European Journal of Risk Regulation (2024), 15, 602–624

doi:10.1017/err.2023.59

ARTICLE

ChatGPT: A Case Study on Copyright Challenges for

Generative Artificial Intelligence Systems

Nicola Lucchi
Department of Law, University Pompeu Fabra, Barcelona, Spain.
Email: Nicola.Lucchi@upf.edu

Abstract
This article focuses on copyright issues pertaining to generative artificial intelligence (AI) systems,
with particular emphasis on the ChatGPT case study as a primary exemplar. In order to generate
high-quality outcomes, generative AI systems require substantial quantities of training data, which
may frequently comprise copyright-protected information. This prompts inquiries into the legal
principles of fair use, the creation of derivative works and the lawfulness of data gathering and
utilisation. The utilisation of input data for the purpose of training and enhancing AI models presents
significant concerns regarding potential violations of copyright. This paper offers suggestions for
safeguarding the interests of copyright holders and competitors, while simultaneously addressing
legal challenges and expediting the advancement of AI technologies. This study analyses the ChatGPT
platform as a case example to explore the necessary modifications that copyright regulations
must undergo to adequately tackle the intricacies of authorship and ownership in the realm of
AI-generated creative content.

Keywords: Artificial intelligence; ChatGPT; copyright; data sharing; intellectual property; language
models; training data

I. Introduction
News articles, academic papers, social media posts, photos and even chatbot chats are just
some of the examples of how artificial intelligence (AI) is being put to use in the content
creation process. Concerns have been voiced regarding the potential for AI to replace or
https://doi.org/10.1017/err.2023.59 Published online by Cambridge University Press

mimic human behaviour as the technology continues to improve and find diverse
applications across a wide range of sectors and industries. As a result, many organisations
and academics in the field of law are starting to think about how AI might affect our
society and the law.1 Many areas of law are now grappling with the implications of these

1
The emergence of generative AI infrastructures has presented new regulatory challenges in the field. For
example, the European Commission is currently in the process of drafting the AI Act, the first law on AI by a major
regulator, to regulate the emerging technology that has seen a surge in investments and popularity, particularly
following the release of ChatGPT and its derivatives. Similar to the EU’s General Data Protection Regulation
(GDPR) in 2018, the EU AI Act has the potential to become a global standard, shaping the extent to which AI can
have either positive or negative effects on individuals’ lives worldwide. The draft is currently undergoing the
trilogue phase, where EU parliamentarians and Member States will define the final details of the regulation. For
more information, see the “Proposal for a Regulation of the European Parliament and of the Council Laying down
Harmonised Rules on Artificial Intelligence (Artificial Intelligence Act) and Amending Certain Union Legislative
Acts”, COM(2021) 206 final. See N Helberger and N Diakopoulos, “ChatGPT and the AI Act” (2023) 12 Internet Policy
Review 10.14763/2023.1.1682.

© The Author(s), 2023. Published by Cambridge University Press. This is an Open Access article, distributed under the terms of the
Creative Commons Attribution licence (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use,
distribution and reproduction, provided the original article is properly cited.
 European Journal of Risk Regulation 603

technologies.2 In this text, however, we will focus specifically on how AI-generated works
may impact intellectual property law, with a particular emphasis on copyright law. In this
work, we will briefly investigate some of the copyright issues linked with the usage of AI
systems that recognise and generate text, known as large language models (LLMs),3
focusing specifically on the ChatGPT case study.4 Being a frequently utilised and well-
known example of AI content production, ChatGPT provides a good lens for examining
some of the fundamental copyright concerns at play in this rapidly growing sector.
ChatGPT is a language model created by OpenAI5 – a San Francisco-based AI company –
that can generate replies in natural language to a variety of queries.6 A LLM is a highly
effective type of machine learning process designed specifically for natural language
processing tasks.7 Its main focus is on language modelling, which involves creating
probabilistic models that can accurately predict the next word in a given sequence based
on the preceding words.8 This is accomplished by training the model on large amounts of
text data, which allows it to learn the probability of word occurrences and the patterns in
language usage.9 The goal of language modelling is to create a system that can accurately
generate human-like responses and recognise natural language input, making it an
essential component of modern natural language processing applications.
It is important to stress that the language modelling task relies solely on form as
training data, and therefore cannot inherently lead to the learning of meaning.10 These
models are therefore characterized by their ability to “agere sine intelligere”11; that is, to act
without understanding exactly what they return as a result. This concept highlights the
fascinating nature of their modus operandi, as they are able to perform complex tasks and
produce results that can be remarkably accurate despite lacking a comprehensive
understanding of the underlying processes. This phenomenon challenges conventional
notions of intelligence, as these models have the potential to produce impressive results
through a combination of sophisticated algorithms, vast amounts of data and intricate
pattern recognition capabilities. Their ability to “agere sine intelligere” demonstrates the
power of machine learning and its potential to revolutionise various fields, from natural
language processing to image recognition and beyond. The advent of language models and
various AI systems that produce content has been nothing short of a game-changer in

2
An illustration of this is the recent decision by the Italian Data Protection Authority to take action against
OpenAI’s operations of ChatGPT in Italy, highlighting the tensions that exist between the EU’s GDPR and the use of
generative AI infrastructures that are trained on massive datasets containing both personal and non-personal
data. See Garante per la Protezione dei Dati Personali, “Intelligenza artificiale: il Garante blocca ChatGPT. Raccolta
https://doi.org/10.1017/err.2023.59 Published online by Cambridge University Press

illecita di dati personali. Assenza di sistemi per la verifica dell’età dei minori” (31 March 2023), <https://www.
garanteprivacy.it/home/docweb/-/docweb-display/docweb/9870847> (last accessed 1 August 2023).
3
See, eg, Y Goldberg, Neural Network Methods for Natural Language Processing (Cham, Springer 2017) p 105;
P Henderson et al, “Ethical Challenges in Data-Driven Dialogue Systems” (2018) Proceedings of the 2018 AAAI/
ACM Conference on AI, Ethics, and Society 123; CD Manning et al, An Introduction to Information Retrieval
(Cambridge, Cambridge University Press 2008) p 238.
4
The OpenAI GPT model was proposed in A Radford et al, “Improving Language Understanding by Generative
Pre-Training” (2018) <https://www.cs.ubc.ca/∼amuham01/LING530/papers/radford2018improving.pdf> (last
accessed 1 August 2023).
5
See OpenAI <https://openai.com/>.
6
See OpenAI, “Introducing ChatGPT” <https://openai.com/blog/chatgpt> (last accessed 1 August 2023).
7
See EM Bender and A Koller, “Climbing towards NLU: On Meaning, Form, and Understanding in the Age of
Data” in Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (Association for
Computational Linguistics, Online, 2020) pp 5185–98 (defining the term “language model” as any system trained only
on the task of string prediction, whether it operates over characters, words or sentences and sequentially or not).
8
Goldberg, supra, note 3, 105.
9
Manning et al, supra, note 3, 238.
10
Bender and Koller, supra, note 7, 5185.
11
L Floridi, “AI as Agency without Intelligence: on Chat GPT, Large Language Models and Other Generative
models” (2023) 36 Philosophy & Technology 1, 6.
 604 Nicola Lucchi

today’s world. These systems have the ability to generate text in any language, in any
format and on any topic within seconds. The impact of these systems is therefore truly
enormous, and it has given rise to numerous legal and ethical issues that need to be
explored, especially from a copyright perspective.
Much of the current legal debate surrounding generative AI and copyright has focused
on the potential protection of a “creative” product produced by AI technologies under
copyright or similar intellectual property (here referred to as “the output”).12 However, it
is important to recognise that there are also significant copyright issues associated with
the use of copyrighted information to train and develop AI systems (here referred to as
“the input”). Indeed, AI systems require massive amounts of training data, which
frequently contain copyrighted information, in order to create high-quality outputs. This
raises concerns about whether and how such data may be collected and utilised lawfully, as
well as concerns about derivative works13 and fair use.14 Furthermore, as AI systems grow
more prevalent and vital in our daily lives, it is critical to address the copyright challenges
arising from the process of training AI models. This involves the creation of derivative
works from protected sources, often requiring modifications or manipulations of data to
enhance their suitability for training purposes. Recently, the legal debate surrounding AI
has intensified, leading to numerous lawsuits against creators of generative AI systems
such as ChatGPT, alleging copyright infringement.15 These lawsuits raise legitimate
concerns about the unauthorised use of copyrighted material in order to create new
creative content. In light of these challenges, a comprehensive and holistic approach is
needed to tackle the copyright problems associated with AI, considering both the inputs
and outputs of AI systems. This investigation will also delve deeper into the policy
rationales for considering a free or open-access approach to AI training data, with the goal

12
Many scholars have given attention to the question of ownership and authorship: see, eg, R Abbot, The
Reasonable Robot (Cambridge, Cambridge University Press 2020); E Bonadio and L McDonagh, “Artificial
Intelligence as Producer and Consumer of Copyright Works: Evaluating the Consequences of Algorithmic
Creativity” (2020) 2 Intellectual Property Quarterly 112; E Bonadio et al, “Intellectual property aspects of robotics”
(2018) 9 European Journal of Risk Regulation 655; A Bridy, “Coding Creativity: Copyright and the Artificially
Intelligent Author” (2012) 5 Stanford Technology Law Review 1; R Denicola, “Ex Machina: Copyright Protection for
Computer-Generated Works” (2016) 69 Rutgers University Law Review 251; T Dornis, “Artificial Creativity:
Emergent Works and the Void in Current Copyright Doctrine” (2020) 22 Yale Journal of Law & Technology 1;
J Grimmelmann, “There’s No Such Thing as a Computer-Authored Work – And It’s a Good Thing, Too” (2016) 39
Columbia Journal of Law & the Arts 403; A Guadamuz, “Do Androids Dream of Electric Copyright? Comparative
Analysis of Originality in Artificial Intelligence Generated Works” (2017) 2 Intellectual Property Quarterly 169;
https://doi.org/10.1017/err.2023.59 Published online by Cambridge University Press

