# Copyleft for Alleviating AIGC Copyright Dilemma: What-if Analysis, Public
Perception and Implications

Xinwei Guo1111These authors have contributed equally to this work. Yujun
Li2111These authors have contributed equally to this work. Yafeng Peng2&Xuetao
Wei1
1Southern University of Science and Technology
2Jiangxi Normal University
<EMAIL_ADDRESS>{liyujun<EMAIL_ADDRESS><EMAIL_ADDRESS>
###### Abstract

As AIGC has impacted our society profoundly in the past years, ethical issues
have received tremendous attention. The most urgent one is the AIGC copyright
dilemma, which can immensely stifle the development of AIGC and greatly cost
the entire society. Given the complexity of AIGC copyright governance and the
fact that no perfect solution currently exists, previous work advocated
copyleft on AI governance but without substantive analysis. In this paper, we
take a step further to explore the feasibility of copyleft to alleviate the
AIGC copyright dilemma. We conduct a mixed-methods study from two aspects:
qualitatively, we use a formal what-if analysis to clarify the dilemma and
provide case studies to show the feasibility of copyleft; quantitatively, we
perform a carefully designed survey to find out how the public feels about
copylefting AIGC. The key findings include: a) people generally perceive the
dilemma, b) they prefer to use authorized AIGC under loose restriction, and c)
they are positive to copyleft in AIGC and willing to use it in the future.

##  1 Introduction

With the advent of the large model era, AIGC (Artificial Intelligence
Generated Content) applications such as Midjourney(Mijourney Inc., 2023) and
GPT series(OpenAI, 2023) have demonstrated impressive performance, marking the
AIGC technology entering a new historical stage(Cao et al., 2023). Literally,
AIGC refers to content generated by artificial intelligence models, which
promises to replace a large amount of work automatically, thus significantly
improving users’ productivity. Because of the powerful capabilities and broad
prospects demonstrated by AIGC technologies, it has received widespread
attention from society(Zhang et al., 2023a; Meskó and Topol, 2023). However,
due to the lack of sound regulations for AIGC, it faces various burning
ethical issues, including copyright disputes.

For example, in Case 1, OpenAI was sued by the New York Times for illegally
using unauthorized copyright content in model training. In Case 2, the court
did not support AIGC’s copyright claim. Contrary to the verdict in Case 2, as
Case 3 illustrates, disagreement remains on whether AIGC should be protected
by copyright law. Thus, settling AIGC copyright disputes is critical to
advance its development and regulation(Lyu, 2023; Zhang et al., 2023b).

Case 1: The New York Times sued OpenAI In December 2023, the New York Times
sued OpenAI over copyright infringement, alleging OpenAI used the newspaper’s
material without permission to train the massively popular GPT(; ).

Case 2: Unsupported AIGC copyright claim In September 2022, Kris Kashtanova
applied for copyright protection for her comic book Zarya of the Dawn but did
not reveal that the images used in the book were created by the AIGC tool,
Midjourney. The US Copyright Office said that while the content of the book is
protected by copyright, the copyright of AI-generated images is not
protected(Analla, 2023).

Case 3: Supported AIGC copyright claim In February 2023, the plaintiff
generated a series of images using Stable Diffusion and uploaded them to a
social platform. Then, the defendant removed the watermark embedded by the
plaintiff and used them in his article. As China’s first lawsuit involving the
copyright of AIGC, the plaintiff sued the defendant for infringement of his
copyright, and the Beijing Internet Court supported the plaintiff’s copyright
claim in the first-instance judgement(Xie, 2023).

As reflected in these three cases, AIGC’s copyright disputes involve three
stakeholders, namely, (1) Owners of training data(referred to as Data Owner)
whose data is utilized for training the AIGC models; (2) Owners of AIGC models
(referred to as Model Owner) who develop the AIGC model and provide services;
(3) Users of AIGC models (referred to as Model User) who develop the prompts,
feed into AIGC models and generate the content.

