# Foregrounding Artist Opinions: A Survey Study on Transparency, Ownership,
and Fairness in AI Generative Art

Juniper Lovato<EMAIL_ADDRESS>0000-0002-1619-7552 Department of Computer
Science, University of Vermont82 University PlBurlingtonVermontUSA05405 ,
Julia Zimmerman<EMAIL_ADDRESS>0000-0003-1910-8030 Vermont Complex
Systems Center, University of Vermont82 University PlBurlingtonVermontUSA05405
,  Isabelle Smith<EMAIL_ADDRESS>0009-0001-8487-9782 Department of
Mathematics & Statistics, University of Vermont82 University
PlBurlingtonVermontUSA05405 ,  Peter Dodds<EMAIL_ADDRESS>0000-0003-1973-8614 Department of Computer Science, University of Vermont82
University PlBurlingtonVermontUSA05405 and  Jennifer Karson
<EMAIL_ADDRESS>Department of Art and Art History, University of
Vermont82 University PlBurlingtonVermontUSA05405

(2024; 20 February 2007; 12 March 2009; 5 June 2009)

###### Abstract.

Generative Artificial Intelligence (AI) tools are used to create art-like
outputs and aid in the creative process. While these tools have potential
benefits for artists, they also have the potential to harm the art workforce
and infringe upon artistic and intellectual property rights. Without explicit
consent from artists, Generative AI creators scrape artists’ digital work to
train Generative AI models and produce art-like model outputs at scale. These
outputs are now being used to compete with human artists in the marketplace as
well as being used by some artists in their generative processes to create
art. We surveyed 459 artists to investigate the tension between artists’
opinions on Generative AI art’s potential utility and harm. This study surveys
artists’ opinions on the utility and threat of Generative AI art models, fair
practices in the disclosure of artistic works in AI art training models,
ownership and rights of AI art derivatives, and fair compensation. We find
that artists, by and large, think that model creators should be required to
disclose in detail what art and images they use to train their AI models. We
also find that artists’ opinions vary by professional status and practice,
demographics, whether they have purchased art, and familiarity with and use of
Generative AI. We hope the results of this work will further more meaningful
collaboration and alignment between the art community and Generative AI
researchers and developers.

AI Art, Generative AI, AI Ethics, Survey Study, Consent, Privacy

††copyright: none††journalyear: 2024††doi: XXXXXXX.XXXXXXX††conference: Make
sure to enter the correct conference title from your rights confirmation
email; June 3-6, 2024; Rio de Janeiro, Brazil††isbn: 978-1-4503-XXXX-X/18/06

Figure 1. “Art is the joint responsibility of all [people]” (Huyghe, 1970).

An illustration of a rose garden connecting the minds of artists and being
pruned by a machine

##  1\. Introduction

Art has a rich history in co-evolution with the development of new
technologies (Etro and Galenson, 2023). The advent of Artificial Intelligence
(AI) tools used to create or assist in the creative process carries great
potential utility for artists but also harbors the significant potential for
harm to the art workforce and artistic and intellectual property. To examine
this tension between utility and threat, we surveyed 459 artists about their
views on AI-generated art.

Our study surveys artists’ opinions on fair practices in the disclosure of
artistic works in AI training models, ownership and rights of AI art
derivatives, and fair compensation models. Future work will involve conducting
long-form interviews with them to understand artists lived experiences. Our
research questions for this study can be summarized as follows: RQ1: Do
artists see AI models as a threat to art workers?; RQ2: Do artists see AI art
models as a positive development in the field of art?; RQ3: What types of
disclosure for the use of artwork to train an AI art model do artists consider
fair?; RQ4: From an artist’s perspective, who should own the derivative
artwork created by AI art models?