AH Khoury, “Intellectual Property Rights for Hubots: On the Legal Implications of Human-like Robots as
Innovators and Creators” (2017) 35 Cardozo Arts & Entertainment Law Journal 635; M Lemley and B Casey,
“Remedies for Robots” (2019) 86 University of Chicago Law Review 1311; E Bonadio and N Lucchi (eds), Non-
Conventional Copyright: Do New and Non Traditional Works Deserve Protection? (Cheltenham, Edward Elgar 2018);
D Lim, “AI & IP: Innovation & Creativity in an Age of Accelerated Change” (2018) 52 Akron Law Review 813;
P Samuelson, “Allocating Ownership Rights in Computer-Generated Works” (1986) 47 University of Pittsburgh
Law Review 1185; P Yu, “The Algorithmic Divide and Equality in the Age of Artificial Intelligence” (2020) 72 Florida
Law Review 331; R Yu, “The Machine Author: What Level of Copyright Protection Is Appropriate for Fully
Independent Computer-Generated Works?” (2017) 165 University of Pennsylvania Law Review 1245; DL Burk,
“Thirty-Six Views of Copyright Authorship, by Jackson Pollock” (2020) 58 Houston Law Review 263.
13
In copyright law, a derivative work is a work that is based on one or more pre-existing works, such as a
translation, adaptation, sequel or a work that is based on another work in some way. A derivative work is
considered to be a new work, but it still retains some of the characteristics of the original work. A work can serve a
transformative purpose, even if it does not alter the content of the original work. See RA Reese,
“Transformativeness and the Derivative Work Right” (2008) 31 Columbia Journal of Law & the Arts 467, 485.
14
17 U.S.C. § 107 (2018).
15
See, eg, Getty Images (US), Inc. v. Stability AI, Inc., No. 1:23-cv-00135-GBW (D. Del. Mar. 29, 2023); Silverman et al. v.
OpenAI, Inc. et al., No. 4:23-cv-03416 (N.D. Cal. Jul. 7, 2023); Tremblay et al. v. OpenAI, Inc. et al., No. 4:2023-cv-03223
(N.D. Cal. Jul. 7, 2023). Essentially, all of these very recent lawsuits (still pending) allege that the incorporation of
training data by generative AI models is an infringement of copyright holders’ rights.
 European Journal of Risk Regulation 605

of potentially proposing legislation that encourages the responsible and ethical use of such
data while protecting intellectual property rights.16
In light of the scenarios mentioned, this article aims to propose effective strategies that
can address the legal issues arising from AI system development while simultaneously
safeguarding the rights of copyright holders and competitors. Given the rapid
advancements in AI technology, it is essential to establish a robust legal framework as
well as a set of rules to ensure the protection of all stakeholders involved.
The article is divided into three parts. In the first part, we set the stage by discussing the
capabilities and limitations of powerful language models, including their potential and
actual applications as well as their limitations. In the second part, we look at the case study
of ChatGPT and explore how this generative AI system works, discussing specific copyright
concerns. In particular, we explore the practical applications of ChatGPT-generated text
and address important issues related to ownership and copyright, especially when the
content is created by a machine rather than a human author. In the third part, we further
analyse the ChatGPT case study by focusing on the challenges related to training data and
copyright. We address the complexities of data ownership and use and explore the
different types of data used to train the ChatGPT models. We also analyse some recent
court cases and examine the ethical and legal dilemmas that arise when dealing with large
datasets. In addition, we investigate and evaluate a number of potential alternatives that
can effectively safeguard copyrighted training data used in the field of AI to feed
generative AI systems. To conclude, we provide a concise summary of our comprehensive
analysis and highlight the significant findings and insights we have gained from
investigating ChatGPT language models. We acknowledge the obstacles and limitations
that must be overcome to advance these models and emphasise the importance of
addressing these issues responsibly and ethically.
Due to the breadth of the legal and policy issues presented in this article, and in order to
support the “circulation of models”, the article will provide a mix of civil and common law
elements based on the regulations and laws of multiple jurisdictions, with a particular
emphasis on the USA, the UK and the European Union (EU).

II. ChatGPT: the AI language model revolutionising content creation

You may have heard about ChatGPT, which attracted millions of users quickly after its
launch. OpenAI released ChatGPT as a chatbot17 designed primarily to assist with online
https://doi.org/10.1017/err.2023.59 Published online by Cambridge University Press

customer care. Yet, the AI technology underpinning this program has outperformed all
forecasts and grown in popularity.

16
Numerous policy initiatives have been implemented at both the international and EU levels pertaining to the
intersection of AI and copyright (even if they mainly focused on the question of the output of AI). See, eg,
European Parliament, Resolution on a comprehensive European industrial policy on artificial intelligence and
robotics, (2018/2088 (INI)), 12 February 2019; European Parliament resolution of 20 October 2020 on intellectual
property rights for the development of artificial intelligence technologies (2020/2015 (INI); WIPO, “WIPO
Technology Trends 2019: Artificial Intelligence” <https://www.wipo.int/publications/en/details.jsp?id=4386>
(last accessed 1 August 2023); WIPO, “Revised Issues Paper on Intellectual Property Policy and Artificial
Intelligence”, 2020 (WIPO/IP/AI/2/GE/20/1 REV) <https://www.wipo.int/edocs/mdocs/mdocs/en/wipo_ip_ai_
2_ge_20/wipo_ip_ai_2_ge_20_1_rev.pdf> (last accessed 1 August 2023).
17
Chatbots are computer programs that use natural language processing (NLP) and machine learning
algorithms to understand and interpret user input and respond with appropriate pre-programmed messages or
actions designed to simulate human-to-human conversation, typically via text-based interfaces such as messaging
apps, websites or mobile applications. See R Dale, “The return of the chatbots” (2016) 22 Natural Language
Engineering 811; E Adamopoulou and L Moussiades, “An Overview of Chatbot Technology” (2020) 584 Artificial
Intelligence Applications & Innovations 373.
 606 Nicola Lucchi

What is the definition of ChatGPT? We have chosen to ask the question directly to the
chatbot:

ChatGPT is an AI language model developed by OpenAI, which uses Natural Language

Processing (NLP) to generate human-like text in response to various inputs. It is
essentially an auto-generating chatbot that extracts data from various sources,
processes it, and produces grammatically correct and contextually appropriate
responses. ChatGPT can be used for a variety of applications, including customer
service, chatbots, and virtual assistants. It has been trained on a massive amount of
text data and is constantly learning and improving.

The ChatGPT software application was designed to facilitate and support interactive
communication by generating automatic text-based responses. The methodology involves
integrating information from various sources, such as books, journals, websites and
articles, resulting in the development of original and engaging discourse. The field of
natural language processing (NLP) involves the use of computational methods to facilitate
the understanding and analysis of large amounts of data in a way that mimics human
language. Computer systems are capable of acquiring significant amounts of data or
executing commands by utilising NLP functionalities.18 It is worth noting that the
operational functionalities of ChatGPT are limited to the understanding of syntax. The
employed methodology involves a comprehensive analysis of the sentence’s syntactic
structure, along with the identification of the interdependencies among its constituent
elements. Although ChatGPT has undergone comprehensive training in handling vast
datasets, it continues to depend on statistical techniques for analysis and comprehen-
sion.19 The aforementioned statement implies that the system possesses the ability to
identify patterns in the utilisation of specific terminologies and idiomatic expressions and
employs these patterns to generate informed assumptions regarding the intended
interpretation of a provided sentence or phrase. It is crucial to recognise that this
statement precludes any inferences about ChatGPT’s understanding of the aforementioned
principles. In the event that an individual were to inquire of ChatGPT regarding the
essence of existence, the response provided may appear to be lucid and perceptive;
however, it may lack a comprehensive comprehension of the philosophical principles and
hypotheses that underlie the inquiry. ChatGPT is just an advanced chatbot that employs
NLP to comprehend vast quantities of information and produce responses that closely
resemble human language. Its capabilities are currently limited to syntactic understand-
https://doi.org/10.1017/err.2023.59 Published online by Cambridge University Press

ing, meaning that it can analyse the grammatical structure of sentences and comprehend
how words and phrases relate to each other within a sentence. Although ChatGPT is able to
detect patterns in the use of words and phrases in context and to use this information
to make educated guesses about the meaning of a sentence or phrase, it still approaches
semantic understanding through statistical analysis.20 For this reason, ChatGPT’s responses

18
See generally D Jurafsky and JH Martin, Speech and Language Processing: An Introduction to Natural Language
Processing, Computational Linguistics, and Speech Recognition (Upper Saddle River, NJ, Pearson 2009). See also
A Guadamuz, “Authors sue OpenAI for copyright infringement” (TechnoLlama, 8 July 2023) <https://www.
technollama.co.uk/authors-sue-openai-for-copyright-infringement> (last accessed 1 August 2023; explaining
how large language model are trained).
19
See TB Brown et al, “Language Models Are Few-Shot Learners” (2020) arXiv preprint <http://arxiv.org/abs/
2005.14165> (last accessed 1 August 2023; noting that despite being trained on massive amounts of data, these
models still lack true semantic understanding and instead approximate it through statistics).
20
“Syntactic understanding” refers to the ability to understand the structure and rules of language, including
grammar and syntax. “Semantic understanding”, on the other hand, refers to the ability to understand the
meaning of language, including concepts and context. See CD Manning and H Schutze, Foundations of Statistical
Natural Language Processing (Cambridge, MA, MIT Press 1999) p 3.
 European Journal of Risk Regulation 607

do not always reflect true understanding of the underlying concepts or theories related
to a particular question. ChatGPT is capable of independently stimulating dialogues
and thus has the potential to produce content protected by intellectual property,
including but not limited to articles, music lyrics, programming codes and text
translations. The results produced by ChatGPT depend on the data it was programmed
with and the computational techniques used and may not always be suitable for all
targets. While acknowledging the commendable nature of ChatGPT, it is important to
point out that its AI-powered functions are created without human intervention. Even
though AI has remarkable precision, it is not free of limitations. Therefore, it is
important that individuals – in order to prevent potential problems or errors – review
and modify the designs to ensure that they meet established standards for accuracy
and efficiency for specific usage scenarios.