The core question centered around these copyright disputes is: who owns the
copyright of the content generated by artificial intelligence? The answer
could be any stakeholder. This is a tricky problem as no matter which
stakeholder obtains the copyright of AIGC, it will inevitably hinder the
development of the AIGC ecosystem, which is referred to as the AIGC copyright
dilemma. On the one hand, legislation for AIGC is a long and complicated
process. On the other hand, the development of AIGC is of great significance
for improving the productivity of the whole society. Solving this problem at
the expense of AIGC development is not a wise solution. Therefore, a better
solution is expected, one that can balance stakeholders’ interests to resolve
copyright disputes.

Interestingly, previous work has its voice in advocating for copyleft in AI
governance. Copyleft is a legal technique that has been practiced in GNU
licenses for free software(FSF, 2023). It grants certain freedoms over copies
of copyrighted works while requiring that the same rights be retained in
derivatives. The “contagiousness” of copyleft ensures that the work must be
used under copyleft terms during dissemination. This restriction could promote
the spread of AIGC while protecting copyright. However, these previous work
just mentioned the copyleft and lack a substantive analysis of its
feasibility.

In this paper, we take a step further to substantiate this advocate by
answering the following three questions:

  * •

What are the intricacies of the AIGC copyright dilemma?

  * •

What is the public perception of the dilemma and copyleft?

  * •

What are the implications and recommendations of these studies?

First, we conduct a formal and thorough what-if analysis to clarify the
dilemma. By assembling three scenarios of what if one stakeholder claims the
copyright and what impact the other two stakeholders have, we progressively
present the outcomes from three aspects: benefits, challenges, and risks. The
results of the analysis clearly reveal the intricacies of this dilemma and
show that no matter which stakeholder claims the copyright, the development of
the other two stakeholders is inevitably hindered, which is not beneficial to
the whole AIGC lifecycle. Given that there is no perfect solution to this
dilemma, we must seek a “trade-off” solution, which is also the reason why
copyleft was advocated. It alleviates conflicts among stakeholders from the
development point of view. We also conduct a qualitative analysis of three
cases of copyright disputes with the application of copyleft and find that it
could alleviate the conflicts and resolve the disputes. Furthermore, we
carefully design a rigorous survey to quantitatively study the public
perception of such a dilemma and the extent to which people accept copyleft as
a feasible solution. Based on the survey data, we build a regression model to
capture how various factors impact people’s willingness to adopt copyleft in
the future. The results overall support copyleft as a workable solution.

Our contribution can be summarized as follows:

  * •

We clarify the AIGC copyright dilemma via a formal and thorough what-if
analysis and reveal the dilemma’s intricacies, which serve as the ground for
why copyleft was advocated.

  * •

We conduct a quantitative analysis through a rigorous questionnaire survey to
further assess the effectiveness of copyleft from the public perception
perspective.

  * •

The implications of our study show that (1) copyleft has the potential to
mitigate stakeholders’ conflicts and facilitate the development of AIGC; (2)
The public clearly perceives the AIGC copyright dilemma and is generally
positive about copylefting AIGC, thus open to the implementation of copyleft
in AIGC.

Note that, we admit the copyleft has its own limitations, e.g., privatization
difficulty, which is also the reason we emphasize “alleviate” rather than
“solve”. Given the complexity of the AIGC copyright dilemma, we argue that it
is better to find a workable solution that minimizes the risks of conflicts
and promotes the significance of AIGC-empowered society development. This is
also the major motivation for us to come up with this substantive analysis of
the copyleft feasibility, which paves the way for future research and public
discussion. We envision our work could be one of the beneficial supplements to
AIGC copyright governance.

##  2 Related Work

The AIGC copyright dilemma did not arise unconsciously. In 2020, the European
Union published a report on the challenges of AI to the intellectual property
rights framework, which has already pointed out the copyright issues
associated with AI-assisted outputs(Commission et al., 2020). Shortly after
that, the UK Intellectual Property Office launched an open consultation on
copyright protection for computer-generated works(Kretschmer et al., 2022).
Over the last year, AIGC’s copyright issues have become a common concern; the
US Copyright Office released guidance(Register, 2023) on copyright
registration of works containing AI-generated content, and China issued
interim measures(CAC, 2023) to guide the development of AIGC. Despite these
efforts, the policies and guidelines are vague and difficult to materialize,
and there are still many contentious lawsuits.

