with this title, in original works of authorship fixed in any tangible
medium of expression, now known or later developed, from which they
can be perceived, reproduced, or otherwise communicated, either
directly or with the aid of a machine or device.”
11 Computer Software Copyright Act, Pub. L. No. 96-517, § 117, 94 Stat.
3028 (1980) (codified at 17 U.S.C. § 117 (1988)).
12 U.S. COPYRIGHT OFFICE, supra note 4, § 306
13 Id. § 313.2.
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such, the resulting autonomous works are not eligible for
copyright protection and fall directly into the public domain.
II.THE ISSUE OF AI AUTHORSHIP
A.Current Stance of the U.S. Copyright
Office
Although the term “writings” is open to
interpretation within U.S. copyright law, a great number of
AI generated works often fall outside its scope by failing to
satisfy all of its requirements.14 The latest version of the
Compendium of best practices published by the U.S.
Copyright Office also poses a challenge to the registration of
autonomously generated AI works. In fact, creative works
generated solely by AI machines are not copyrightable if
they do not satisfy the human author requirement of the
Copyright Office.15 In other words, unless AI generated
works can directly be attributed to a human author, they
would theoretically not be copyrightable and would fall into
the public domain upon their creation.
As the copyright requirements listed by the U.S.
Copyright Office in the latest version of its Compendium
state, “[the office] will not register works produced by a
machine or mere mechanical process that operates randomly
or automatically without any creative input or intervention
from a human author.”16 This makes works created by AI
machines, for which the human author of the machine is not
directly responsible, fall into the public domain. As AI
14 The term “‘Writings’ . . . have not been construed in [its] narrow literal
sense but, rather, with the reach necessary to reflect the broad scope of
constitutional principles.” Goldstein v. California, 412 U.S. 546, 561
(1973).
15 U.S. COPYRIGHT OFFICE, supra note 4, § 306.
16 Id. § 313.2.
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programs become more sophisticated, less human
intervention would be required, resulting in an increasingly
autonomous creative process and a growing number of
works without any form of copyright protection. This issue
would only be magnified by the future development and
expansion of AI.
B.Disadvantages of the Current Stance
There is a considerable disadvantage to the release of
independently generated AI creative works into the public
domain. Without an established period of protection, there
is no tangible incentive for developers of AI machines to
continue creating, using, and improving their capabilities.
Simply put, even if programmers and the companies for
which they work have invested a substantial amount of time
and money into the creation of AI machines, for the most
part, they would not be able to enjoy copyright protection or
the financial benefits associated with it. This trend could
ultimately limit innovation by dissuading developers and
companies from investing in AI research, resulting not only
in the decline of AI but also in the decline of innovation
across a number of related sectors.
In the 1984 case of Sony Corp. of Am. v. Universal
Studios, Inc. the Supreme Court ruled that the limited
benefits associated with copyright ownership are “intended
to motivate the creative activity of authors and inventors by
the provision of a special reward, and allow the public access
to the products of their genius after the limited period of
exclusive control has expired.”17 Copyrighted works not
only serve as an incentive to creativity, but also increase the
number of works available in the public domain after their
copyright expiration.
17 Sony Corp. of Am. v. Universal City Studios, Inc., 464 U.S. 417, 429
(1984).
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Denying copyright from being issued to developers
and owners of AI machines reduces their incentives to create
new AI programs, and may ultimately lead to a lower
number of AI generated copyrightable works and (after
expiration of their copyrights) a considerable decrease in
works entering the public domain. As a result, it becomes
apparent that immediately releasing AI works into the public
domain, as opposed to doing so after a certain period of
copyright protection, significantly decreases incentives for
creativity and is counterproductive to the development of AI.
Less available, AI generated copyright protected
works would also mean less material available for use in
teaching, scholarship and research under the Copyright Act’s
fair-use doctrine. The doctrine allows the use of copyrighted
material for non-commercial educational purposes.18 A
decreased number of AI generated works would potentially
have far reaching negative effects in numerous sectors where
the impact of AI research is proving very beneficial. The
arts, education, medicine, technology, among others, could
suffer significantly, resulting in loss of valuable research and
future AI applications.
III.METHODOLOGY
Both an analytical and deductive approach have been
employed in determining the most effective solution to the
above mentioned issue. A number of scholarly and legal
texts relating to the matter of AI copyright have been
scrutinized and used to support the authors point. The U.S.
Copyright Act; legal cases which have set copyright law
precedent; and published articles on non-human creativity
and innovation have been analyzed, and a number of
solutions and recommendations have been formulated as a
result.