III. An analysis of the ChatGPT case study: the question of the output
The emergence and widespread application of AI systems in the creative sectors have
raised concerns about the rightful ownership of intellectual property and the
protection of copyright. To gain a more comprehensive understanding of the above
issues, our research has specifically focused on ChatGPT, a language acquisition model
developed by OpenAI. ChatGPT – as mentioned earlier – is a generative AI tool. A full
understanding of the complexity of the evolution and ownership of the ideas created
by AI can be achieved by evaluating the results and sharing observations about the
interaction between humans and computer systems. This emphasises the importance
of a recasting of legal rules in order to adequately deal with this set of challenges. In
this perspective, the ChatGPT case study can be used to initiate a debate about the legal
and ethical implications of rapid technological advancements, as well as the concerns
associated with the application of AI in the creative industries. The use of generative
techniques such as ChatGPT raises significant issues regarding intellectual property,
authorship and the scope of copyright protection for material created with generative
AI systems. These aspects have attracted considerable attention in the legal field.21 The
primary inquiry relates to the need to determine the rightful owner of copyright in
content generated by AI, whether a natural person or a legal entity. We attempted to
ask ChatGPT directly about this question, and the platform provided us with the
following response:
https://doi.org/10.1017/err.2023.59 Published online by Cambridge University Press

As an AI language model, I do not own the copyright of the text generated with my
help. The ownership of the text belongs to the user who inputs the prompts and
generates the output.22

Of course, the question of who owns the content developed by ChatGPT is more
complicated and may require further clarification or reference to be fully answered.
Because ChatGPT is an AI system that generates results based on training data and user
input, it is difficult to identify specific authors. Authorship of AI-generated content may
depend on a variety of factors, including the purpose of the content, the intent of the user
and the legal framework at the time.
In the case of ChatGPT, the author of the content may be based around the individual
who created the prompt or input for the response. If the user provides the input, they can
take ownership of the output. If the input comes from ChatGPT’s training data or other
21
See supra, note 12.
22
K Anderson, “ChatGPT says it’s not an author” (The Geyser, 13 January 2023) <https://www.the-geyser.com/
chatgpt-says-its-not-an-author/?ref=the-geyser-newsletter> (last accessed 1 August 2023).
 608 Nicola Lucchi

sources, it may be more difficult to identify the owner. Usually, the copyright owner of
texts created with tools such as ChatGPT is the person or organisation that provided the
original ideas and data on which the system is based or the person who creatively
implemented the instructions in the prompt.
As an AI language model, the text generated is not, per se, protected by copyright law,
as copyright law generally recognises the human creator of an original work as the
copyright owner. However, in some cases, the text generated may be considered original
enough to be protected by copyright if it was created with sufficient human input or
intervention. For example, the resulting work could be deemed sufficiently unique to be
protected by copyright law if someone uses the replies as a starting point and then adds
significant creative or original content, such as editing, adding commentary or analysis or
merging it into a bigger work. The individual who added the extra creative or original
content in this scenario would normally own the copyright for the final product.
AI’s inability to hold copyright stems from its legal identity or status as a non-human
entity.23 While the Berne Convention and other international copyright regulations do not
require human authorship, many countries, such as the USA and those in the EU, place
importance on the presence of a human being as the creator of a work.24 In addition,
copyright law itself adopts a predominantly anthropocentric approach, as exemplified by
the copyright term “70 years after the calendar year in which the author of the work died”.
This term inherently assumes that the author is a human being, subject to mortality.
Original pieces of art produced with AI assistance or by automated means are not novel
occurrences. Some might argue that what we are witnessing with AI systems is simply the
repetition of history. After all, copyright laws have always had to evolve and keep pace
with emerging technologies and their effects on society. This pattern can be observed, for
instance, with the arrival of photography, motion pictures, computer programs and
various other novel forms of creative expression.25 A historical example of this is the well-
known case in Burrow-Giles v. Sarony in 1885,26 which was argued before the US Supreme
Court. At issue was whether a photograph could be considered a copyrighted work, given
that the image was created by a camera rather than a human being. In its ruling, the Court
held that the photographer, who was the person behind the camera, was the author of the
photograph and therefore had exclusive copyright over it. This rationale persisted even in

23
A Guadamuz, “Artificial Intelligence and Copyright” (World Intellectual Property Office Magazine, October 2017)
<https://www.wipo.int/wipo_magazine/en/2017/05/article_0003.html> (last accessed 1 August 2023; listing
https://doi.org/10.1017/err.2023.59 Published online by Cambridge University Press

many jurisdictions requiring human authorship for copyright protection).

24
See, eg, Case C-5/08 Infopaq International A/S v Danske Dagblades Forening; Case C-145/10 Eva-Maria Painer v
Standard Verlags GmbH and Others (emphasising that a work achieves the originality criterion when it contains a
“intellectual production” with the author’s own creative visible touch). Similarly, in the US – shortly after Naruto
v. Slater, No. 16-15469 (9th Cir. 2018) – the Compendium of US Copyright Practices was updated to provide that “the US
Copyright Office will register an original work of authorship, provided that the work was created by a human
being” and “will refuse to register a claim if it determines that a human being did not create the work.” See US
Copyright Office, Compendium of US Copyright Office Practices section 306, 3rd edition (as emended 2021). See also the
recent rejection of the US Copyright Office to register the copyright of a picture generated by an AI: “Second
Request for Reconsideration for Refusal to Register a Recent Entrance to Paradise” (Correspondence ID 1-3ZPC6C3;
SR # 1-7100387071), U.S. Copyright Off. Rev. BD. 1–2 (14 February 2022) <https://www.copyright.gov/rulings-
filings/review-board/docs/a-recent-entrance-to-paradise.pdf> (last accessed 1 August 2023). After receiving the
Office’s final rejection, legal action was taken, resulting in a lawsuit against the US Copyright Office. In a very
recent development, the US District Court for the District of Columbia ruled on the case. The court’s decision, as
seen in Thaler v. Perlmutter, No. 22-1564 (D.D.C. Aug. 18, 2023), declared that AI-created works are ineligible for
protection under US copyright law.
25
See eg B Sherman and L Wiseman (eds), Copyright and the Challenge of the New (Alphen aan den Rijn, Kluwer
Law International 2012) p 1 (noting that “one of the most challenging things about copyright law is that it is
constantly subject to change”).
26
See Burrow-Giles Lithographic Co. v. Sarony, 111 U.S. 53 (1884).
 European Journal of Risk Regulation 609

cases where the machine was responsible for most of the work, since it was recognised that
human input was necessary and indispensable for the creation of the work.27 The UK is the
only country where the concept of “computer-generated works” is recognised in domestic
law, and it seeks to address the issue in a practical way by expanding the concept of
authorship. Section 9(3) of the Copyright, Designs and Patents Act 1988 (CDPA) provides
that the person who makes the necessary arrangements for the creation of the work shall
be deemed to be the author.28 However, determining who is the “arranger” is not always
easy and often needs to be determined on a case-by-case basis. In particular, according to
contemporary standards for defining “computer-generated” works, the UK approach can
be considered quite outdated.29 These provisions became law in 1988, and the AI systems
available today are vastly different from the computer systems that existed at that time.
Moreover, given the complexity of modern AI programming, there is considerable
uncertainty in determining which party is responsible for the “arrangements necessary for
the creation of the work”.30
Being an AI language model, ChatGPT also lacks legal identity and the ability to possess
property or assets in the conventional sense because it is not a human. Even if the content
created by an AI language model is original and creative enough to be protected by
copyright law, the AI will not own it. According to the various jurisdictions mentioned, the
copyright for the material could belong to the individual or entity that has legal authority
over the AI, such as the AI system’s developer or owner. In some instances, the content’s
copyright may belong to the human users who contributed to or edited the AI-
generated work.
The practical approach, then, is to grant copyright to the people behind the machines,
namely the programmer, the user and the owner. These key actors are the human or
human-owned entities behind the process of AI production and, accordingly, the actors at
the centre of the legal discussion about copyright in AI-generated works. On the other
hand, if we consider only the statutory and common law understanding of the doctrine of
originality and the requirement of human authorship, there is indeed no copyright in
works created by AI, and copyright-free works naturally belong to the public domain.

IV. An analysis of the ChatGPT case study: the question of originality

The issue of authenticity of the output of AI generative systems presents another challenge
and variation, especially with respect to ChatGPT. While chatbots are excellent at
https://doi.org/10.1017/err.2023.59 Published online by Cambridge University Press

generating responses that engage humans in conversation, these responses run the risk of
being unoriginal, completely invented and simply repeating information from the past.
The use of chatbots and generative tools for content creation can lead to problems,
especially in cases where the resulting output requires distinctiveness and appeal.

27
See R Yu, “The Machine Author: What Level of Copyright Protection Is Appropriate for Fully Independent
Computer-Generated Works?” (2017) 165 University of Pennsylvania Law Review 1245, 1253.
28
See § 9(3) of the UK Copyright, Designs and Patents Act 1988. For a more detailed discussion, see E Bonadio
et al, “Will Technology-Aided Creativity Force Us to Rethink Copyright’s Fundamentals? Highlights from the
Platform Economy and Artificial Intelligence” (2022) 53 International Review of Intellectual Property and
Competition Law 1174, 1187.
29
See Intellectual Property Office (UK), “Artificial intelligence call for views: copyright and related rights” (UK
Government, 2020) <https://www.gov.uk/government/consultations/artificial-intelligence-and-intellectual-property-
call-for-views/artificial-intelligence-call-for-views-copyright-and-related-rights> (last accessed 1 August 2023).
30
For some additional critical comments on this provision, see PB Hugenholtz and JP Quintais, “Copyright and
Artificial Creation: Does EU Copyright Law Protect AI-Assisted Output?” (2021) 52 International Review of
Intellectual Property and Competition Law 1190, 1211 (noting that “since the introduction of the regime on
computer-generated works in UK law in 1988, this has led to just a single court decision, which has not clarified
this issue”).
 610 Nicola Lucchi