In the current AIGC copyright protection and traceability research,
watermarking is the mainstream method. There are corresponding watermark
technologies for different forms of generated content, whether it is
image(Fernandez et al., 2023; Zhang et al., 2023c), text(Kirchenbauer et al.,
2023) or audio(DeepMind, 2023). In addition, there are also methods such as
blockchain(Liu et al., 2023), perturbation (Chen et al., 2023b) and
C2PA(OpenAI, 2024; C2PA, 2024) used by OpenAI in the DALL·E 3. However, these
methods are all aimed at solving the AIGC’s accountability problem and cannot
solve or alleviate the existing AIGC copyright dilemma.

In existing research on AI and licenses, (Contractor et al., 2022) used
behavioral licenses to constrain downstream tasks and implement responsible
AI. However, the application object of their method is software or code, which
does not involve copyright disputes as complex as those surrounding AIGC.
Similarly, Schmit et al. (Schmit et al., 2023) simply mentioned that the
copyleft can be used to achieve AI ethical governance in a three-page policy
forum essay published online. However, they did not provide a detailed
qualitative and quantitative analysis of its feasibility like ours.

Copyright Owner | Benefits | Challenges | Risks
---|---|---|---
Data Owner |  • Protect the training data |  • Huge scale • Diverse data sources • Hard to trace identities |  • Massive copyright lawsuits that hinder the development of AIGC.
Model Owner |  • More revenue to develop better AIGC models |  • Trained models based on data that may not have copyright • Rely on the user-constructed prompts |  • Slow the adoption of AIGC • Less creative works
Model User |  • More motivation to explore AIGC untapped potential • More creative works |  • Rely on AIGC models • Hard to judge the originality |  • Slow the adoption of AIGC • Huge overhead on the Copyright registration system due to massive generated content

Table 1: A summary of the what-if analysis: there is no ideal solution to
solve the AIGC copyright issue, which causes the AIGC copyright dilemma. Thus,
we need an option that could alleviate the dilemma.

##  3 The AIGC Copyright Dilemma: A What-if Analysis

In this section, we leverage the what-if analysis to clarify the AIGC
copyright dilemma. The what-if analysis method (Zagonel et al., 2004;
Kleijnen, 1994) is a decision-making process that is widely used in evaluating
complicated scenarios and potential risks beforehand, thereby helping make the
right decision. Following are the three scenarios we consider in terms of
benefits, challenges, and risks, respectively.

###  3.1 What if the data owner claims the copyright?

Benefits: This way could best protect the copyright of the training data,
which also complies with the copyright law to protect the creation in the
public domain. This practice has been used in non-AIGC domains since the debut
of the copyright law. For example, an artist creates an artwork in a physical
format and posts the electronic copy online. Whether it is online or offline,
the artist could claim the copyright of the artwork and legally share the
revenue from the artwork. Such practice has boosted the sustainability of
corresponding industry sectors with the prosperous development of the economy.

Challenges: Unlike traditional proprietary materials, the training data of
AIGC models is mostly from the online content of the Internet(Crawl, 2023),
along with some private and high-quality data already in hand. It has the
following properties: 1) the scale is huge, which covers most of the public
content online; 2) the data sources are very diverse, which varies from high-
quality collections to low-quality content of free riders; 3) the number of
data owners is hard to count, and the identities of these data owners are
impossible to trace. It is extremely challenging to identify the exact owner
of every piece of the massive training data. The consent from every data owner
is almost impossible to achieve in practice.

Risks: We assume we could identify all the data owners. Then, if the data
owners claim to share the copyright of the AIGC content, there is no way to
quantify the attribution, which could be a mess. The reality that we currently
cannot identify the data owners makes it even worse. Any data owner who
resorts to claiming the copyright by suing the AIGC model owners may suspend
the development of AIGC applications and, in return, harm the booming AI
industry.