18 17 U.S.C. § 107.
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The author’s focus falls primarily on U.S. copyright
law as formulated in the Copyright Act of 1976 and its
subsequent amendments. The Act was written and
implemented at a time when AI generated works were still
uncommon and the capability of computers was still in its
infancy. The most recent representation of copyright law is
used in order to reflect the current issues facing U.S.
copyright policy and emphasize the need for a contemporary
solution by Congress. In addition, the made for hire doctrine
of U.S. copyright law is closely examined. After in-depth
analysis, the author of this paper deduces that a
reinterpretation of the terms “employee” and “employer” in
the made for hire doctrine is the least disruptive and most
practical solution to the issue of AI generated works falling
into the public domain.
The use of current copyright law, legal copyright
precedents, and scholarly articles pertaining to the issue,
serve as a method which helps the author formulate a much
needed solution to a growing problem in the AI sector. By
examining scholarly articles it is possible to understand the
scope of the issue. Legal cases and the precedents they set,
allow us to weigh the positive and negative effects of any
future changes in U.S. Copyright Act. Finally, close analysis
of current copyright law emphasizes the limited nature of
copyright protection offered to AI generated works, an issue
which reflects the outdated nature of the U.S. Copyright Act
of 1976.
IV.FINDINGS
A.Non-humans as Authors
Since only the authors of creative works may enjoy
legal protection,19 some scholars have argued that the term
19 Id. § 201(a)
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“authorship” should be redefined to include both human and
non-human authors.20 Professor Ryan Abbott is one such
strong proponent of legal rights for non-human authors and
inventors. In a recently published paper he argues that
assigning inventorship and authorship to non-humans is an
innovative new way to encourage AI growth and
development.21 In theory, this could prevent works
independently created by AI machines from falling into the
public domain and offer the programmers and companies
behind these machines some exclusivity to the resulting
copyrightable works. This theoretical solution, however, is
controversial and could lead to an uncertain future full of
legal challenges and systemic abuse.
Non-humans are not natural persons and may not be
held legally responsible in a court of law.22 As such, they
may not be considered authors according to guidelines set by
the U.S. Copyright Office.23 Redefining copyright
authorship to include non-human authors would undermine
the current U.S. legal system, creating further uncertainty by
raising more questions than answers. As a result, an
20 Colin R. Davies and Ryan Abbot have (independently) both argued
that computers should be considered legal authors/inventors under
relevant IP law. See Ryan Abbott, I Think, Therefore I Invent: Creative
Computers and the Future of Patent Law, 57 B.C. L. REV. 1079 (2016);
Colin R. Davis, An Evolutionary Step in Intellectual Property Rights—
Artificial Intelligence and Intellectual Property, 27 COMPUTER L. &
SECURITY REV. 601 (2011).
21 Abbott, supra note 20, at 1098–99.
22 The legal rights and responsibilities of non-human animals were issues
ruled on in both People v. Frazier; and Naruto v. Slater. In both
instances, the non-humans involved were deemed to have no legal
standing in front of the law, thus being absolved of all legal rights and
responsibilities within each case. Naruto v. Slater, 2016 U.S. Dist.
Lexis 11041 (N. D. Cal. Jan. 23, 2016); People v. Frazier, 173 Cal. App.
4th 613 (2009).
23 U.S. COPYRIGHT OFFICE, supra note 4, § 306.
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effective solution would require that both the legal status of
a copyright holder and the need for incentives for AI
developers are considered. These two important conditions
are necessary in order to ensure the legal standing and future
development of the AI sector.
B.Human Authorship Solution
The notion of assigning authorship of computer
generated works to humans can be traced back to U.K.
Copyright Code.24 Professor Annemarie Bridy echoes the
United Kingdom’s position by suggesting the use of an
amendment to the made for hire doctrine of the U.S.
Copyright Act as a way to transfer copyright to a human
author.25 An amendment of the Copyright Act, however,
must diverge from the current agency law approach used to
categorize the relationship between an employee and
employer, set as precedent by the Supreme Court’s decision
in Community for Creative Non-Violence v. Reid.26
Employing a relative interpretation of terms “employee” and
“employer” within the made for hire doctrine, as opposed to
rigidly defining them in accordance with agency law, is one
of the most effective ways to allow transfer of AI generated
works to human authors.
24 The copyright of computer generated works in the U.K. is attributed
to “the person by whom the arrangements necessary for the creation of
the work are undertaken,” similar to the employer in the U.S. Copyright
Act’s made for hire doctrine, who is prescribed authorship under
relevant copyright law. Copyright, Designs and Patents Act, 1988, c.