We summarize our research results as follows: (Q1) 61.87% of survey
participants agree that AI models are a threat to art workers. (Q2) 44.88% of
survey participants agreed that AI art models are a positive development in
the field of art (22.03% of survey participants agreed that both AI was a
positive development and a threat). (Q3) Artists overwhelmingly agreed
(80.17%) that model creators should be required to disclose in detail what art
and images they use to train their AI models. (Q4) Artists were relatively
split between thinking that derivatives from AI art models should be owned by
the original artists whose work is represented in the AI art (41.39%) and the
user who used the model to create the AI art (39.22%). In our study, 26.80% of
the participants agreed that the work and its derivatives be considered the
property of the AI model creators. We also looked into what types of
compensation artists consider to be fair in exchange for the use of their
artwork to train an AI art model. Half of the respondents (50.97%) stated that
they did not need compensation, but what mattered to them was who was
profiting from the use of their artwork. The most common fair compensation
option selected by the survey participants was “I don’t need to profit, but I
don’t want for-profit companies to profit from my art” (22.80%). This suggests
that artists are not entirely against their art being used in AI art model
training data but oppose for-profit companies benefiting financially. We
describe the survey results in detail in Section 3 and determine which
characteristic variables of our survey participants are most associated with
these results. We will present the survey methodology in Section A.1.

Our paper draws on our team’s multidisciplinary background – in art and art
history, social science, complex systems, computer science, mathematics, and
statistics – to address these questions and understand the possible impact of
Generative AI on artists. We hope this work will provide a modest step towards
understanding artists’ opinions on Generative AI technology, its impact on
their field, and the role they would like their work to play in the burgeoning
field of Generative AI.

##  2\. Background

There is ongoing disagreement regarding whether the output of Generative AI
can be considered art (AI, 2023a; Commission, 2023; Jiang et al., 2023); we
will not examine the topic in depth here. For the sake of convenience, we will
refer to the comparable output of AI as “art” throughout this paper. However,
this is not intended to refute (or endorse) the claim that AI cannot create
art.

According to work by Jiang et al. (Jiang et al., 2023), “following
philosophers of art and aesthetics from varied disciplines… we define art as a
uniquely human endeavor connected specifically to human culture and
experience.” They distinguish between inspiration/training and
technique/creation. They also point out that a person being able to mimic
another person’s artistic style so perfectly as to be indistinguishable is
rare, as good forgeries are typically noteworthy. They claim that these
aspects, among others, differentiate between what humans and AI can create.

Another dimension of debate concerning whether Generative AI creates art is
whether the output of the model is copyrightable under US law. The Courts in
the United States recently rejected for copyright a work the plaintiff
described as “autonomously created by a computer algorithm running on a
machine,” saying there is a “consistent understanding that human creativity is
the sine qua non at the core of copyrightability, even as that human
creativity is channeled through new tools or into new media” (US District
Court for the District of Columbia, 2023).

Among the arguments intended to distinguish human art from AI-generated art,
we find three that are particularly compelling. First, human art is the
product of activity without “organic pressure” – allowing us to distinguish
between the music of people, songbirds, and AI models (Jiang et al., 2023).
Second, that the world and the artist are always tightly coupled (Jiang et
al., 2023), in part through corporeality (Pfeifer and Bongard, 2006). Third,
that art plays a special role in the lived experience of humans (Huyghe,
1970).

Regardless of whether AI can make art, the output of Generative AI can meet
the minimally viable criteria for a commercial product, like the cover of a
book (Crabapple, 2022) (see Sec. 2.3), which means it is making its way into
some of the many places art currently lives in our world, impacting artists
and others along the way.

It is important to note that, although how technology can be incorporated into
art may be up for debate, that technology is part of the artistic process is
not new or ambiguous. Artists have always used tools (Huyghe, 1970), and many
art forms involve machines (such as woodwork, metalwork, and digital art) and
automation (such as digital printers and cameras). Some artists also choose to
use technology in ways other than its intended purpose or develop new art-
making techniques by breaking technology. Such artists may be both suspicious
and intrigued by the technology they use, questioning the relationship between
humans and machines and expanding art practices and processes. When presented
with new technology, an artist may ask questions like “How can I keep my work
with the machine intentional? How might this technology distract me? Is the
machine using me, or am I using it? Is this image-making or art-making? How
does this automation serve my artistic intentions and practice?” (Karson,
2023).

In the case of machine learning, a crucial question for many artists is “What
data was used to train this model?” (See Sec. 3.) Such inquiries are part of
the artistic tradition of challenging the status quo and are in stark contrast
to how the creators of Generative AI models have presented their tools for use
to the general public (AI, 2023a).