###  3.2 What if the model owner claims the copyright?

Benefits: It is an expensive process to create a workable AIGC model, which
involves not only expensive computational costs but also the expensive cost of
talented scientists and engineers (Strubell et al., 2019; Sharir et al.,
2020). With the vast amount of investment, model owners expect to claim the
AIGC copyright to protect themself and seek more revenue in this business
sector, which could, in return, benefit the economy. This will allow model
owners to have more funds for AIGC model research and training, thereby
obtaining better-performing models, promoting the development of AIGC, and
providing better services to users.

Challenges: The copyright claim of the model owner will face two types of
contribution disputes, both of which may lead to the model owner being sued
for infringement. First, crawling the public online Internet content without
data owners’ consent to train the AIGC model; this congenital inadequacy is
like a chain to prevent model providers from moving forward to claim the
copyright. Second, for model users who provide prompts, it is difficult to
accept that they spend money to create copyrights for others.

Risks: If model owners claim the copyright of the AIGC content, it would have
two direct risks. On the one hand, this would limit the spreading speed of the
AIGC models and become an obstacle to accelerating their development, namely,
shooting themself in the foot. On the other hand, people are more hesitant to
adopt AIGC in their daily lives, which deviates from the original purpose of
inventing AIGC – raising industry productivity that brings more benefits to
society. Both consequences are harmful to model owners.

###  3.3 What if the model user claims the copyright?

Benefits: Though AIGC is powerful enough to generate content, it still has a
gap to reach the ideal content. To bridge this gap, model users spend efforts
and expertise to finetune the prompts(Chen et al., 2023a). Such efforts are
protected so that more users are motivated to explore the AIGC’s untapped
potential.

Challenges: It is challenging for model users to claim the copyright of AIGC
content as they still rely on the AIGC model. The only input they have is the
prompt. Since the AIGC content results from the Human-AI collaboration, it is
impossible to claim the copyright by excluding the model part. Moreover, the
creation behavior of model users using AIGC models can be regarded as re-
creations or secondary creations to a certain extent, and there is still
controversy over whether secondary creations have copyright(Wong, 2008; Lan et
al., 2023).

Risks: If the model user claims the copyright of AIGC content, it has a direct
impact on slowing the spreading of AIGC. This is because other users may
obtain the same or similar content out of the AIGC model but are not aware of
its copyright. To avoid further legal risks, people hesitate to use the AIGC
in their work, which again deviates from the original purpose of inventing
AIGC to raise productivity. Furthermore, due to the fast speed of generating
content from AIGC models, the scale of generated content could put a lot of
overhead on the copyright registration system, which hinders society’s
development.

###  3.4 Summary of What-if Analysis

Through the what-if analysis above, we can find that there is no ideal
solution that could take into account the interests of every stakeholder.
Every stakeholder has his own grounds to appeal for the copyright interest.
Most importantly, AIGC is created via the synergy of the data owner, model
owner, and model users, any stakeholder alone could not develop AIGC at all.
It is challenging for current regulations to address the intricacies of the
AIGC copyright dilemma. On the one hand, strictly enforcing current existing
copyright regulations may probably halt the blooming of AIGC. On the other
hand, if we place no restrictions on the copyright claim, it could also impose
legal and management risks that slow the AIGC adoption in other industry
sectors. We believe that the most important function of AIGC is to facilitate
social productivity and boost the economy. Therefore, we must balance
stakeholders’ interests and seek a compromise scheme, which is also the
motivation for the advocate of copyleft in previous work.

##  4 Case Study Under the Copyleft

As mentioned in the introduction, there are three real-world cases of AIGC
copyright disputes.

####  4.0.1 Case 1: The New York Times sued OpenAI

When the copyleft license is applied to the generated content, this
contradiction could be alleviated. As the model owner, OpenAI does not own the
copyright of generated content and cannot profit from the copyright. This will
significantly limit the commercial use of generated content and reduce
competition with the New York Times, thereby alleviating the dispute.