48, § 9(3) (U.K.).
25 See generally Annemarie Bridy, Coding Creativity: Copyright and the
Artificially Intelligent Author, 2012 STAN. TECH. L. REV. 5, ¶¶ 66–67
(2012).
26 Cmty. for Creative Non-Violence v. Reid, 490 U.S. 730, 739–40
(1989). Based on the Supreme Court’s ruling, the term “employee” in
17 U.S.C. § 101 must be viewed in accordance with agency law. Id.


The works made for hire doctrine defines two types
of copyrightable creations. The first is “a work prepared by
an employee during the scope of his or her employment.”27
The second, “a work specifically ordered or commissioned
for use . . . if the parties expressly agree in a written
instrument signed by them that the work shall be considered
a work made for hire.”28 In both examples copyright is
awarded to a party which was not originally responsible for
the creation of the work. The focus of this paper only falls
on the first example of employee generated works. This
paper argues that both “employer” and “employee” should
be viewed as relative terms within the scope of the made for
hire doctrine. This open interpretation would prevent AI
generated works from falling into the public domain by
assigning their copyright to a human author.
C.Human Authors: Programmers; Owners;
End Users
There are three possible parties which may have
claims to the copyright of AI generated works: AI
programmers; owners (large companies and financial
investors in the AI sector); and end users. When determining
the best possible author, it is necessary to consider the
overall social benefit of the copyright attribution process. In
other words, would society benefit most if copyright is
assigned to the AI programmer, the institution responsible
for funding the development of the AI, or the potentially
millions of end users of AI programs. To better gauge the
societal impact of each party, we must first determine the
ultimate goal of assigning copyright of AI generated works
to human authors. Next, we can assess which party
contributes most to this goal. Finally, we may deduce that
27 17 U.S.C. § 101.
28 Id.
 

the party which contributes most to the realization of this
goal is best suited to posses authorship of AI generated
works.
1.The Goal of Human Authorship
Providing financial incentives in order to encourage
the growth and development of the AI industry and ensure
the dissemination of AI generated works is arguably the
ultimate goal of assigning copyright to human authors. The
very idea of offering a temporary monopoly over new works
in order to promote innovation and creativity is enshrined in
the U.S. Constitution.29 As a result, American society has
been able to sustain its creative and innovative spirit for over
two centuries. Financial incentives should, therefore, be
reserved for the greatest contributors to the development and
dissemination of AI.
AI machines, unlike human developers, have no need
for financial incentives. Their performance is not dependent
on tangible rewards but rather on the investment of time and
skills by AI programmers and the financial backing of the
companies for which they work. These two entities are the
most important contributors to the research and development
of the AI sector. Without their contribution, AI devices
would simply not be available for use by the general public.
2.End Users and Authorship
Since end users have the smallest contribution to the
initial development of AI, their claims for authorship are
least compelling. In fact, assigning authorship to end users
instead of AI developers could be detrimental to the growth
of the AI sector. By losing copyright claims to end users,
owners and programmers may restrict the use of AI by third
parties. These protective measures would allow developers
to maintain copyright over the works generated by AI but
would also limit the applications of AI and the numerous
29 U.S. CONST. art. I, § 8, cl. 8.


benefits associated with them. As a result, society would
likely see a significant decline in AI generated works and a
decline in the overall development of the AI industry.
3.How to Incentivize the Contribution
of Developers
Providing incentives to AI programmers and owners
would be the logical solution to ensuring sustainable growth
and development of the AI sector. While independent
programmers may retain copyright for the work generated
by their AI, copyright of AI works created within large
companies may be settled through employment contracts
and attributed to either programmers or the companies for
which they work (based on the contractual agreement).
Should owners and programmers choose to assign copyright
to end users, this may be done through End User Licensing
Agreements (EULA). In the long term, licensing may prove
more financially viable for some companies, while
commercializing AI generated works may work best for
others.
D.Reinterpreting the Made for Hire
Doctrine’s Employer and Employee
As previously stated, it is necessary to allow AI
generated works to be copyrighted by either the author or
owner of the AI program. Since the authors and owners are
not always directly responsible for these AI generated
works, this is not possible under current U.S. copyright
practice.30 A feasible solution may be found in the made for
hire doctrine of the U.S. Copyright Act.31 According to the
doctrine, “(if) a work is made for hire, an employer is
considered the author even if an employee actually created
the work. The employer can be a firm, an organization, or
30 See U.S. COPYRIGHT OFFICE, supra note 4 at § 306.
31 Bridy, supra note 25, ¶¶ 63–69.
 

an individual.”32 These guidelines on issuing authorship to
a party that did not directly create a copyrightable work
could be applied to the AI industry.