Intellectual property ownership is closely related to the uniqueness of AI chatbots. As AI

production processes become more sophisticated and generate content that closely
resembles human-created content, it is important to establish clear guidelines and rules
for creation and submission. The significance of this matter is particularly salient in
domains such as journalism and creative writing, given that the utilisation of AI-generated
content can engender ethical and legal dilemmas. Typically, the degree to which creative
works are safeguarded is contingent upon their level of uniqueness. While the Berne
Convention does not expressly mandate the condition of “originality” for copyrighted
works, several nations enforce this prerequisite.31 As a consequence, the originality
requirement is a prerequisite for the granting of copyright protection to literary, dramatic,
musical and artistic works. Currently, the prevailing approach to determining whether a
work is original is by evaluating whether it is “the author’s own intellectual creation”.32
This means that the work must have an intellectual content that goes beyond the mere
combination of its individual parts, taking into account the overall impression. However,
the standard for originality varies across different jurisdictions. For instance, US law
adopts the “minimal degree of creativity” test, which was established in the Feist v. Rural
case,33 while the EU requires that the work be an author’s own “intellectual creation”.34
But the issue of originality becomes more nuanced when considering content created by
AI. Copyright laws may apply to certain AI-generated material if it is made with sufficient
human input or participation. An AI chatbot may be regarded to have created an original
work under copyright law if a human offers input or instruction to the bot to create a
particular work, such as a tale or a song, and the bot then develops the final output based
on that input. Some AI-generated content, however, may be less unique than expected and
more derivative or based on previously published works. For example, if AI-generated
systems merely reproduce data or existing information without adding significant ideas or
original content, the output they create cannot be considered unique enough to be
copyright protected. By applying the US “minimal degree of creativity” test for originality,
which sets a low bar for copyright protection, one could argue that ChatGPT’s output
meets this standard. This is because ChatGPT utilises sophisticated NLP techniques to
generate text that is not merely a repetition of its input data, indicating some level of
creativity. However, under the EU’s standard for originality, AI-generated works may not
qualify, as they lack the creative choices and personal expression of a human author.
ChatGPT, as an AI language model, has undergone extensive training using vast
amounts of textual data gathered from diverse online and offline sources. By leveraging
these training data, it generates responses to user queries encompassing a broad spectrum
https://doi.org/10.1017/err.2023.59 Published online by Cambridge University Press

of subjects, ranging from common knowledge to specialised fields. When presented with a

31
See E Bonadio and N Lucchi, “Introduction: Setting the Scene for Non-Conventional Copyright” in E Bonadio
and N Lucchi (eds), Non-Conventional Copyright: Do New and Atypical Works Deserve Protection? (Cheltenham, Edward
Elgar Publishing 2018) p 6.
32
The Court of Justice of the European Union (CJEU) provided that in order to pass the threshold for copyright
protection, a “work” needs to be “original” in the sense that it is the author’s “own intellectual creation”. See Case
C-05/08, Infopaq International v. Danske Dagblades Forening (2009) ECLI:EU:C:2009:465 (Infopaq). This ruling has been
reaffirmed in subsequent CJEU cases, including Levola Hengelo, Funke Medien, Cofemel and Brompton Bicycle. See the
following case references for more information: Case C-310/17 (Levola Hengelo); Case C-469/17 (Funke Medien NRW
GmbH v. Bundesrepublik Deutschland (2019) ECLI:EU:C:2019:623); Case C-683/17 (Cofemel - Sociedade de Vestuário SA v.
G-Star Raw CV (2019) ECLI:EU:C:2019:721); and Case C-833/18 (SI and Brompton Bicycle Ltd v. Chedech/Get2Get (2020)
ECLI:EU:C:2020:461).
33
See Feist Publications, Inc., v. Rural Telephone Service Co., 499 U.S. 340 (1991).
34
See Case C-5/08, Infopaq Int’l A/S v. Danske Dagblades Forening, 2009 E.C.R I-6569 (setting out the EU originality
standard for copyright protection). According to the court, copyright can only protect the author’s own
intellectual creativity, which reflects their personality and has a certain originality. It states that originality is
characterised by the author’s individuality and expression and that the work must reflect the author’s personal
touch and creativity.
 European Journal of Risk Regulation 611

question, ChatGPT examines the overall context and relevant keywords to formulate a
response based on learned relationships and patterns derived from the training data.
These responses are algorithmically generated and do not rely on the respondent’s
personal opinions or experiences. It is important to note again that ChatGPT establishes
statistical correlations between words without genuine comprehension of the underlying
meaning. The tool excels at producing high-quality written content across various
domains, saving considerable time compared to human effort, thanks to its extensive
database and syntactic correlation capabilities. However, due to the absence of human
authorship, ChatGPT lacks the necessary human creative input required to substantiate a
copyright claim.35 In copyright law, in fact, the act of creating a copyrightable work is
typically associated with human creativity and authorship. Therefore, if there is no human
involvement in the creation process, there is a lack of originality, and consequently the
work may not be eligible for copyright protection. The concept of personhood is crucial in
this context, as it distinguishes between entities with naturalistic dimensions of life and
self-awareness and those that do not possess these attributes. Robots or other AI
technologies, regardless of their level of autonomy, cannot be considered as persons under
ethical and legal frameworks. The qualification of personhood plays a significant role in
copyright law as it serves as a boundary for attributing creative authorship and the
associated rights. This distinction is based on the understanding that copyright protection
is intended to incentivise and reward the unique and subjective contributions of human
creators.
While robots and AI systems can generate content or imitate human-like behaviours,
they lack the essential qualities that define personhood, such as consciousness,
intentionality and the capacity for subjective experience. These intrinsic limitations
prevent us from assimilating them into the category of persons within ethical and legal
contexts.
Therefore, it remains firmly established that copyright protection requires a human
element, where the creative efforts and expression originate from individuals possessing
the characteristics and attributes inherent to personhood.
In light of this analysis, it can be deduced that in order for an AI system to fully replace a
human author, it would require the capacity to independently conceptualise and complete
a creative work without relying on explicit training or pre-programmed instructions.36 As
technology continues to progress, it is reasonable to envisage a gradual reduction in
human involvement in the creative process, leading to the emergence of new artistic
creations that cannot be attributed to a specific or recognised artist.37 While the current
https://doi.org/10.1017/err.2023.59 Published online by Cambridge University Press

capabilities of ChatGPT may not align with this vision, a completely revolutionary future
appears to be within reach.

V. An analysis of the ChatGPT case study: the question of the input

At present, the intellectual property discourse pertaining to AI predominantly revolves
around the issues regarding the authorship and creative ownership of the outcomes
35
There is a common consensus on this: recently, the US Copyright Office has issued an official policy
declaration that firmly denies the registration of copyright for works produced entirely by AI without any human
involvement. Copyright Office, “Copyright Registration Guidance: Works Containing Material Generated by
Artificial Intelligence” (16 March 2023) 37 CFR Part 202 <https://www.govinfo.gov/content/pkg/FR-2023-03-16/
pdf/2023-05321.pdf> (last accessed 1 August 2023).
36
See JC Ginsburg and LA Budiardjo, “Authors and Machines” (2019) 34 Berkeley Technology Law Journal 343.
37
See, eg, RB Abbott and E Rothman, “Disrupting Creativity: Copyright Law in the Age of Generative Artificial
Intelligence” Florida Law Review, forthcoming, available at SSRN <https://ssrn.com/abstract=4185327> (last
accessed 1 august 2023; claiming that AI should be recognised as an author when it performs tasks equivalent to
human authors).
 612 Nicola Lucchi

generated by AI systems. Despite the ongoing discourse, there appears to be a significant

gap in the examination of the legal questions that emerge in the management of
intellectual property rights pertaining to the inputs, namely the data employed in the training
of these AI systems.38 The second and more fundamental question to be addressed here is
whether the use of copyrighted material to train generative AI programs represents an
infringement of copyright. Indeed, machine learning heavily relies on vast amounts of training
data to achieve accurate results, including in facial recognition, stop sign recognition, natural
language recognition and translation generation. This is especially important when it comes to
ChatGPT because it relies on large amounts of training data being fed into the system.39 In
order to create interactive and authentic articles, ChatGPT needs to ingest information,
including text, images and other content, from publicly available websites on the Internet.
To facilitate the training of AI algorithms, various techniques are used, including text
and data mining (TDM)40 as well as generative deep learning techniques.41 TDM processes
involve the extraction and analysis of vast amounts of data to identify meaningful insights
and patterns, which can then be leveraged to improve the performance of AI models.42
TDM has become an essential tool in the field of AI, enabling researchers and data
scientists to explore vast amounts of unstructured data and extract valuable information
that would otherwise be impossible to obtain manually. By analysing these vast amounts of
data, AI algorithms can learn from these patterns and make predictions with a high degree
of accuracy, facilitating the creation of content, discoveries and innovations. So, without
access to large volumes of data, AI algorithms would struggle to “learn” and improve their
performance. Therefore, it is clear that the future of AI hinges on TDM and its capacity to
extract and analyse data on a large scale. However, a significant challenge lies in the fact
that AI systems cannot learn from art in the same way humans do, since they require an
exact copy of the artwork in their training dataset.43 This necessitates the creation of a

38
Scientific interest in this topic is growing among legal scholars: see, eg, A Strowel, “ChatGPT and Generative
AI Tools: Theft of Intellectual Labor?” (2023) 54 International Review of Intellectual Property and Competition
Law 491; G Franceschelli and M Musolesi, “Copyright in generative deep learning” (2022) 4 Data & Policy e17; E
Bonadio et al, “Can Artificial Intelligence Infringe Copyright? Some Reflections”, in R Abbott (ed.), Research
Handbook on Intellectual Property and Artificial Intelligence (Cheltenham, Edward Elgar 2022); J Quang, “Does Training
AI Violate Copyright Law?” (2021) 36 Berkeley Technology Law Journal 1407; B Sobel, “A Taxonomy of Training
Data: Disentangling the Mismatched Rights, Remedies, and Rationales for Restricting Machine Learning”, in R
Hilty et al (eds), Artificial Intelligence and Intellectual Property (Oxford, Oxford University Press 2021) pp 221–42; G
Abbamonte, “The rise of the artificial artist: AI creativity, copyright and database right” (2021) 43 European
https://doi.org/10.1017/err.2023.59 Published online by Cambridge University Press