####  4.0.2 Case 2: Unsupported AIGC copyright claim

According to our proposed method, the generated content is licensed under a
copyleft license. Therefore, the model users cannot claim the copyright of the
images alone, which directly resolves the dispute.

####  4.0.3 Case 3: Supported AIGC copyright claim

If the content generated by the plaintiff is licensed under a copyleft
license, then the defendant’s use of the generated content must be under the
copyleft license. Otherwise, it is illegal, which quickly resolves the
dispute.

##  5 Public Perception: A Survey Method

We developed a survey to uncover public opinions on copylefting AIGC. It
roughly consists of two parts: the AIGC copyright dilemma and the feasibility
of copyleft as an alleviation. 22 items were designed, 8 of which were
displayed on an ordinal scale of 1 to 5, 8 were yes-or-no questions, and the
remaining 6 items were on nominal scales ranging from 4 to 11. The detailed
questionnaire contents can be found in Appendix A of the supplementary. A
total of 250 participants were recruited through an IRB-approved process.
After removing participants who did not pass the attention check, we had a
total of 211 of them providing valid responses.

###  5.1 Results

####  5.1.1 The AIGC Copyright Dilemma

As shown in Figure 5.1.1, the majority of our participants have experienced
AIGC models and have at least limited knowledge about copyright. However,
exposure to AIGC and familiarity with copyright were not enough to lift people
out of the AIGC copyright dilemma.

Figure 1: Familiarity with copyright across whether experienced AIGC. 1 =
Never heard, 2 = Heard before, 3 = Limited knowledge, 4 = Familiar, and 5 =
Very familiar.

Figure 2: Copyright ownership of an AI-generated image across different groups
(all participants, participants experienced AIGC, and participants familiar
with copyright). a = van Gogh, b = da Vinci, c = Model user, d = Model owner,
and e = Data owner.

Figure 3: Concern about AIGC copyright issue, 1 = completely indifferent, 2 =
indifferent, 3 = neutral, 4 = Concerned, and 5 = Very concerned.

Figure 4: Concern about AIGC copyright issue across different groups
(“experienced AIGC: no” VS “experienced AIGC: yes” for the left panel, and
“unfamiliar with copyright” VS “familiar with copyright” for the right panel).
The x-axis has a scale identical to Figure 5.1.1.

Figure 5: Relationship between copyleft and copyright. 1 = very conflicted, 2
= somewhat conflicted, 3 = unsure, 4 = somewhat not conflicted, and 5 = Not
conflicted at all.

Figure 6: Relationship between copyleft and copyright across different groups
(“experienced AIGC: no” VS “experienced AIGC: yes” for the left panel, and
“unfamiliar with copyright” VS “familiar with copyright” for the right panel).
The x-axis has a scale identical to Figure 5.1.1.

Figure 7: Level of agreement on using authorized AIGC and extent of freedom
expected when using AIGC across different groups (“AIGC copyright infringement
after copylefting: no” VS “AIGC copyright infringement after copylefting:
yes”).

Figure 8: Number of participants selecting different numbers of upsides and
downsides.

People were confused about the ownership of AIGC copyright. Participants were
presented with a fictitious question about who should own the copyright of an
AI-generated Mona Lisa in Vincent Van Gogh style. Five options were provided
for them to choose from(see Figure 5.1.1 for more details). It is clear that
fewer people considered data owners should own the copyright. We surmise this
is because a) the public’s intuition that it is hard to copyright data, and b)
in AI-related scenarios, the algorithm/model behind it is directly responsible
for the content generated and is usually given more emphasis than the data
used for training. We, therefore, excluded the “data owner” option in the
following statistical analysis.