The employee–employer relationship in the made for
hire doctrine may be applied to AI programs and their
developers if the terms “employer” and “employee” are
interpreted as relative within the confines of the doctrine.
Just as the term “author” may be applied to various entities
(an individual, a firm or organization), and the term
“writings” is an all-encompassing word that could mean
books, sound recordings, films, images, and even computer
code, so too should employer and employee be left open to
interpretation in order to satisfy newly arising requirements
and reflect contemporary social changes.33 Although the
current legal definition of employee may be constrained to
“a person usually below the executive level who is hired by
another to perform a service especially for wages or salary
and is under the other's control,” a more flexible definition
could also be used to accommodate the existing legal
limitations of AI generated works.34
A relative interpretation would mean that an
“employer” may be considered as someone who employs the
services of another entity in order to achieve a goal or
complete a task. A programmer or owner of an AI machine
would satisfy this definition as he or she employs the
services of the AI device in order to generate new creative
32 U.S. Copyright Office, Circular 9: Works Made for Hire (Sep. 2012),
https://www.copyright.gov/circs/circ09.pdf [https://perma.cc/V86P-
SA8A].
33 The terms “author” and “writings” have long been understood to have
flexible interpretations under the scope of relevant copyright law. See
Goldstein v. California, 412 U.S. 546 (1973).
34 Employee, MERRIAM-WEBSTER ONLINE, http://www.merriam-
webster.com/dictionary/employee#legalDictionary [https://perma.cc/
M4N2-LH6J] (last visited July 22, 2016).


works. Furthermore, if a relative interpretation is used, an
AI machine could be considered an employee since its
generative services are employed by its programmer or
owner. This new interpretation of two of the terms
(employer and employee) in the made for hire doctrine could
prove essential for the future development of AI by
providing the incentive of copyright protection to innovative
AI developers.
The employee–employer relationship, as interpreted
in relative terms to allow the passage of authorship from the
AI machine to its developer, would effectively solve the
current issue of AI generated works falling into the public
domain. Although authorship belongs to the original creator
of the work, in this case the AI device, the made for hire
doctrine would allow the developer or owner of the AI to be
“considered the author for the purpose of the title.”35 In
essence under the provisions of the made for hire doctrine,
the employer is not the actual author of the work, but is only
considered as such to satisfy requirements of the law.
V.SIGNIFICANCE
A.The Legal/Natural Person Dilemma
By reinterpreting the employee–employer
relationship of the made for hire doctrine a number of issues
are avoided. Firstly, copyrights are attributed to a
legal/natural person instead of a non-human with no legal
protection. Human programmers and companies who own
AI machines are considered natural and legal persons,
respectively.36 As such, they are fully responsible under the
law and enjoy all privileges and liabilities associated with it.
35 17 U.S.C. § 201(b).
36 Elvia Arcelia Quintana Adriano, The Natural Person, Legal Entity or
Juridical Person and Juridical Personality, 4 PENN. ST. J. L. & INT'L
AFF. 363, 366 (2015).
 

This is essential when awarding copyrights or if any future
legal challenges associated with ownership of the works in
question should arise.
This issue is clearly illustrated in the case Naruto v.
Slater.37 In 2011 the British wildlife photographer David
Slater traveled to Indonesia to take photographs of the local
macaques.38 During one of his shoots, Slater placed his
camera on a tripod, adjusted the camera’s settings to
accommodate for the surrounding environment and left the
remote shutter button deliberately accessible to the
macaques he was photographing.39 A female macaque
seized the opportunity and took a number of photos.40
Although only a handful of the resulting photographs were
actually usable, the “monkey selfies,” as they came to be
known, proved widely popular around the world.41 Upon
returning home, Slater began licensing the photos under the
presumption that he owned their copyright.42 His legal
claims over the photos were soon challenged in U.S. court.
People for the Ethical Treatment of Animals (PETA) argued
that the female macaque who had taken her own photographs
should be the legal owner of their copyright.43
37 Naruto v. Slater, 2016 U.S. Dist. Lexis 11041 at *1 (N. D. Cal. Jan.
23, 2016).
38 Photographer ‘lost £10,000’ in Wikipedia monkey ‘selfie’ row, BBC
NEWS (Aug. 7, 2014), http://www.bbc.com/news/uk-england-
gloucestershire-28674167 [https://perma.cc/2SWX-WWMR] (last
visited Sept. 25, 2016).