Intellectual Property Review 702; MA Lemley and B Casey, “Fair Learning” (2021) 99 Texas Law Review 743; P
Keller, “Protecting creatives or impeding progress? Machine learning and the EU copyright framework” (Kluwer
Copyright Blog) <https://copyrightblog.kluweriplaw.com/2023/02/20/protecting-creatives-or-impeding-progress-
machine-learning-and-the-eu-copyright-framework/> (last accessed 1 August 2023); J Vincent, “The scary truth
about AI copyright is nobody knows what will happen next” (The Verge, 15 November 2022) <https://www.theverge.
com/23444685/generative-ai-copyright-infringement-legal-fair-use-training-data> (last accessed 1 August 2023); CJ
Craig, “The AI-Copyright Challenge: Tech-Neutrality, Authorship, and the Public Interest” (14 December 2021)
Osgoode Legal Studies Research Paper <https://ssrn.com/abstract=4014811> (last accessed 1 August 2023);
R Abbott (ed.), Research Handbook on Intellectual Property and Artificial Intelligence (Cheltenham, Edward Elgar 2022).
39
See Brown et al, supra, note 19; A Radford et al, “Language Models Are Unsupervised Multitask Learners”
(2019) 8 OpenAI Blog 1 <https://insightcivic.s3.us-east-1.amazonaws.com/language-models.pdf> (last accessed
1 August 2023); J Devlin et al, “BERT: Pre-Training of Deep Bidirectional Transformers for Language
Understanding” (2018) ArXiv:1810.04805 [Cs], arXiv.org <http://arxiv.org/abs/1810.04805> (last accessed
1 August 2023).
40
See E Alpaydın, Introduction to Machine Learning (Cambridge, MA, MIT Press 2004) p 2 (explaining that TDM is
an essential tool for machine learning and data mining, particularly in cases where the data are too numerous or
too complex for humans to analyse manually).
41
Franceschelli and Musolesi, supra, note 38.
42
Alpaydın, supra, note 40.
43
Lemley and Casey, supra, note 38, 775.
 European Journal of Risk Regulation 613

training set of millions of examples by making copies of copyrighted images, videos, audio
or text-based works. Consequently, the question of whether machine copying should fall
under fair use or other copyright exceptions arises. On the other hand, we have generative
deep learning, a specialised branch of deep learning that focuses primarily on the task of
generating novel data.44 Generative models are crucial in this domain as they provide a
probabilistic framework for describing the data generation process.45 By harnessing these
models, it becomes possible to generate new data samples through the process of
sampling. These techniques employ deep neural networks, which are artificial neural
networks with multiple layers, to learn and replicate the patterns, structures and
statistical properties present in the training data. ChatGPT is precisely a form of
generative deep learning technique that harnesses the power of deep learning models,
particularly the GPT (Generative Pre-trained Transformer) architecture.
The problem here is that established companies such as Google, Facebook, Amazon and
OpenAI have access to large collections of language and image data, which they can use for
AI purposes.46 Access to large collections of language and image data can be also
considered a competitive advantage in the field of AI.47 As a consequence, these companies
can leverage their existing datasets to train and develop more advanced AI models, which
in turn can improve their products and services. This can create a legal problem for new
entrants because the ownership and licensing of datasets can be complex and subject to
intellectual property rights, privacy regulations and other legal considerations.48
Additionally, the cost of building or licensing a dataset from scratch can be prohibitive,
making it difficult for smaller companies to compete with established players.
Moreover, there may also be antitrust concerns if the dominant players in the market
control access to the datasets needed to develop AI models, as this could potentially stifle
innovation and competition. Therefore, ensuring fair and open access to training data is a
critical legal issue in the development and deployment of AI technology.
Another issue with input data is that while some large datasets are merely
informational and not protectable, the majority of training datasets consist of copyrighted
works. For instance, the corpus of works used to develop AI algorithms for text, facial
recognition and image recognition all include copyrighted works. Thus, the question arises
as to whether using these works is lawful and under what circumstances.
Currently, data collection for TDM has been considered fair use in the USA,49 and there
are exceptions and limitations under EU copyright law.50 Specifically, in the USA, Google
Books was granted permission to search entire libraries to provide search functions and
https://doi.org/10.1017/err.2023.59 Published online by Cambridge University Press

44
I Goodfellow et al, Deep Learning (Cambridge, MA, MIT Press 2016).
45
D Foster, Generative Deep Learning: Teaching Machines to Paint, Write, Compose and Play (Newton, MA, O’Reilly
2019) p 1 (stating that “a generative model can be broadly defined as a generative model describing how a dataset
is generated, in terms of a probabilistic model. By sampling from this model, we are able to generate new data”).
46
ibid, at 66 (illustrating how some companies have an advantage in the AI space because they are capable of
using larger sets of training data to improve their algorithms, resulting in better performance. This gives them a
“privileged zone” compared to other companies).
47
ibid.
48
See, eg, J Vesala, “Developing Artificial Intelligence-Based Content Creation: Are EU Copyright and Antitrust
Law Fit for Purpose?” (2023) 54 International Review of Intellectual Property and Competition Law 351.
49
See MW Carroll, “Copyright and the Progress of Science: Why Text and Data Mining Is Lawful” (2019) 53 UC
Davis Law Review 893, 894 (arguing that fair use allows a TDM researcher to create non-transitory copies during
processing and to preserve the processed data for archival purposes due to the transformative and beneficial
nature of TDM). See also Lemley and Casey, supra, note 38, 746 (questioning whether machine copying will
continue to be treated as fair use).
50
See Art 4 of Directive (EU) 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,
Official Journal of the European Communities 2019 L 130, 92.
 614 Nicola Lucchi

excerpts from books.51 However, it is unclear whether these conclusions apply to data
collection and input for machine learning, as there is no copyrightable output. Indeed, it
cannot be guaranteed that courts will apply this precedent to comparable technologies.52
In the USA, data collection for TDM may be permissible if it is a transformative use,53 but it
is not immediately clear that a copyrighted work is being transformed into another
copyrighted work. In addition, in the Google Books case, the court recognised that Google’s
digitisation of copyrighted books, undertaken for the purpose of creating an extensive
index and facilitating search functionality, constituted fair use.54 This digitisation process
was specifically designed to enhance users’ ability to locate and access copyright owners’
books, providing an invaluable tool for researchers, scholars and the general public. In this
context, it is important to note that Google did not intend to compete with or replace the
original works, but rather to improve their discoverability and enable consumers to make
informed decisions about purchasing or accessing the entire works. On the other hand,
when we examine generative AI technology, we encounter a contrasting scenario.
Generative AI systems have the potential to empower users to easily produce content that
may directly compete with the original ingested material. These systems utilise algorithms
and machine learning techniques to generate new works, such as texts, images or music,
based on the patterns and information gathered from existing content. Unlike Google’s
indexing and search functionality, which primarily served as a tool for information
retrieval, generative AI opens the door for the creation of derivative works that could
potentially overshadow or undermine the market for the original content. So, while
Google’s efforts in the Google Books case were found to align with fair use principles, the
ease and accessibility of generative AI introduce complexities and challenges regarding
copyright protection. Exactly for this reason, numerous court cases are currently
underway in the USA seeking to clarify the definition of a “derivative work” and
“transformative use” under intellectual property law, particularly with respect to
copyrighted material used to train AI systems.55 In particular, OpenAI and other prominent
generative AI platforms are currently facing lawsuits alleging copyright infringement for
training AI systems with illegally acquired datasets.56 Specifically, in the legal case of
Tremblay v. OpenAI Inc.,57 the plaintiffs assert that OpenAI employed their copyrighted
books without obtaining proper authorisation in order to train ChatGPT. The assertion is
made that ChatGPT possesses the ability to effectively condense the content of various
books, thereby implying that the chatbot has comprehensively engaged with and
assimilated the information contained within said literary works. In the case of Silverman
et al. v. OpenAI Inc., the plaintiffs assert that OpenAI engaged in unauthorised utilisation of
https://doi.org/10.1017/err.2023.59 Published online by Cambridge University Press

copyrighted work, specifically the book titled The Bedwetter, for the purpose of training
ChatGPT.58 Specifically, the authors of this class action claim that ChatGPT is capable of
producing summaries of their novels when provided with a suitable prompt. They base this

51
See Authors Guild v. Google, Inc., 804 F.3d 202, 214–15 (2d. Cir. 2015) (Google was authorised by the court to
digitise all books available on the market, which served as an initial step towards creating a book search system
that could provide exact excerpts of copyrighted text to users).
52
Lemley and Casey, supra, note 38, 763.
53
A transformative use is one that “alter[s] the first [work] with new expression, meaning, or message”. See
Campbell v. Acuff-Rose Music, Inc., 510 U.S. 569, 579 (1994).
54
See Authors Guild, 770 F. Supp. 2d at 207–08.
55
See, eg, Getty Images (US), Inc. v. Stability AI, Inc., No. 1:23-cv-00135-GBW (D. Del. Mar. 29, 2023); Silverman et al. v.
OpenAI, Inc. et al., No. 4:23-cv-03416 (N.D. Cal. Jul. 7, 2023); Tremblay et al. v. OpenAI, Inc. et al., No. 4:2023-cv- 03223
(N.D. Cal. Jul. 7, 2023). It is expected that the resolution of these litigations will depend on the interpretation of the
fair use doctrine.
56
ibid.
57
See Tremblay et al. v. OpenAI, Inc. et al., No. 4:2023-cv- 03223 (N.D. Cal. Jul. 7, 2023).
58
See Silverman et al. v. OpenAI, Inc. et al., No. 4:23-cv-03416 (N.D. Cal. Jul. 7, 2023).
 European Journal of Risk Regulation 615

claim on the fact that the AI tool has been trained using their copyrighted material,
thereby establishing its familiarity with the content. Finally, in the dispute Getty Images Inc.
v. Stability AI, the famous photo agency alleges that the software developer responsible for
the AI art tool known as Stable Diffusion engaged in the unauthorised scraping of a
substantial number of its images.59 This act was purportedly carried out for the purpose of
training the aforementioned system without obtaining proper permission or providing
compensation to Getty Images. In addition, the AI tool Stable Diffusion generated a
modified rendition of Getty’s watermark, with the purpose of promoting, facilitating or
concealing the infringement of Getty Images’ copyright. This action – according to the
plaintiff – also constitutes a violation of the Digital Millennium Copyright Act (DMCA)
regulations regarding copyright management information.60 Getty Images has also filed a
similar complaint in the UK, requesting the High Court of London to issue an injunction
barring Stability AI from selling its AI image generation technology in the country.61
The resolutions of all of these cases remain pending, and the manner in which they will
be resolved remains uncertain at present. Nevertheless, these cases mark the initial
significant legal confrontations regarding the utilisation of AI in relation to copyright
violation. If the plaintiffs achieve a favourable outcome, they have the potential to exert a
substantial influence on the advancement of AI technology.
However, the US Supreme Court’s recent ruling in a non-technological case has already
raised concerns about potential adverse implications on the intellectual property rights of
works generated by AI.62 This case seems to have shifted the focus of the transformative
use assessment. The controversy pertains to a conflict concerning copyright infringement,
specifically regarding the utilisation of a photograph featuring the musician Prince that
was taken in 1981.63 The photograph was subsequently incorporated by the artist Andy
Warhol in a series of prints and illustrations without obtaining the photographer’s
authorisation.64 The fair use doctrine was invoked by the Andy Warhol Foundation for the
Visual Arts to justify the creation of derivative works. In this context, the US Supreme
Court ruled that the Foundation lacked a fair use defence to license a derivative rendition
of the photograph for commercial purposes.65 This recent decision could potentially result
in a significant restriction of the transformative use doctrine, given that the Supreme
Court appears to have effectively limited its scope.66 So, it will be interesting to see what
happens when US courts have to use the rules set up in this case to judge the licensing of AI
training input. In the event that a court determines that data ingestion – which involves
acquiring unprocessed data from one or more sources and modifying them to render them
appropriate for the purpose of training AI machines – constitutes an act of infringement,
https://doi.org/10.1017/err.2023.59 Published online by Cambridge University Press

the entire AI system may encounter significant legal difficulties. In fact, the vast majority
of data that generative AI systems have assimilated – including both textual and visual
content – have been de facto obtained without the express authorisation of the rights