A $\chi^{2}$ test revealed that the difference among the numbers of
endorsements of the rest four options was statistically insignificant
($\chi^{2}=3.47$, $df=3$, $p=0.32$). This pattern was consistently observed
among people who were familiar with copyright ($\chi^{2}=2.63$, $df=3$,
$p=0.45$) and among people who have used AIGC before ($\chi^{2}=2.90$, $df=3$,
$p=0.41$). Given this prevailing confusion, it was not surprising that more
than half of our participants thought it was not easy to establish copyright
for AIGC ($n=121$, $57.35\%$). Familiarity with copyright failed to change
this perception, for those familiar with copyright, about $56.46\%$ of them
recognized the difficulty, whereas the ratio for people who were unfamiliar
with copyright was $57.58\%$, no statistical difference observed between these
two proportions ($z=-0.12$, $p=0.91$). The experience in AIGC had no effect
either ($z=0.11$, $p=0.92$).

People concerned about the AIGC copyright issue. As shown in Figure 5.1.1,
$56.40\%$ participants worried about the copyright of AIGC, $24.64\%$ of them
were neutral, only $18.96\%$ of them lacked in concern. The number of people
who lacked in concern remained stable across the two groups (see the left part
of each panel of Figure 5.1.1). But the number of people who were concerned
about the issue varied dramatically in groups; the AIGC copyright issue was
more likely to be on the radar of people who were familiar with copyright or
used to be AIGC users (see the right part of each panel of Figure 5.1.1).

People were unclear about the relationship between copyleft and copyright. As
shown in Figure 5.1.1, less than half of our participants thought that
copyleft is not the antithesis of copyright ($n=95$, $45\%$), which is the
correct understanding of the relationship between copyleft and copyright. We
further examined the pattern by conducting a $\chi^{2}$ test (see Figure 5.1.1
for more details). When doing so, we excluded the “very conflicted” option
because the cell size was smaller than five in both the group of participants
who were unfamiliar with copyright and the group of participants with no
experience in AIGC. The results suggested that whether people were familiar
with copyright tended to influence their attitude towards the relationship in
interest ($\chi^{2}=7.30$, $df=3$, $p=0.06$). Surprisingly, it seemed that the
knowledge of copyright and experience in AIGC tended to shape the response of
our participants into a bimodal distribution. In particular, among
participants who had prior knowledge of copyright, there was a $153.85\%$
increase in the number of individuals who accurately perceived the
relationship as “somewhat not conflicted” compared to those who were
unfamiliar with copyright. the number of people who were familiar with
copyright but chose the “somewhat conflicted” category also increased by
$66.67\%$. A similar pattern was clearly shown in the right panel of Figure
5.1.1. Familiarity with copyright and experience in AIGC seemed to complicate
the problem.

Immediately after the question about AI-generated images, we asked our
participants a yes-no question about whether they think they can use the
generated picture freely. No statistically significant difference between the
number of people who selected “yes” and “no” ($\chi^{2}=1.37$, $df=1$,
$p=0.2419$), which further demonstrated the ambivalence rooted in the AIGC
copyright dilemma.

####  5.1.2 Copyleft is a feasible option

People inclined to use authorized AIGC under restrictions. Three questions
were presented to the participants about their expected level of authorization
and freedom when using AIGC, the results were captured in Figure 5.1.1.
Clearly, we observed two negatively skewed distributions of bar, implying that
more people a) supported obtaining authorization of AIGC before use
($n=96+55$, $71.56\%$), and b) preferred to be imposed on loose restrictions
or have no restriction at all while using AIGC ($n=86+42$, $60.66\%$). More
specifically, the option we considered as most reasonable, using authorized
AIGC under loose restrictions (response = 4 in Figure 5.1.1), received the
strongest endorsement from our participants.

People generally agreed that copyleft could resolve AIGC copyright
infringement. Meanwhile, more than half of our participants ($n=123$,
$58.29\%$) thought copyleft is capable of preventing copyright infringement of
AIGC if properly used by both model owner and model user. Interestingly, the
largest difference between the number of participants who chose “no” ($n=42$)
and “yes” ($n=13$) was observed in the bar at the upper-right corner of Figure
5.1.1. That is, among the people who strongly agreed in obtaining
authorization of AIGC ($n=55$), $79.25\%$ of them thought copylefting AIGC
could prevent copyright infringement.