39 Id.
40 Id.
41 Id.
42 Id.
43 ‘Monkey Selfie’ Case Headed to U.S. Court of Appeals, PETA (Aug.
2, 2016), http://www.peta.org/blog/monkey-selfie-case-headed-u-s-


In January 2016 the judge presiding over Naruto v.
Slater dismissed the case stating that the monkey (identified
by PETA by the name Naruto) could not be considered an
author for the purpose of the law and as a result may not
posses any copyright even though the animal was directly
responsible for the creative works in question.44 The judge
further clarified that since an animal (non-human) does not
have legal standing in court, it may not sue or pursue
copyright using the law.45 The court’s ruling effectively
released the photographs in question into the public domain,
denying any claims of authorship by either David Slater or
the female macaque.
B.The Human Author Requirement
In addition, the latest publication of the
Compendium of best practices by the U.S. Copyright Office
clearly states that copyrights will only be granted to human
authors.46 Since animals and machines are not considered
humans they do not satisfy this requirement. Under the work
made for hire doctrine, however, authorship would not be
awarded to the non-human creator of the work but, rather, to
its human employer, effectively satisfying the human
requirement of the U.S. Copyright Office. The proposed
reinterpretation of the made for hire doctrine would ensure
that the copyright of all AI generated works is attributed to a
human author, eliminating the need for a lengthy debate over
the legality and practicality of non-human authorship.
court-appeals/ [https://perma.cc/Y6ZD-W236] (last visited Sept. 25,
2016).
44 Naruto v. Slater, 2016 U.S. Dist. Lexis 11041 at *3 (N. D. Cal. Jan.
23, 2016).
45 Id.
46 U.S. COPYRIGHT OFFICE, supra note 4, § 306.
 

C.Proper Disclosure
Another problem avoided by issuing copyright to
humans through a reinterpretation of the terms “employee”
and “employer” in the made for hire doctrine is the failure to
disclose AI participation in the creative process. Since a
cloud of uncertainty currently hangs over the registration of
AI generated works, developers of AI programs are often
reluctant to file for copyright, fearing that the process may
ultimately result in rejection by the U.S. Copyright Office.
In some cases, this reluctance may even result in knowingly
withholding information about the contribution of AI in the
creative process. Failing to attribute the creation of a work
to its rightful author has serious consequences and could
potentially invalidate a copyright claim.47 Allowing the
transfer of copyright to a human employer effectively
resolves the above issue and ensures that AI generated works
are not only registered lawfully, but also properly
documented.
D.Term of Copyright Protection
Finally, unlike human authors who have a limited
lifespan, AI programs could perpetually exist. This
challenges the predetermined term of copyright protection
given to authors (life of author plus 70 years in the U.S.).48
A reinterpretation of the employee–employer relationship in
the made for hire doctrine to allow transfer of copyright from
AI to its employer effectively resolves this issue since the
doctrine’s provisions state that “(the) term of copyright
protection of a work made for hire is 95 years from the date
of publication or 120 years from the date of creation,
47 17 U.S.C. § 411(a).
48 Id. § 302(a).


whichever expires first.”49 Both the date of publication and
the date of creation may easily be determined unlike the
actual lifespan of an AI program.
VI.RECOMMENDATIONS
A.PREVIOUS RECOMMENDATIONS
The 1974 formation of the Commission of New
Technological Uses of Copyrighted Works (CONTU) by
Congress was a response to new emerging technologies and
the rapid growth of private computer use in the U.S.50 The
Commission, tasked with researching and formulating
recommendations for Congress on copyright in the computer
age, declared in its 1978 report that computers were simply
tools whose main function was to assist human authors in the
creative process.51 In addition, the report also stated that
independently generated computer works required no
particular consideration since autonomous works were not
deemed possible in the foreseeable future.52 In light of
technological advancements over the last three decades and
the rapid growth of AI, a reassessment of the
recommendations issued in CONTU’s 1978 report is long
overdue.
49 Id. § 302. For a complete explanation of copyright terms related to
works under the works made for hire doctrine of 17 U.S.C., see U.S.
COPYRIGHT OFFICE, Circular 15A: Duration of Copyright (Aug. 2011),
https://www.copyright.gov/circs/circ15a.pdf [https://perma.cc/3BU5-
DGAK].
50 NATIONAL COMMISSION ON NEW TECHNOLOGICAL USES OF
COPYRIGHTED WORKS, Final Report on New Technological Uses of
Copyrighted Works 1–21 (1978), http://eric.ed.gov/PDFS/
ED160122.pdf [https://perma.cc/WEE2-746L].