###  2.1. Generative AI

The field of Generative AI has created much excitement around its relevant
applications, both anticipated and emergent (Harby, 2023; Bubeck et al., 2023;
Abdin et al., 2023). Its valuation of tens of billions of dollars (Jiang et
al., 2023; for Human-Centered Artificial Intelligence, 2023; Crabapple, 2022)
has, of course, inspired a gold rush of research (academic, industrial, and
otherwise) and media coverage (Shan et al., 2023; Gozalo-Brizuela and Garrido-
Merchán, 2023). Many thousands of models are available and accessible (Face,
2023), along with an ecosystem of tools supporting their use. (Gozalo-Brizuela
and Garrido-Merchán, 2023) The space of related work being explored,
therefore, extends from well-established benchmarking (Abdin et al., 2023;
Lynch, 2023; for Human-Centered Artificial Intelligence, 2023), all the way to
cynical (Nicoletti and Bass, 2023), often predatory (Hall, 2023), money-making
schemes and fraud (Horton, 2023). Since 2010, the number of yearly AI-related
publications has only grown, reaching nearly 500,000 in 2021 alone (for Human-
Centered Artificial Intelligence, 2023).

The core functionality of a Generative AI model is as follows: based on input
from a person – through a user interface, as a conversational text prompt, or
as a piece of media to use as a starting point, such as a video, text, code
snippet, or an image – Generative AI models can produce novel output in an
equal variety of media (see Fig. 2) (Gozalo-Brizuela and Garrido-Merchán,
2023).

Currently, the major players in textual Generative AI are large language
models (LLMs), such as OpenAI’s ChatGPT and GPT-4 (Zhao et al., 2023; AI,
2023b). These models work by learning how to provide a plausible next word in
response to a sequence of text, which results in seemingly fluent linguistic
output (Zimmerman et al., 2023). In the visual sphere, models like
StableDiffusion and Midjourney (Holz and team, 2024) can modify an image by
iteratively tweaking random noise until they create an image that matches the
user’s input. The model decides which part of the image needs to be modified
at any given time based on the diffusion process (Andrew, 2023).

For the purposes of our paper, we define Generative AI as being a subset of
tools or processes that combine massive probability distributions with sense-
making algorithms in order to create output that, taken as a whole, was not
included in the training data (Jiang et al., 2023). The probability
distribution should be based on the training data. The sense-making algorithm
could be based on several strategies, such as a physical process like
diffusion or an architectural approach like transformers/neural nets, although
in the end, it should produce something akin to a latent/embedding space (Shan
et al., 2023); a place to hold meaning (Gozalo-Brizuela and Garrido-Merchán,
2023; Zhao et al., 2023; Grand et al., 2022). Research is ongoing into the
various distributions and processes that can be implemented in Generative AI
models to achieve specific behaviors and results (Xu et al., 2023). However,
irrespective of details, all of these models use advanced architectures in
conjunction with large amounts of data gathered through brute-force methods
across vast swathes of the publicly accessible internet (Xu et al., 2023; Zhao
et al., 2023; Salkowitz, 2022).

Figure 2. Generative AI models can produce novel output in various media such
as video, text, code snippet, or image. This output is based on the input from
an individual through a user interface, conversational text prompt, or a piece
of media, as mentioned in a survey conducted by Gozalo Brizuela (Gozalo-
Brizuela and Garrido-Merchán, 2023). The technology is advancing rapidly, and
in many contexts, the output generated by these models is indistinguishable
from that of a human.

A schematic of typical inputs to Generative AI models such as text and images
and the resulting outputs (e.g., text, images, video, code, music)

Here, we are interested specifically in how artists perceive Generative AI,
what its impact on art-related careers might be, and what moral, ethical,
artistic, and financial implications are coming hand-in-hand with the broader
flourishing of Generative AI (Shan et al., 2023; Baio, 2022; Crabapple, 2022;
Murphy, 2022; for Human-Centered Artificial Intelligence, 2023; Gozalo-
Brizuela and Garrido-Merchán, 2023; Zhao et al., 2023). We, therefore, touch
on four relevant topics: first, related academic work; second, the economic
context for this discussion; third, what sort of stages are within the
pipeline of Generative AI models and how those stages can each cause harm (a
specific and noteworthy type of impact); fourth, an example of how a previous
technological change caused upheaval within a creative field.