People were generally positive about applying copyleft to AIGC. We asked
participants what will happen if AIGC is copylefted. Six negative predictions
and five positive predictions were provided; they were free to choose any
number of these upsides and downsides. As shown in Figure 5.1.1, people were
generally positive about the future of applying copyleft to AIGC in that a)
the number of participants who selected at least one downside ($n=150$) was
smaller than the number of participants who selected at least one upside
($n=185$), and b) $73.71\%$ participants opted for no more than two downsides
and $65.55\%$ participants selected two or more upsides.

####  5.1.3 Willingness to utilize copyleft in AIGC

We used an ordinal logistic regression model with Willingness to Use Copyleft
in AIGC as the numeric dependent variable (1-5). Ten variables investigated in
the two sub-sections above were included as independent variables (see Table 2
for more details). The results consist of two parts: AIGC copyright dilemma
and Copyleft as a feasible option.

AIGC copyright dilemma: confusion and difficulty over assessing the
relationship between copyleft and copyright decreased the will, but concern
about copyright issue did the opposite. Familiarity with copyright
($\beta=-0.01$, $p=0.97$) and Experience in AIGC ($\beta=0.23$, $p=0.49$) have
no measurable effect on Willingness to Use Copyleft in AIGC. Relationship
Between Copyleft and copyright, and Difficulty in Establishing AIGC Copyright,
both significantly and negatively impacted Willingness to Use Copyleft in
AIGC. More specifically, the odds decreased by $100\%-74.43\%=25.57\%$ for
every one unit increase in Relationship Between Copyleft and copyright
($\beta=-0.30$, $p=0.04$), implying that participants who tended to perceive
the relationship between copyleft and copyright as not opposed to each other
were less willing to apply copyleft to AIGC. Similarly, the more difficult
people felt about establishing copyright for AIGC, the less likely they will
accept copyleft ($\beta=-0.27$, $p=0.08$, odds reduced by $23.75\%$). Whereas
Concern About AIGC Copyright Issue significantly increased the odds of a
participant having a higher willingness to embrace copyleft ($\beta=0.57$,
$p=0.00$) by $77.15\%$.

Copyleft as a feasible option: the preference for using authorized AIGC while
under loose restrictions increased the will, and so did the number of upsides
selected. AIGC Copyright Infringement After Copylefting and Downsides of
copylefting AIGC failed to have a significant impact on Willingness to Use
Copyleft in AIGC. Authorizing AIGC Before Use had a favorable influence on
Willingness to Use Copyleft in AIGC ($\beta=0.73$, $p=0.00$), increases the
odds of interest by $107.63\%$ per unit. People expecting a higher level of
freedom in using AIGC were more likely to advocate the use of copyleft
($\beta=0.58$, $p=0.00$, odds increased by $78.48\%$). It seemed that the
number of upsides people recognized regarding copylefting AIGC reaped a
positive effect on Willingness to Use Copyleft in AIGC ($\beta=0.19$,
$p=0.05$), the odds increased per unit change was $21.06\%$.

| Independent variable | Coding | $\beta$ | $se$ | $t$ | $p$ | Odds ratio(%)
---|---|---|---|---|---|---|---
Part 1: AIGC copyright dilemma | Familiarity with Copyright | 1-5 | -0.01 | 0.20 | -0.03 | 0.97 |
Experience in AIGC | 0-1 | 0.23 | 0.32 | 0.70 | 0.49 |
Relationship Between Copyleft and Copyright | 1-5 | -0.30 | 0.14 | -2.09 | 0.04 |  $\downarrow$ 74.43%
Difficulty in Establishing AIGC Copyright | 1-5 | -0.27 | 0.15 | -1.77 | 0.08 |  $\downarrow$ 76.25%
Concern about AIGC Copyright Issue | 1-5 | 0.57 | 0.18 | 3.24 | 0.00 |  $\uparrow$ 177.15%
Part 2: Copyleft as a feasible option | Authorizing AIGC Before Use | 1-5 | 0.73 | 0.17 | 4.38 | 0.00 |  $\uparrow$ 207.63%
Freedom of Using AIGC | 1-5 | 0.58 | 0.14 | 4.07 | 0.00 |  $\uparrow$ 178.48%
AIGC Copyright Infringement After Copylefting | 0-1 | -0.13 | 0.29 | -0.43 | 0.67 |
Upsides of Copylefting AIGC | 0-5 | 0.19 | 0.10 | 1.94 | 0.05 |  $\uparrow$ 121.06%
Downsides of Copylefting AIGC | 0-6 | -0.02 | 0.08 | -0.28 | 0.78 |