51 Id. at 110. The Commission further provides a number of examples of
how computer programs may simplify or shorten the creative process
but not be solely responsible for it.
52 Id. at 109.
 

B.Author’s Recommendations
The U.S. Copyright Act has gone through a number
of revisions over the years. Each new addition to the U.S.
Copyright Act reflects a fundamental change in the way
American society perceives the creative process and the
tools deemed necessary to reinforce it. No changes,
however, have been exercised to reflect the most recent
technological phenomenon of machine learning, commonly
referred to as artificial intelligence. The following segment
of this paper summarizes the necessary steps needed to bring
the U.S. Copyright Act to modernity by directly addressing
the issue of AI generated works and their copyright
eligibility.
In order to promote future development of the AI
industry and ensure dissemination and application of AI
generated works Congress needs to take the following steps:
1)Acknowledge that as a result of recent enhanced
computer capabilities, humans are no longer the only
source of innovative and creative works.
2)Recognize the need for incentives (under the form of
copyright protection) needed by programmers and AI
owners in order to stimulate future development and
investment in the AI field.
3)Do not redefine “authorship” by including non-
humans or non-legal persons. This would open a
Pandora’s Box of complications and future legal
challenges.
4)Allow a relative interpretation of the terms
“employer” and “employee” in the made for hire
doctrine of the U.S. Copyright Act. By accepting
employer and employee as relative terms open to
interpretation (just like the term “author” in the U.S.
Copyright Act) the doctrine may be used to transfer
authorship from the original creator (the AI
machine), to its employer (the programmer or owner
of the device).
5)Any new legislation enacted by congress should be
periodically reviewed and amended in light of new
and emerging technological advances. The copyright


of AI generated works will undoubtedly need to be
reassessed in the not too distant future as machine
learning becomes more sophisticated and AI devices
become more capable and autonomous.
VII.CONCLUSION
The recent development of machine learning
capabilities has resulted in an increased number of AI
generated works and an understanding that humans are no
longer the only source of creativity or innovation. The
outdated nature of the current U.S. Copyright Act, however,
fails to reflect this contemporary reality, resulting in the
release of a great number of AI generated works into the
public domain. This trend does not benefit the programmers
and owners of AI devices and limits their willingness to
invest resources in the future development of AI.
The consequences of this gap in copyright law are far
reaching and may result in a decrease of valuable new works
available to scholars, researchers, and consumers, and a
significant delay in technological and artistic progress of
modern society. As significant as this issue may be, it has
yet to be effectively addressed and a need for a practical
solution still exists. This solution should be both
motivational to AI developers and non-disruptive to the
current legal system. Satisfying these requirements would
ensure the smooth development of AI and secure its long-
term role as a driver of creativity and innovation.
The proposed reinterpretation of the terms
“employee” and “employer” in the made for hire doctrine is
an effective and practical way to address the above
mentioned shortcoming of the U.S. Copyright Act. Under a
new interpretation of the terms in the made for hire doctrine,
authorship of AI generated works would be awarded to the
programmers and owners of AI devices. This legal incentive
would financially benefit those responsible for AI
development, resulting in a significant boost in research and
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investment in the AI sector and the modernization of a
rapidly aging U.S. Copyright Act.
... Central to this discourse is the vexing question of determining rightful ownership of AIgenerated works, a question exacerbated by the intricate interplay between human agency and machine output. While AI systems possess the capacity for independent generation, they remain reliant on human programming and input, thereby prompting a fundamental inquiry into the locus of ownership: whether it resides with the AI system itself, its human programmers, the entities deploying it, or a combination thereof (Hristov, 2016). ...
... Is the responsibility for the AI system primarily attributed to the AI system itself, the human programmer, the entity that has the AI system, or a combination of these entities? (Hristov, 2016). ...
Article
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This article investigates the impact of artificial intelligence (AI) on intellectual property (IP) rights, addressing challenges in ownership and authorship of AI-generated creations while exploring legal and ethical dilemmas in traditional IP domains. It offers strategies for navigating these complexities, drawing on legal precedents, international agreements, and policy recommendations. The research emphasizes the urgent need for legislative updates to address these challenges effectively. Recommendations include the enactment of innovative constitutional provisions, updating IP legislation to encompass AI-related issues comprehensively, and advocating for effective judicial intervention. By implementing these strategies, Sri Lanka can foster a harmonious coexistence of AI and IP, ensuring the protection of intellectual property rights while stimulating innovation in the AI era.