Although in our paper we focus more on the pressing potential for harm, it is
worth noting that there is also growing excitement around Generative AI’s
capabilities and how it could be incorporated into artistic processes (Karson,
2023). For example, artist Cosmo Wenman worked with blind researchers Brandon
Biggs, Lindsay Yazzolino, and Joshua Miele to create images using the
Midjourney tool (Holz and team, 2024). Their team noted that programs like
Midjourney can find “weird and thematic intersections” in art that go
unnoticed by human eyes. They also discussed the role of chaos in creating
“visual intrigue” and how technology can be used to “enhance agency.” Wenman
excitedly called the new technology “essentially moveable type for visual
rhetoric” (Schrader, 2023).

###  2.2. Related work

Jiang et al.(Jiang et al., 2023) analyze Generative AI art models and their
potential impact on the world through the lens of various academic sources
(both philosophical and scientific). They describe many opportunities for harm
as a result of such products: violation of the consent and privacy of artists
(Crabapple, 2022; Murphy, 2022; Baio, 2022; Ho, 2024; the Midjourney team,
2023), a lack of dataset transparency (shielding violations and biases) (Ho,
2024; the Midjourney team, 2023), a failure to uphold the contextual integrity
of content (Nissenbaum, 2004), harm to the livelihood of the artist, biases in
the training data, automating processes in a way that is detrimental to the
creation of art, reduced accessibility, erasure of human works by obscurity,
and increasing reluctance by artists to share their works openly (Crabapple,
2022).

The harms Jiang et al. (Jiang et al., 2023) foresee for artists fall into two
categories, financial and emotional, which manifest primarily as the
replacement of a human artist on a project, the impersonation of an artist
without consent (Baio, 2022; Murphy, 2022; Crabapple, 2022; Gozalo-Brizuela
and Garrido-Merchán, 2023), or plagiarism. Impersonation, particularly in this
context, could be weaponized for nefarious purposes (Horton, 2023; Ho, 2024;
the Midjourney team, 2023). Moreover, these two kinds of harm, enacted against
individuals at scale, create societal-level harm. Specific societal harms
Jiang et al. discuss in their work include data laundering – through academic
or open-source projects where public funds are funneled into research with
ultimately private profits – the proliferation of stereotypes and “hegemonic
views,” and erasure by obscurity (i.e., that AI-generated outputs can be
created much more quickly than human ones). They also point out that US
copyright and intellectual property jurisprudence needs to be better equipped
to deal with these new tools and that open sourcing, often called on as a
solution to software-related problems, is incomplete in this case (Jiang et
al., 2023).

As possible solutions, Jiang et al. bring up regulations regarding opt-in
consent (for training data), independent funding (much current research is
supported by industry), and education about how power interacts with
technology. In particular, they note there are many incentives to label work
as “apolitical” and that the spin capitalism is putting on Generative AI is
the “democratization of art” (and other fields) (Jiang et al., 2023).

Going beyond Jiang et al.’s list of solutions, Shan et al. (Shan et al., 2023)
propose tools allowing artists to “poison” models that scrape their work
without permission. The tool they created (Nightshade) can “destabilize
general features” in the targeted generative model and “bleed-through” to
semantically similar prompts without injecting large amounts of tailored data
(Shan et al., 2023).

###  2.3. Economic context: livelihood of the artist

AI (a term which includes, but is not limited to, Generative AI) is
increasingly working its way into the world economy. Across nearly every
economic sector for which data is gathered, AI-related job postings have been
increasing (for Human-Centered Artificial Intelligence, 2023). Globally,
employers in a broad range of fields seek AI-skilled workers, and every year,
many companies decide to adopt AI tools. The top benefit these companies claim
to see in response to that decision is lowered costs (for Human-Centered
Artificial Intelligence, 2023), including through layoffs and replacing
employees with AI (Korn, 2024). On the other end of the employment ladder,
workers in an increasing variety of fields list AI as a relevant skill (for
Human-Centered Artificial Intelligence, 2023).

Some of these impacts are already coming to bear on the economic reality of
artists. Entry-level positions in film, TV, and gaming seem particularly at
risk, and the conceptual period of a project seems to be a popular phase in
which to replace artists with AI (Carter, 2023). Traditional niches like
illustrating book covers can now be filled by the output of Generative AI
(Crabapple, 2022; Chalk, 2024), and major employers can reduce the number of
employees needed (for example, Netflix Japan’s layoff of animators) (Jiang et
al., 2023).