59
See Getty Images (US), Inc. v. Stability AI, Inc., No. 1:23-cv-00135-GBW (D. Del. Mar. 29, 2023).
60
See Section 1202(b) of the Digital Millennium Copyright Act, 17 U.S.C.A. § 1202(b). Specifically, the plaintiffs
argued that the defendant’s actions violated the provisions of the Digital Millennium Copyright Act (DMCA)
because it altered or removed copyright management information (CMI) embedded in the plaintiffs’ images and
instructed the AI system to exclude any CMI from its generated output.
61
S Tobin, “Getty asks London court to stop UK sales of Stability AI system” (Reuters, 1 June 2023) <https://
www.reuters.com/technology/getty-asks-london-court-stop-uk-sales-stability-ai-system-2023-06-01/> (last
accessed 1 August 2023).
62
See Andy Warhol Foundation for the Visual Arts, Inc. v. Goldsmith, 143 S.Ct. 1258 (2023).
63
ibid, p 1.
64
ibid, p 1.
65
ibid, p 2.
66
See W Patry, “Andy Warhol Foundation for the Visual Arts, Inc. v Goldsmith: did the U.S. Supreme Court tighten up
fair use?” (2023) 18 Journal of Intellectual Property Law & Practice jpad060.
 616 Nicola Lucchi

holders. Consequently, here the question at hand pertains to the potential copyright
infringement that may arise from utilising copyrighted works as training data. Specifically,
it is necessary to determine whether such usage automatically constitutes copyright
infringement or whether the distinct purpose of training data sufficiently diverges from
that of the original copyrighted works, thereby warranting a fair use defence.
In contrast to the USA, the EU adopts a protectionist stance and has established a degree
of accountability for the utilisation of training data. Specifically, the Directive on
Copyright in the Digital Single Market (CDSM Directive)67 includes Article 4(1), which
provides a broad exception for TDM. Under this provision, individuals such as commercial
AI system developers and educators may make copies of works or databases for the
purpose of extracting information from text and data. They may retain these copies for as
long as they are needed for the AI training process.68 However, rights holders have the
option to exclude TDM exemptions from their contracts with miners (ie entities or
individuals that engage in TDM activities) in order to safeguard their commercial
interests.69 This particular provision has met with considerable criticism for providing a
copyright exception that is perceived as being too restrictive. In contrast to the traditional
understanding of copyright, which generally focuses on the protection of original
expression, this provision appears to include factual information and data, and this aspect
has drawn much criticism.70 However, the manner in which this opt-out option can be
implemented and the extent to which AI developers will adhere to it are still to be
determined.
An additional issue associated with data aggregation pertains to the implementation of
EU data protection legislation.71 Indeed, the process of data aggregation is of paramount
importance in the training and refinement of generative AI models. This entails the
gathering and merging of substantial quantities of data from diverse origins to augment a
model’s proficiency and functionalities. The processing of personal data within the EU is
subject to stringent requirements and limitations, as stipulated by the General Data
Protection Regulation (GDPR).72 These challenges remain unexplored in both doctrine and
policy and need to be further explored and resolved.

VI. Source attribution and other copyright challenges in language models

ChatGPT’s input as a language model comes from a variety of sources, including books,
essays, websites and social media posts. These sources may contain copyrighted works that
https://doi.org/10.1017/err.2023.59 Published online by Cambridge University Press

are used to train the language processing algorithms in ChatGPT. Given the legal concerns
67
Directive (EU) 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, Official Journal of
the European Communities 2019 L 130, 92.
68
See Arts 4(1) and (2).
69
See Art 4(3) providing that “[t]he exception or limitation provided for in paragraph 1 shall apply on condition
that the use of works and other subject matter referred to in that paragraph has not been expressly reserved by
their rightholders in an appropriate manner, such as machine-readable means in the case of content made
publicly available online”. See also Bonadio et al, supra, note 38, 54.
70
See T Margoni and M Kretschmer, “A Deeper Look into the EU Text and Data Mining Exceptions:
Harmonisation, Data Ownership, and the Future of Technology” (2022) 71 GRUR International 685. As to the role of
the idea/expression dichotomy in the generative AI debate, see also Lemley and Casey, supra, note 38.
71
See generally P Hacker et al, “Regulating ChatGPT and Other Large Generative AI Models” in Proceedings of the
2023 ACM Conference on Fairness, Accountability, and Transparency, 1112–23 (New York, Association for Computing
Machinery 2023) <https://doi.org/10.1145/3593013.3594067> (last accessed 1 august 2023).
72
Regulation (EU) 2016/679 of the European Parliament and of the Council of 27 April 2016 on the protection of
natural persons with regard to the processing of personal data and on the free movement of such data, and
repealing Directive 95/46/EC (General Data Protection Regulation) (Apr. 27, 2016), Art 99(2), 2016 O.J. (L 119)
<https://eur-lex.europa.eu/eli/reg/2016/679/oj> (last accessed 1 August 2023).
 European Journal of Risk Regulation 617

surrounding copyright and the use of training data for machine learning, it is likely that
ChatGPT faces similar issues. The dilemma, as with other AI systems, is whether using
copyrighted material to train ChatGPT’s language processing algorithms is legal and under
what conditions. Because ChatGPT and other generative AI systems rely heavily on large
amounts of training data, which may include copyrighted works, this presents a significant
legal hurdle. The input data utilised by ChatGPT are produced via a method referred to as
“training”. During the training phase, the model is presented with a vast corpus of textual
data, which are employed to instruct the speech-processing algorithms. The corpus under
consideration exhibits the capacity to encompass a diverse range of text-based sources,
including but not limited to books, articles, websites, social media posts and analogous
materials. The type of data employed to furnish instructions to ChatGPT is dependent on
the particular task or use case for which the model has been trained. If ChatGPT is directed
to address customer service inquiries in a particular language, the training data corpus
employed could be sourced from transcriptions of customer conversations or online
evaluations. To guarantee adherence to copyright regulations, it is important to acquire
any information utilised for the dissemination of ChatGPT through lawful channels. This
may involve obtaining permission to use copyrighted materials or accessing publicly
available information. Under specific circumstances, fair use or other legal exemptions
may be relevant. However, it is essential to note that this is a multifaceted and dynamic
field of law that necessitates meticulous examination on a per-case basis.
It is relevant to bear in mind that the programmers accountable for the development
and training of ChatGPT hold the responsibility for ensuring that the training data remain
free from any copyright violations. The provision of a comprehensive list of data sources
may not be practical; however, OpenAI could explore more transparent options for
disclosing the origins of the training data it employs. This could involve specifying the
sources utilised or outlining the methodologies employed to gather and evaluate the data.
This could reduce concerns about potential copyright infringement and improve
transparency in the creation of AI models.
An intelligent individual writing creative content must provide a list of sources to prove
the validity of their work and to avoid plagiarism. Why are ChatGPT language models
exempt from this requirement? The answer is quite evident: language models lack
personal convictions and the capacity to generate authentic ideas. Their capabilities stem
from extensive training on diverse data sources, enabling them to generate texts.
Nonetheless, it is crucial for any writer, be it human or machine, to acknowledge and
reference their sources appropriately. This practice not only ensures the accuracy and
https://doi.org/10.1017/err.2023.59 Published online by Cambridge University Press

reliability of the text, but also prevents some cases of plagiarism. However, there are
significant differences in the way human writing and language models (eg ChatGPT) go
about proofreading and citing sources. Human writers often view the source as a moral
responsibility and express their accountability. They are responsible for the truth of their
claims, and citing sources is one way to support their evidence.
In contrast, the issue of citing sources is somewhat more complicated for language
models such as ChatGPT. Because these models generate text by using patterns and
structures from training data, they do not inherently “support” particular claims or ideas.
Instead, their answers are formulated based on statistical probabilities and patterns in the
data. Consequently, it should be noted that ChatGPT’s responses are not always accurate or
reflective of reality, even though they come from an extensive data corpus. Therefore, it is
important to emphasise the importance of source citations for language models.
Incorporating this practice would effectively maintain the accuracy and credibility of the
text, curb the spread of misinformation and provide transparency regarding the
legitimacy of data sources. Sometimes sources can be provided automatically by using
training data or contextual cues in the input text. In essence, the issue of source citation is
equally important for human authors and language models. Although the approach and
 618 Nicola Lucchi

timing of source citation may be different for these two groups, the basic principles of
maintaining accuracy and credibility and avoiding plagiarism remain unchanged. It is also
worth noting that there are different perspectives that challenge copyright holders’
concerns about the use of their intellectual property in generative AI systems. Differing
views arise from the fact that developers prioritise data encapsulated in copyrighted
works over actual expression.73 From the developers’ perspective, documents and creative
works are fundamentally viewed as collections of textual content, visual elements or
auditory components that serve as unprocessed inputs to computational goals. The main
goal of their research is to use the above raw material to train and extend generative AI
models. This, in turn, facilitates the development of novel content by leveraging patterns
and insights gained from existing works.
On the other hand, copyright law focuses on protecting the unique manifestation of a
creation, commonly referred to as “original expression”. This refers to the distinctive and
innovative approach authors use to convey their concepts or create visual representations,
melodies or other forms of artistic expression. It should be noted that copyright law is not
able to cover the basic data, facts and concepts contained in copyrighted materials.74
Proponents of using copyrighted materials for the purpose of training generative AI
systems contend that copyright laws do not protect the basic data and concepts, and
therefore it should be considered acceptable to use such works for computational
purposes. The argument is that the focus is not on reproducing the exact form of the
source material, but on using the information and structures present in that material to
achieve novel and inventive results.