... However, like any technological revolution, it has great disruptive and destructive potential. Experts' concerns regarding the adverse effects of using AI technologies range from privacy and copyright issues to fear of global annihilation (Bartneck et al., 2021;Hristov, 2016;Yudkowsky, 2008;Yudkowsky et al., 2010). More moderate assessments of the concerns regarding the use of AI refer to issues of fairness, ethical use, inconsistency in service delivery, and discrimination (Nzobonimpa and Savard, 2023). ...
Article
Full-text available
In a world grappling with technological advancements, the concept of Artificial Intelligence (AI) in governance is becoming increasingly realistic. While some may find this possibility incredibly alluring, others may see it as dystopian. Society must account for these varied opinions when implementing new technologies or regulating and limiting them. This study ( N = 703) explored Leftists’ (liberals) and Rightists’ (conservatives) support for using AI in governance decision-making amidst an unprecedented political crisis that washed through Israel shortly after the proclamation of the government’s intentions to initiate reform. Results indicate that Leftists are more favorable toward AI in governance. While legitimacy is tied to support for using AI in governance among both, Rightists’ acceptance is also tied to perceived norms, whereas Leftists’ approval is linked to perceived utility, political efficacy, and warmth. Understanding these ideological differences is crucial, both theoretically and for practical policy formulation regarding AI’s integration into governance.
... 43 A distinct and further legal issue arises when an LLM-generated output can be regarded as an autonomous creation, legally independent from the pre-existing materials. In this scenario, the question pertains to whether such output may be eligible for protection under IP law, specifically through copyright (in the case of literary, artistic, or scientific works) or through patent protection (in the case of an invention) [48,[59][60][61]. ...
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The complexity and emergent autonomy of Generative AI systems introduce challenges in predictability and legal compliance. This paper analyses some of the legal and regulatory implications of such challenges in the European Union context, focusing on four areas: liability, privacy, intellectual property, and cybersecurity. It examines the adequacy of the existing and proposed EU legislation, including the Artificial Intelligence Act (AIA), in addressing the challenges posed by Generative AI in general and LLMs in particular. The paper identifies potential gaps and shortcomings in the EU legislative framework and proposes recommendations to ensure the safe and compliant deployment of generative models.
... 10 In its second case, DABUS, an AI system, was listed as an inventor before the European Patent Office. 11 The claimed invention improved the shipping safety measure based on fractal geometry and consisted of a beacon which flickered in patter mimicking neural activity to attract attention. Based on artificial neural networks, the invention was developed by DABUS to generate and analyze ideas and their novelty and utility. ...
Article
The judicial decision in DABUS case have stirred up an international debate over AI authorship. This article analyses the judicial decisions determining the legal status under intellectual property laws of the work generated by an artificial intelligence. Since the traditional concept of intellectual property protects the work generated by humans, proliferation of artificial Intelligence raises the need to redefine the status of personhood, inventorship, authorship and subject matter eligibility. The research is non-empirical, based on the judicial decisions on DABUS and Dreamwriter along with the international conventions. The paper explores the question whether the existing laws are sufficient to address the technological advancements, and if not, then what might be the possible outcome.
... The protection of AI-generated works under copyright law is a topic of evolving legal interpretation, varying significantly across jurisdictions. Traditionally, copyright law requires a work to originate from a human author to qualify for protection [16,17]. This principle, based on the concept of original human creativity, poses a challenge when applied to AI, which can create works independently of direct human input. ...
Article
Full-text available
This research explores the intricate landscape arising from the integration of big data and large language models (LLMs) across sectors, unveiling intellectual property (IP) challenges requiring careful scrutiny. The transformative impact of big data and the ascendancy of LLMs in artificial intelligence have precipitated complex inquiries into data ownership, copyright law, and privacy. Central to these challenges is the ownership of datasets, especially those crucial in LLM training, reflecting the ambiguous nature of data as a contemporary digital asset. LLMs, proficient in generating content akin to their training materials, introduce nuances challenging traditional copyright boundaries. Privacy concerns escalate due to the pivotal role of personal data in both big data analytics and LLM functionality. This research aims for a comprehensive examination of these IP challenges by scrutinizing existing legal frameworks, evaluating their adequacy in the context of big data and LLMs, and unraveling the intricate relationship between technological innovation and IP law. The ultimate goal is to propose legal solutions or frameworks adept at tackling these emergent challenges. The significance of this research lies in its potential to shape robust legal and ethical standards in the digital age, providing valuable insights for policymakers, technologists, and legal experts to navigate the evolving nexus of technology and intellectual property.