It isn’t just the output of AI models that can have an economic impact on
artists, however. Harm, both financial and emotional, can occur based on
whether people have – or even could have – consented to the content they
created and shared in a certain context (Nissenbaum, 2004) (especially in the
complex context of social media, e.g., an artist with a physical gallery
presence whose work was photographed by a visitor) being repurposed by machine
learning models and the people – and companies – who control or use them
(Solon, 2019; Baio, 2022; Ho, 2024; the Midjourney team, 2023). The harm done
by the AI model can be incipient before the AI model was ever conceived of;
the complications of the model creation pipeline are discussed in more detail
in Sec. 2.4.

Furthermore, the issue of copyright protection for artworks (made by humans)
to train commercial AI models (without consent, attribution, or sufficient
compensation to human authors) is a matter of concern for artists, creative
industries, and federal agencies (Commission, 2023; Cho, 2023; Team, 2024;
Patel and Palazzolo, 2024; David, 2024; Marcus and Southen, 2024).

###  2.4. Problems in the pipeline of Generative AI tools

As mentioned, when we consider the impact of an AI model, the impact is not
just how the model’s output is used or how the model itself is deployed (as a
replacement for human workers, etc.). Each stage within the pipeline of
creating a Generative AI model can contribute to the eventual harm done by
(and therefore the impact of) the model, even though most stages, with the
sometimes exception of the two endpoints (input and output), are relatively
inaccessible. In this section, we outline the pipeline of creating a
Generative AI model, and we examine one particularly pervasive harm, bias
(Zhao et al., 2023; Gallegos et al., 2023), in order to explain how multiple
stages within the pipeline can contribute to the model’s impact.

When creating an AI model, training materials must be gathered (often a subset
of artifacts from the real world) (Salkowitz, 2022); those materials must be
groomed; poor quality, irrelevant, or over-represented artifacts should be
removed or otherwise addressed (Luccioni and Viviano, 2021; Zhao et al.,
2023). The architecture of the model itself must be assembled. The model must
be trained. For complex Generative AI models whose behavior can be
unpredictable, there is the additional process of alignment, which is intended
to bring the model’s range of possible — or at least likely – behavior in line
with whatever the creators of the model deem desirable (Zhao et al., 2023).

Very early in the pipeline, choosing the training material has a significant
influence on the model’s eventual behavior. Important datasets used as
benchmarks for machine learning have been found to be “overwhelmingly composed
of lighter-skinned subjects” (Buolamwini and Gebru, 2018), introducing bias
obliquely into the model (the global bias introduced would not be caught by
locally vetting each piece of training data for harmful content). However,
there is also explicitly objectionable and biased content in the datasets
themselves – such as racist hate speech and harmfully sexualized images
(Luccioni and Viviano, 2021; Cole, 2023) – even after cleaning procedures.

Midway through the pipeline, in the interior of the trained model, pieces as
basic and abstracted as the meaning of common English words have been found to
encode gender biases (Bolukbasi et al., 2016). Near the end of the pipeline,
the output produced by the model can instantiate bias. For example, the Stable
Diffusion model disproportionately generates images of white male executives,
doctors, lawyers, judges, and law enforcement, and also disproportionately
generates images of black and brown criminals and service workers (Nicoletti
and Bass, 2023; Ghosh and Caliskan, 2023). Additionally, some users have found
that Generative AI tools readily produced images in which they looked younger,
more sexualized, or whiter, even in cases where their prompt did not request
or hint at those parameters (Snow, 2022; Ghosh and Caliskan, 2023; Hall,
2023).

These examples are not unique; similar biases are found across current AI
models and products – including those of Generative AI – at every level, from
the training data they learned from to their internal representations to their
outputs (Luccioni and Viviano, 2021; Ghosh and Caliskan, 2023; Cheng et al.,
2023; Gallegos et al., 2023; Zhao et al., 2023; Common Crawl, [n. d.]; Cole,
2023).