VII. Exploring alternatives for safeguarding AI training data

As previously discussed, using data for training purposes involves assimilating information
obtained from publicly accessible websites on the Internet, including texts, images and
other content. This procedure involves reproducing the content, which may violate the
exclusive right of reproduction protected by copyright law and jeopardise the rights of
authors and performers.75 In order to advance and deploy generative AI across multiple
industries, it is crucial to improve the access to and use of training data in terms of
transparency and fairness. This is because AI algorithms rely heavily on enormous
amounts of data to acquire knowledge and make accurate predictions. The availability and
accessibility of such data are crucial factors in determining the efficacy and performance
https://doi.org/10.1017/err.2023.59 Published online by Cambridge University Press

of an AI system. Keeping this objective in mind, we have considered a number of strategies

for achieving this objective.
Establishing explicit data-sharing agreements with data providers is an essential first
step.76 Data-sharing agreements can be used to address the complex issue of using

73
Regarding the role of the idea/expression dichotomy in the debate surrounding generative AI, see Lemley
and Casey, supra, note 38, 772–76. See also MA Lemley, “How Generative AI Turns Copyright Law on its Head”
available at SSRN <https://ssrn.com/abstract=4517702> (last accessed 1 august 2023; arguing that our current
basic copyright doctrines – the idea–expression dichotomy and the substantial similarity test for infringement –
do not fit generative AI).
74
Lemley and Casey, supra, note 38.
75
A recent open letter to policymakers on AI demanded creative rights in AI proliferation from various
organisations and businesses that collectively represent more than six million artists, producers, performers and
publishers worldwide. See International Confederation of Societies of Authors and Composers, “Global Creators
and Performers Demand Creative Rights in AI Proliferation – An Open Letter to policy makers on Artificial
Intelligence” <https://www.cisac.org/Newsroom/articles/global-creators-and-performers-demand-creative-
rights-ai-proliferation> (last accessed 1 august 2023).
76
For a view regarding the current data policies in the EU, see M Leistner and L Antoine, “IPR and the
Use of Open Data and Data Sharing Initiatives by Public and Private Actors” (European Parliament, May 2022)
 European Journal of Risk Regulation 619

protected content for AI training while ensuring compliance with copyright laws and
protecting content owners’ rights.77 These agreements are essential for delineating the
scope of data usage, establishing limitations, specifying required permissions and
arranging the necessary licenses for using copyrighted material in AI training processes.78
Data-sharing agreements also enable AI developers to establish a legally binding
framework that regulates the access, utilisation and administration of protected content
throughout the AI training process. The implementation of agreements can yield
advantages in terms of establishing unambiguous provisions pertaining to the authorised
usage of data and guaranteeing that such usage is confined to the domain agreed upon by
both parties. In addition, they have the potential to establish criteria for identifying non-
viable data and assessing the permissible utilisation of content in the context of AI
training. This encompasses determinations such as the permissible categories of AI
algorithms or models and the criteria that govern the timing and duration of data
utilisation. Furthermore, these agreements may comprise clauses that pertain to
limitations on data utilisation and guarantee adherence to limitations enforced by
proprietors of content by AI developers. These limitations may entail forbidding the
retrieval or repurposing of data beyond the initial AI instruction or the dissemination or
monetisation of protected material.
An additional viable measure for ensuring the protection of AI training data is to
contemplate the implementation of certain types of remuneration programmes, such as
revenue sharing or royalty payments, to guarantee that creators of copyrighted materials
utilised in AI systems are duly compensated.79 This strategy is important to demonstrate
recognition of the inherent value of copyrighted content and to ensure that content
creators receive a fair share of the benefits resulting from the use of their works by AI
systems. AI developers can establish a direct correlation between the financial gains
generated by AI systems and the use of copyrighted works by implementing revenue-
sharing or royalty structures. The aforementioned scenario presents a persuasive
motivation for content producers to provide their works as training data, given that they
stand to gain directly from the financial prosperity of AI systems that utilise their creative
output. In accordance with a revenue-sharing arrangement, creators of content would be
entitled to a pre-established portion of the revenue produced by an AI system that is
utilising their copyrighted materials. This could be a proportional arrangement in which
the content creator receives a fair share of the revenue in proportion to their contribution
to the training data. Such an arrangement ensures that content creators receive fair
https://doi.org/10.1017/err.2023.59 Published online by Cambridge University Press

<https://www.europarl.europa.eu/RegData/etudes/STUD/2022/732266/IPOL_STU(2022)732266_EN.pdf> (last
accessed 1 August 2023).
77
On the importance of data sharing, see “Towards a European Strategy on Business-to-Government Data
Sharing for the Public Interest: Final Report Prepared by the High-Level Expert Group on Business-to-Government
Data Sharing”, COM (2020).
78
M Kop, “The right to process data for machine learning purposes in the EU” (2021) 34 Harvard Journal of
Law & Technology 1 (2021) (supporting the proposal for a right to process data for machine learning purposes).
79
See M Senftleben, “Generative AI and Author Remuneration” (14 June 2023) <https://ssrn.com/abstract=
4478370> (last accessed 1 August 2023; proposing to introduce remuneration mechanisms that ensure the
payment of compensation for the use of generative AI systems in the literary and artistic field); M Senftleben, “A
Tax on Machines for the Purpose of Giving a Bounty to the Dethroned Human Author – Towards an AI Levy for the
Substitution of Human Literary and Artistic Works” (28 January 2022) <https://ssrn.com/abstract=4123309>
(last accessed 1 August 2023); Sobel, supra, note 38 (supporting the idea of a scheme for authors who do not object
to their works being used to train AI, but who want to be compensated); G Frosio, “Should We Ban Generative AI,
Incentivise It or Make It a Medium for Inclusive Creativity?” in E Bonadio and C Sganga (eds), A Research Agenda for
EU Copyright Law (Cheltenham, Edward Elgar, forthcoming) available at SSRN <https://ssrn.com/abstract=
4527461> (last accessed 1 August 2023; (exploring alternative mechanisms to support and promote human
creativity in the face of AI advancements).
 620 Nicola Lucchi

compensation for the value that their copyrighted works have on the functionality and
success of the AI system.
Alternatively, a royalty-based compensation model could be implemented in which
content creators receive a set fee for each use of their copyrighted works by the AI system.
This fee structure could consist of a fixed amount per use or a percentage of the revenue
generated by the AI system. This model guarantees that content creators receive fair
compensation for the duration of the AI system’s use of their copyrighted works by linking
the fee to their usage.
The implementation of revenue-sharing or royalty structures requires explicit
agreements between AI developers and content creators, specifying the exact terms of
compensation. It is obviously important that these agreements specify the exact method
for calculating revenue sharing or royalties, as well as their periodicity and temporal
scope. Implementing transparent and mutually agreed-upon remuneration mechanisms
can safeguard the interests of both AI developers and content creators, promoting a fair
and sustainable ecosystem for integrating copyrighted works into AI systems. In general,
the concept of compensating content producers through revenue sharing or royalties aims
to recognise the importance of their copyrighted material in the AI ecosystem and to
ensure that they receive adequate compensation for their role in the prosperity of
generative AI tools. The scheme fosters a symbiotic connection between AI developers and
content creators while maintaining equitable and just practices in the use of copyrighted
material in the AI field.
An additional crucial policy element for safeguarding and reinforcing AI training data
could potentially involve the creation and maintenance of open-source datasets intended
for the purpose of training machine learning models. The majority of AI research is
currently being funded by larger corporations. Hence, it is imperative to institute a
programme that provides unrestricted or unobstructed entry to AI training data with the
aim of fostering ingenuity, promoting collaboration and propelling the field of AI on a
more democratic, equitable and transparent trajectory. The provision of such datasets
could ensure that scholars, programmers and corporations are able to utilise them to
construct and improve AI models. Advocating for the free or open accessibility of AI
training data aligns with the principles of knowledge dissemination, accountability and
equitable opportunities for both incumbent enterprises and emerging players from a
policy perspective.80 The reason for this is that well-established enterprises, owing to their
prevailing market positions, possess extensive repositories of linguistic and visual data
that can be leveraged for the advancement of AI. Enabling broad access to data has the
https://doi.org/10.1017/err.2023.59 Published online by Cambridge University Press

potential to promote the progress of AI technology for the collective benefit of society
rather than confining its advantages to a select few. This approach can also foster
equitable competition by reducing entry barriers and facilitating the participation of
smaller entities and marginalised communities in AI research.81 Hence, it is desirable to
develop legislative measures that facilitate the promotion and the exchange of data while
safeguarding privacy and intellectual property rights, thereby facilitating the utilisation of
open-access AI training data. Specifically, these training data should be considered as a
public “participatory good” because their production is based on collective efforts and
their value results from the collective participation of numerous individuals who offered
their creative content for the creation of training datasets.