... Administrators and instructors are concerned that students might use AI to create materials for evaluation with varying levels of originality, potentially compromising educational fairness and effectiveness 21,22 . Similarly, there is a growing debate, underscored by notable incidents 23,24 , concerning the copyright eligibility of AI-assisted works 20,25,26 . ...
Preprint
With the growing prevalence of generative artificial intelligence (AI), an increasing amount of content is no longer exclusively generated by humans but by generative AI models with human guidance. This shift presents notable challenges for the delineation of originality due to the varying degrees of human contribution in AI-assisted works. This study raises the research question of measuring human contribution in AI-assisted content generation and introduces a framework to address this question that is grounded in information theory. By calculating mutual information between human input and AI-assisted output relative to self-information of AI-assisted output, we quantify the proportional information contribution of humans in content generation. Our experimental results demonstrate that the proposed measure effectively discriminates between varying degrees of human contribution across multiple creative domains. We hope that this work lays a foundation for measuring human contributions in AI-assisted content generation in the era of generative AI.
... Some argue extending copyright law to machine-created works would reduce the value of human creativity [76], flood the market with creations of questionable quality [37], and concentrate power in the hands of a few [17,47]. In contrast, proponents of extending copyright to AI-generated works defend that protecting these outputs could promote innovation by incentivising research and development of AI [44,61], as well as enable users to create works that would not be possible without GenAI. ...
Preprint
Recent breakthroughs in generative AI (GenAI) have fueled debates concerning the status of AI-generated creations under copyright law. This research investigates laypeople's perceptions (N = 424) of AI-generated art concerning factors associated with copyright protection. Inspired by prior work suggesting that people show egocentric biases when evaluating their own creative outputs, we also test if the same holds for AI-generated art. Namely, we study the differences between the perceptions of those who have something to gain from copyright protection -- creators of AI-generated art -- and uninvested third parties. To answer our research questions, we held an incentivized AI art competition, in which some participants used a GenAI model to generate images for consideration while others evaluated these submissions. We find that participants are most likely to attribute authorship and copyright over AI-generated images to the users who prompted the AI system to generate the image and the artists whose creations were used for training the AI model. We also find that participants egocentrically favored their own art over other participants' art and rated their own creations higher than other people evaluated them. Moreover, our results suggest that people judge their own AI-generated art more favorably with respect to some factors (creativity and effort) but not others (skills). Our findings have implications for future debates concerning the potential copyright protection of AI-generated outputs.
Conference Paper
This article examines the potential impact of AI-generated images on the creative labor industry from a social informatics perspective. The research uses quantitative data collection and classification clusters to analyze outcomes. However, various factors, such as the data collection mechanism of AI, have caused deviations from the intended research objectives. Future research should focus on implementing detailed inspection clusters and specialized data collection tools to ensure the quality of results. Additionally, the paper highlights the negative impact of AI tools on social and moral aspects, apart from the professional issues within the design industry ecology. This emerging technology-driven phenomenon requires further academic attention, especially in social informatics.
Designs and Patents Act
  • Law
  • Copyright
copyright law. Copyright, Designs and Patents Act, 1988, c. 48, § 9(3) (U.K.).
Coding Creativity: Copyright and the Artificially Intelligent Author
25 See generally Annemarie Bridy, Coding Creativity: Copyright and the Artificially Intelligent Author, 2012 STAN. TECH. L. REV. 5, ¶ ¶ 66–67 (2012).
Based on the Supreme Court's ruling, the term " employee
  • Cmty
  • For Creative Non-Violence V
  • Reid
Cmty. for Creative Non-Violence v. Reid, 490 U.S. 730, 739–40 (1989). Based on the Supreme Court's ruling, the term " employee " in 17 U.S.C. § 101 must be viewed in accordance with agency law. Id.
Lexis 11041 at *1 (N. D. Cal
  • Naruto V Slater
Naruto v. Slater, 2016 U.S. Dist. Lexis 11041 at *1 (N. D. Cal. Jan. 23, 2016).
000' in Wikipedia monkey 'selfie' row
  • Bbc News
38 Photographer 'lost £10,000' in Wikipedia monkey 'selfie' row, BBC NEWS (Aug. 7, 2014), http://www.bbc.com/news/uk-englandgloucestershire-28674167 [https://perma.cc/2SWX-WWMR] (last visited Sept. 25, 2016).
Lexis 11041 at *3 (N. D. Cal
  • Naruto V Slater
Naruto v. Slater, 2016 U.S. Dist. Lexis 11041 at *3 (N. D. Cal. Jan. 23, 2016).