As a final problem in the pipeline, it is worth noting that these AI tools
cannot be created ex nihilo: they are integrated into complex human systems
that bring their own forces to bear on the model’s potential impact. There are
discrepancies in the demographics of who is wielding the power of these new
technologies: who is making them (for Human-Centered Artificial Intelligence,
2023), profiting off them, using them. This can have many knock-on effects,
sometimes through mechanisms difficult to foresee (Even-Tov et al., 2023),
with respect to the design, use, and role of these tools in society (Jiang et
al., 2023; for Human-Centered Artificial Intelligence, 2023). In particular,
the companies behind these products often employ a great deal of secrecy as to
their training data, architecture, or implementation (or all three) (Ho, 2024;
the Midjourney team, 2023). As a result, users must interact with the product
as a black box (Jiang et al., 2023; Marcus and Southen, 2024); concerned users
can only conjecture as to what kind of training data could produce the
observed results based on what is known of the basic architecture of the
model, which in turn allows argument over the obscure details (Team, 2024;
Masse, 2024). The lack of transparency benefits the companies offering these
products (with respect to establishing the impact, specifically the harms, of
the model) in giving them a way to sidestep moral and ethical concerns (Marcus
and Southen, 2024; Masse, 2024).

###  2.5. Previous disruption: Napster’s example

Similarities exist between the early years of the Peer-to-Peer music sharing
platform Napster and the nascent stage of Generative AI tools. Both
technologies were exciting and disruptive, with the potential for financially
high stakes (Jiang et al., 2023; for Human-Centered Artificial Intelligence,
2023; Bubeck et al., 2023; Bergen, 2002). Both also forged ahead despite
glaring questions of legality, particularly with respect to copyright
(Grynbaum and Mac, 2023). In Napster’s wake, legal scholars debated whether
contemporaneous copyright law did or should apply to the digital age, and they
wondered whether the public’s perception of copyright laws had changed due to
Napster’s large user base and the demand for free or reduced-cost music (Ku,
2002; Bergen, 2002). Ultimately, Napster shut down, having underestimated the
legal battles it would face (Green, 2002; YellowBrick, 2023).

The argument that Generative AI technologies should be able to be “taught”
their downstream abilities (to speak a language, to reason, to create various
output) (Masse, 2024; Team, 2024; team, 2023) by much of the (ostensibly
(Marcus and Southen, 2024)) publicly available Internet (Salkowitz, 2022) – or
that, morality aside, to prevent them would be infeasible – is a more complex
version of the argument made by Napster’s founders and early users who
insisted that music should (or would) be available to the public for free
(Marcus and Southen, 2024).

In the face of such arguments, it is worth noting that openness is not a good
in and of itself, and can lead to its own harms, such as violations of
contextual integrity or privacy (Nissenbaum, 2004). Furthermore, the forces,
often economic, that shape underlying practices like web scraping (Common
Crawl, [n. d.]; Salkowitz, 2022) may not be the forces best suited to
beneficently shaping technological development (AI, 2023b).

The apparent success of music distribution platforms such as Apple Music and
Spotify (Wise, 2023) – and the absence of Napster – suggests that there is
significant doubt as to the eventual triumph of Generative AI models over
pending legal challenges. As of now, at least, there are no exceptions for AI
companies regarding US copyright law (Commission, 2023).

##  3\. Results

Our anonymous survey of artists is an observational study (N=459) of artists
over the age of 18 years (we report participant demographics in Table 1). We
see from the responses to our primary research questions (see Table 2) that
80.17% of the survey participants agree that model creators should be required
to disclose in detail what art and images they use to train their AI models.
We also find that 61.87% of survey participants agree that AI models are a
threat to art workers. In contrast, 44.88% of survey participants agree that
AI art models are a positive development in the field of art, notably with
27.02% of artists selecting neutral on the Likert scale for this question.
Interestingly, we see an overlap between artists who agree that AI art is a
positive development and those who also agree that it is a threat to art
workers; 22.03% of survey participants agreed that both AI was a positive
development and a threat.

We look at the question that asks participants to think about a scenario where
an AI art model was used by someone else to produce artwork recognizably in
their (the survey participant’s) style and think about who should own the work
and its derivatives. Only 26.80% of the participants agreed that the work and
its derivatives be considered the property of the AI model creators, while
39.22% of participants agreed that the work and its derivatives be considered
the property of the person who used the AI model to generate the artwork.
Finally, 41.39% of the participants agreed that the work and its derivatives
be considered the property of the artists whose style is being represented by
the AI model output. In the following sections, we will analyze which
variables are most associated with participants in our survey study who agreed
with the primary research questions.