Table 2: The results of the ordinal logistic regression model, demonstrating
how various factors impact participants’ willingness to use copyleft in AIGC.

##  6 Implications and Recommendations

Copyleft can effectively alleviate the AIGC copyright dilemma and facilitate
the dissemination of AIGC. Our qualitative analysis has demonstrated that the
implementation of copyleft can help mitigate disputes among stakeholders and
provide a solid foundation for adjudicating AIGC infringement cases.
Furthermore, copyleft can promote the sharing of AIGC by requiring derivatives
to be licensed under the same terms.

The AIGC copyright dilemma was well perceived among people and further
catalyzed their willingness to adopt copyleft. Similar to previous work, we
echoed the confusion over the ownership of AIGC copyright (Lyu, 2023; Chen et
al., 2023b). But unlike prior work, we unveiled that the preknowledge of
copyright and AIGC could make people more concerned about the AIGC copyright
issue but failed to reduce the perplexity. People’s concern over the AIGC
copyright issue naturally converted into a positive factor in becoming a
copyleft adopter. Surprisingly, the correct understanding of the relationship
between copyleft and copyright turned out to be a negative factor, so did the
perceived difficulty in establishing AIGC copyright ownership. There is the
privatization difficulty of copyleft that is to blame. For example, two
participants reported “feeling privatization depressed” if the content they
“create” via the free use of AIGC model has to be freely available to others,
thus they were less willing to use copyleft despite they understood the
relationship and appreciated the difficulty.

Copyleft satisfied people’s need for AIGC, making individuals positive about
its future and increasing their inclination to embrace copyleft. Our
participants preferred to use authorized AIGC with loose restrictions.
Copyleft squared with this preference in that if adopted, copyleft grants
people the permission to use and distribute content ((FSF), 2022) freely.
Therefore, people with stronger preferences were more confident that copyleft
could prevent AIGC copyright infringement. All these built up into an overall
positive prediction from the public about what can happen if copyleft is
applied to AIGC and, in turn, greatly increased their will to embrace
copyleft.

Recommendations. Since AIGC’s copyleft license is shared among data owners,
model owners, and model users, the construction of the license should also be
completed collaboratively. Furthermore, a considerable amount of effort should
be placed into promoting copyleft to make more people aware of its feasibility
and dispel the detrimental misconceptions about copyleft. This is arguably an
across-the-board joint collaboration to be done on the level of our entire
society, including stakeholders, policy designers, educators, etc.

##  7 Conclusion

In conclusion, by adopting a mixed-methods approach, we have demonstrated the
feasibility of alleviating the AIGC dilemma via copyleft. From the qualitative
aspect, we first untangled complex conflicts within the AIGC copyright dilemma
through what-if analysis. Realizing there is no perfect way to consider all
stakeholders’ interests, copyleft could be a practical mitigation method, and
its feasibility has been demonstrated through case studies. From a
quantitative perspective, our survey has found that people concerned about the
AIGC copyright dilemma. They preferred to use authorized AIGC under loose
restrictions, thus were overall positive about copylefting AIGC, laying a good
foundation of promoting copyleft. We have also quantified how various factors
impacted people’s willingness to apply copyleft to AIGC. In particular, the
concern, preference and positiveness mentioned above served as incentives.
Overall, we need an across-the-board joint collaboration to make more people
aware of the feasibility.

##  8 Ethical Statement

This study was approved by the Institutional Review Board (IRB).

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