80
See OECD, “Recommendation of the council on artificial intelligence”, OECD/LEGAL/0449 https://
legalinstruments-oecd-org.sare.upf.edu/en/instruments/OECD-LEGAL-0449 (last accessed 1 August 2023).
81
See, eg, MA Lemley and A McCreary, “Exit Strategy” (2021) 101 Boston University Law Review 1, 68
(suggesting that specific AI training databases should be made accessible to all AI systems, or alternatively
companies should permit their competitors to access these databases to ensure compatibility with the widely
accepted standard).
 European Journal of Risk Regulation 621

To ensure the adequacy of data diversity for the training of AI models, it may be
advisable for the law to incorporate provisions that incentivise businesses to voluntarily
furnish anonymised data to publicly accessible repositories. The proposed framework has
the potential to establish benchmarks for the ethical handling of data, safeguarding the
privacy of individuals and preventing the deployment of AI applications that may lead
to discrimination or harm. By establishing legal frameworks, it is possible to address
issues related to data ownership and licensing, as well as defining the rights and
responsibilities of both data providers and consumers. The implementation of similar
legislative measures has the potential to foster collaboration among the government,
industry and academia by means of funding schemes and recognition systems that
prioritise initiatives pertaining to open-access AI training data. One potential strategy
by which policymakers could foster a culture of openness and collaboration within the
AI industry would be to provide grants and other incentives to researchers and
organisations that prioritise the sharing of data. Under this perspective, a practical
and concrete solution that could address the problem of copyright clearance of input
data (training data) and give AI developers some breathing space would be to establish
data repositories or clearinghouses for machine learning training datasets.82
Establishing data repositories or clearinghouses has the potential to make obtaining
licenses and approvals much easier while promoting a more efficient and open process.
Indeed, these repositories could act as centralised platforms that facilitate the process
of obtaining licenses and permissions and enable negotiations between AI developers
and content creators to take place more easily. They also play a crucial role in
streamlining the complicated process of resolving copyright disputes, ensuring fair
compensation and protecting the interests of all parties involved. Content creators
have the option to formally register their works in a designated repository, where
they must explicitly state the terms of use and compensation they expect to receive for
the use of their protected intellectual property. This allows AI developers to easily
access these data and ensure that negotiations are based on accurate and transparent
information. AI developers and content creators can more effectively manage the
complexity of compensation and rights issues through the use of data repositories.
The centralised nature of these repositories promotes consistency and fairness in
determining compensation and ensures compliance with licensing terms and
copyright laws. In addition to the benefits already mentioned, data repositories can
also promote fair competition among AI participants and provide good opportunities
for new entrants in the field. Namely, these repositories can provide access to valuable
https://doi.org/10.1017/err.2023.59 Published online by Cambridge University Press

datasets that smaller organisations might not have been able to access on their own.
This creates new opportunities for innovation and competition, as a broader range of
AI model and algorithm developers can leverage high-quality data, reducing the
concentration of data ownership and promoting competition in the AI industry.83

82
See, eg, M Kop, “Machine Learning & EU Data Sharing Practices” (3 March 2020) Stanford–Vienna
Transatlantic Technology Law Forum, Transatlantic Antitrust and IPR Developments, Stanford University, Issue
No. 1/2020 <https://ssrn.com/abstract=3409712> (last accessed 1 August 2023; briefly discussing the
establishment of an online clearinghouse for machine learning training datasets).
83
A concrete example of initiatives in the field of open data and data sharing for AI training data is the Global
Initiative on AI and Data Commons. See International Telecommunication Union, “Global Initiative on AI and Data
Commons” <https://www.itu.int/en/ITU-T/extcoop/ai-data-commons/Pages/default.aspx> (last accessed 1
August 2023). See also The European AI Alliance, European Commission <https://futurium.ec.europa.eu/en/
european-ai-alliance> (last accessed 1 August 2023).
 622 Nicola Lucchi

Ultimately, implementing ethical guidelines and industry standards for AI training

could serve as an additional viable means of protecting AI training data.84 These guidelines
address issues such as data collection, consent, fair compensation and the transparency of
the training process. Adherence to these guidelines can help to ensure responsible and
legal use of copyrighted works, builds trust and foster a culture of compliance and respect
for copyright.
Consent is another essential element that can be covered by such ethical principles.
Legal use of copyrighted works requires obtaining appropriate consent from content
creators or affected individuals. Such ethical guidelines provide guidance on obtaining
informed consent that ensures content creators fully understand how their works
will be used in AI training processes and allows them to give consent or opt out if they
wish. This promotes integrity and respect for the rights of content creators and
fosters a mutually beneficial relationship between AI developers and creators of
copyrighted works.
Another fundamental aspect addressed in such ethical guidelines is transparency.
These guidelines would promote transparency in AI education by ensuring that
the sources and uses of copyrighted works are properly documented and disclosed.
Transparency increases accountability and trust among stakeholders and allows
content creators and AI system users to track the use of copyrighted works and resolve
potential concerns or disputes.
Despite significant advances in the field of generative AI, there is still a reluctance to
deploy it widely. A significant obstacle that requires attention is the lack of clearly defined
standards in critical areas, including but not limited to intellectual property rights.
Successful management of copyright protection for training data can be facilitated
through the implementation of effective policies by policymakers. This can ensure the
smooth incorporation of generative AI systems. The aforementioned proposals seek to
highlight the importance of clear regulations in this context. In particular, these proposals
could help to enable fair and clear data retrieval, equitable remuneration of creators,
simplified licensing procedures and compliance with copyright regulations and ethical
principles. When formulating AI regulations and policies, policymakers should consider
the above factors.85 By incorporating these variables into their models, policymakers
can create a durable and innovation-driven environment for the advancement of
generative AI.86
https://doi.org/10.1017/err.2023.59 Published online by Cambridge University Press

84
This particular aspect has attracted significant attention both on a global scale and within the EU. See, eg,
OECD, Observatory of Public Sector Information, “Algorithmic Impact Assessment” (2019) <oecd-opsi.org/
toolkits/algorithmic-impact-assessment/> (last accessed 1 August 2023); EC, “EU Guidelines on ethics in artificial
intelligence: Context and implementation” (2019), PE 640.163.
85
The current discourse on AI governance seems to largely overlook these particular aspects. The EU’s AI Draft
Regulation (see infra, note 86) does not discuss intellectual property issues in depth, as the current version
contains only a general obligation to disclose summaries of the copyrighted information used for training
purposes. The USA has taken steps to address the risks associated with AI, with the National Institute of Standards
and Technology – within the US Department of Commerce – developing a voluntary AI risk management
framework. This is the first explicit US government guideline on standards in AI system design, development,
deployment and use. See Department of Commerce’s National Institute of Standards and Technology, Artificial
Intelligence Risk Management Framework (AI RMF 1.0) (January 2023) <https://nvlpubs.nist.gov/nistpubs/ai/
nist.ai.100-1.pdf> (last accessed 1 August 2023). In addition, the White House Office of Science and Technology
Policy has published non-binding guidance for an AI Bill of Rights. See “Blueprint for an AI Bill of Rights”, Office of
Science and Technology Policy <https://www.whitehouse.gov/ostp/ai-bill-of-rights/> (last accessed 1 August
2023). For a more comprehensive summary of regulatory policies and actions linked to AI in the USA, see CS Yoo
and A Lai, “Regulation of Algorithmic Tools in the United States” (2020) 13 Journal of Law and Economic
Regulation 7, 7–9.
86
For a first concrete example of a piece of law designed to mitigate the potential hazards and challenges
associated with the advancement and execution of AI, see Proposal for a Regulation of the European Parliament
 European Journal of Risk Regulation 623

VIII. Conclusions
This article examined copyright issues related to generative AI from the general
perspective of the ChatGPT case study. It presents methods for addressing legal challenges
in the development of AI systems, with the goal of protecting both copyright holders and
competitors. The first part of the paper explored both the theoretical and practical
applications of complex language models such as ChatGPT. The second part looked at the
output of the ChatGPT model and discussed copyright issues. The third part looked at the
training data and discussed copyright concerns.
We have also emphasised the increasing number of legal actions targeting
generative AI systems. The litigation in question specifically focuses on the developers
responsible for creating these systems, including ChatGPT. A significant number of
cases involve various aspects of copyright protection, such as the training data used to
train AI models and the nature of the data employed for this purpose. The research
concludes that the ethical and legal concerns raised by AI model development must be
addressed holistically, considering both inputs and outputs. The management of
intellectual property rights in AI goes beyond outputs to include inputs, namely
training data. AI systems rely heavily on large amounts of training data, which often
include copyrighted works. This raises questions about the lawful collection and use of
such data, as well as the creation of derivative works during the training process.
Access to large datasets has become a competitive advantage for incumbents that can
hinder innovation and competition. Ensuring fair and open access to training data is
critical to the development and deployment of AI technology. The creation of AI
training data is indeed a collective effort that requires the participation of many
individuals. The value of these data comes from the collective participation of many
individuals who have provided their creative content for the creation of training
datasets, and they should therefore be considered shared resources that are available
to all. Their use should be guided by principles of fairness and transparency.
Current legal frameworks, such as fair use in the USA and the TDM exemption in the EU,
provide some guidance on the use of copyrighted material to train AI models. However,
these frameworks may not fully address the complexities inherent in generative AI
systems, which can directly compete with and even dwarf original works. Balancing
technological advances with the preservation of creators’ rights is critical to navigating
the copyright landscape in the context of AI. In particular, finding alternatives for
protecting AI training data is critical to improving transparency and fairness in data access
https://doi.org/10.1017/err.2023.59 Published online by Cambridge University Press

and use. Strategies such as clear data-sharing agreements, compensation models that
provide for revenue sharing or royalties, data repositories or clearinghouses and the
development of ethical guidelines and industry standards can promote responsible and
lawful use of copyrighted works in AI systems. These approaches ensure compliance with
copyright laws, protect the rights of content creators, streamline licensing procedures and
promote a sustainable and innovation-friendly AI ecosystem.
It is becoming increasingly clear that the growing capabilities of machine learning
systems raise concerns about potential copyright restrictions. As AI technology continues
to advance, the use of copyrighted works in training data is becoming more common,
leading to the need for robust mechanisms to protect intellectual property rights. In the
future, it will also be necessary to emphasise the responsibility of AI developers to be

and of the Council: Laying Down Harmonised Rules on Artificial Intelligence (Artificial Intelligence Act) and
Amending Certain Union Legislative Acts, European Commission (22 April 2021), 2021/0106(COD) <eur-lex.
europa.eu/procedure/FI/2021_106> (last accessed 1 August 2023) [EU AI Draft Regulations] (which puts
obligations on providers and distributors). For some comments from a comparative perspective, see F Patel and
I Dyson, “The Perils and Promise of AI Regulation” (Just Security, 26 July 2023) <https://www.justsecurity.org/
87344/the-perils-and-promise-of-ai-regulation/> (last accessed 1 August 2023).
 624 Nicola Lucchi

proactive with their data-sourcing methods. It will be important for AI developers to

implement methods to ensure the provenance of AI-generated content in order to provide
more clarity about the works contained in training data. In the face of a new technological
dilemma, copyright once again has a critical role to play in reconciling the competing
interests of content creators and AI developers. This involves protecting the integrity of
original works, ensuring adequate remuneration and addressing the potential dangers and
complexities associated with the rapid advancement of generative AI. Consequently, such
technology will require a profound paradigm shift in our conception of creativity and a
corresponding re-evaluation of our approach to copyright.
Competing interests. The author declares none.
https://doi.org/10.1017/err.2023.59 Published online by Cambridge University Press

Cite this article: N Lucchi (2024). “ChatGPT: A Case Study on Copyright Challenges for Generative Artificial
Intelligence Systems”. European Journal of Risk Regulation 15, 602–624. https://doi.org/10.1017/err.2023.59