Type | Count (%)
---|---
Male | 273 (59.48%)
Female | 163 (35.51%)
Non-binary | 23 (5.01%)
White | 361 (78.65%)
Person of Color | 98 (21.35%)
Age 30-49 | 233 (50.76%)
Age 18-29 | 104 (22.66%)
Age 50-64 | 79 (17.21%)
Age 65 or older | 43 (9.37%)
Table 1. Descriptive statistics for our survey participants, N=459.

We also look at what types of compensation artists consider fair in exchange
for the use of their artwork to train an AI art model. The results for this
question can be seen in Table 3. We find that 50.97% of survey respondents
stated that they did not need compensation, but what matters to them is who is
profiting from the use of their artwork. The fair compensation option selected
most by our survey participants was “I don’t need to profit, but I don’t want
for-profit-companies to profit from my art” (22.80% selected this option),
which indicates that artists are not wholesale against art being used in AI
art models training data, but they oppose for-profit-companies reaping the
financial benefits. Our results show that 36.56% of respondents did state they
would find some type of compensation fair, only 1.29% of participants felt
that a tax on companies and individuals who profit from the AI art outputs
would be a fair option, and 11.18% of participants felt that they would not
feel comfortable with any of the compensation options listed.

Question | Strongly Disagree | Disagree | Neutral | Agree | Strongly Agree
---|---|---|---|---|---
AI threat to art workers | 6.32% | 14.16% | 17.65% | 33.77% | 28.10%
AI positive development for art | 13.07% | 15.03% | 27.02% | 22.88% | 22.00%
Required disclosure | 2.83% | 5.23% | 11.98% | 32.03% | 48.15%
Output owned by model creators | 32.46% | 24.62% | 18.74% | 15.25% | 11.55%
Output owned by AI user | 19.61% | 22.22% | 21.57% | 20.92% | 18.30%
Output owned by the artist | 9.37% | 26.58% | 25.05% | 22.00% | 19.39%
Table 2. Participant answers to our primary research questions by percent
(answered on a 5-point Likert scale), N=459.

Descriptive statistics about our survey responses to demographic questions can
be seen in Table 1. A full table of descriptive statistics that outlines
participants who have used AI art models, their familiarity with Generative
AI, professional and artistic status, whether or not they have purchased art,
and art practices can be seen in Table 6.

Type | Counts (%)
---|---
I don’t need to profit, but I don’t want for-profit-companies to profit from my art | 106 (22.8%)
I would donate the use of my artwork to train AI art models (no compensation) | 82 (17.63%)
A flat fee | 55 (11.83%)
A portion of any profit made From the model creators | 51 (10.97%)
Not comfortable with any listed options | 42 (9.03%)
A portion of any profit from people who profit from derivative works made using the model | 32 (6.88%)
A portion of any profit made | 32 (6.88%)
I don’t need to profit, but I don’t want anyone else to profit from my art | 27 (5.81%)
I don’t need to profit | 22 (4.73%)
Other | 10 (2.15%)
Tax | 6 (1.29%)
Table 3. Counts percent per compensation type.

###  3.1. RQ1: Do artists see AI models as a threat to art workers?

Our results show that 61.87% of survey participants believe that AI models
pose a threat to art workers. This finding is related to survey question no.
15 (seen in Section B). An ordinal logistic regression (OLR) was conducted to
investigate the association between the Likert scale answers to RQ1 (dependent
variable) and the secondary variables (as shown in Figure 3) in terms of the
odds ratio of the OLR.

The results demonstrate a significant association (p-value ¡ 0.05, indicated
in Table 4) between agreeing (represents a combination of both the responses
‘Agree’ and ‘Strongly Agree’ in the Likert) that AI models are a threat to art
workers and age, as well as whether or not the participants have used AI to
create art.

Figure 3. The ordinal logistic regression results show the association between
the answering ‘Agree’ or ‘Strongly Agree’ to RQ1 (AI models as a threat to art
workers) and other survey variables. Results are represented by odds ratios on
a log scale on the x-axis, with bars conveying a 95% confidence interval and
red indicating a p-value ¡ 0.05 and a confidence interval that does not cross
the threshold of the dotted line in the figure.

A forest plot centered on $10^{0}$ that shows the odds ratio and the 95%
confidence interval on the x-axis and the variable on the y-axis. The ordinal
logistic regression results show the association between answering ‘Agree’ or
‘Strongly Agree’ to RQ1 and the survey’s secondary variables. Results are
represented by odds ratios on a log scale on the x-axis, with bars conveying a
95% confidence interval and red indicating a p-value ¡ 0.05 and a confidence
interval that does not cross the threshold of the dotted line in the figure.

Secondary Variable Participant Characteristic |  Primary Variable Likert Question |  Odds of Agreeing with RQ | p-value
---|---|---|---
Used AI to make art | RQ1: AI models are a threat to art workers | 1.763 | 0.005
18-29 years old | RQ1: AI models are a threat to art workers | 2.329 | 0.002
Not makers | RQ2: AI art models are a positive development in art | 13.513 | 0.047
30-49 years old | RQ2: AI art models are a positive development in art | 1.535 | 0.039
Male and non-binary | RQ2: AI art models are a positive development in art | 1.663 | 0.007
Male | RQ2: AI art models are a positive development in art | 1.570 | 0.012
Not an artists | RQ2: AI art models are a positive development in art | 1.736 | 0.019
White | RQ2: AI art models are a positive development in art | 1.494 | 0.004
Used AI to make art | RQ2: AI art models are a positive development in art | 5.178 | ¡0.001
Compensation: Would donate their - artwork to train models | RQ2: AI art models are a positive development in art | 15.444 | ¡0.001
18-49 years old and ages above 65 | required disclosure from model creators | 2.439 | 0.001
18-29 years old | RQ3: required disclosure from model creators | 2.874 | 0.003
30-49 years old | RQ3: required disclosure from model creators | 1.777 | 0.028
Familiarity with AI art generally | RQ3: required disclosure from model creators | 2.620 | 0.003
Not an illustrator | RQ3: required disclosure from model creators | 4.822 | 0.026
Compensation: a portion of any - profit from derivatives | RQ3: required to disclose | 6.461 | 0.038
Compensation: portion - of any profit made | RQ3: required disclosure from model creators | 4.213 | 0.042
Has not purchased art | RQ4_A: AI users owners of AI art made | 1.773 | 0.039
Male | RQ4_AA: users owners of AI art made | 2.019 | ¡0.001
Male and non-binary | RQ4_A: I users owners of AI art made | 2.197 | ¡0.001
Compensation: did not select “not comfortable - with any listed compensation type” | RQ4_A: users owners of AI art made | 4.764 | 0.018
Person of color | RQ4_B: Artists owners of AI art made | 2.147 | ¡0.001
Not used AI to make art | RQ4_B:Artists owners of AI art made | 1.523 | 0.0255
Person of color | RQ4_C: Model creators owners of AI art made | 2.491 | ¡0.001
Male | RQ4_C:Model creators owners of AI art made | 2.033 | ¡0.001
Male or non-binary | RQ4_C: Model creators owners of AI art made | 1.949 | ¡0.001
Female or male | RQ4_C: Model creators owners of AI art made | 2.815 | 0.033
Not have a familiar with AI art generally | RQ4_C: Model creators owners of AI art made | 3.671 | ¡0.001
Tattooists | RQ4_C: Model creators owners of AI art made | 6.674 | 0.042
Compensation: None, donate use - of artwork to train models | RQ4_C: Model creators owners of AI art made | 6.074 | 0.010
Table 4. Table represents the results of an ordinal logistic regression (OLR)
to explore the relationship between our dependent variable (the Likert scale
answers to each research question RQ1-4) and our secondary variables. The odds
ratio is represented as the likelihood that the participants who selected a
particular secondary variable agree or strongly agree with the primary
variables research question. For example, for participants who have not used
AI to make art, the odds of being more likely to agree that AI models are a
threat to art workers is 1.7633 times that of participants who have used AI to
make art (p-value=0.005). We consider evidence of a strong association to be a
p-value less than 0.05.

###  3.2. RQ2: Do artists see AI art models as a positive development in the
field of art?

We ran the same analysis for our second research question and found that
44.88% of the survey participants agree that AI art models are a positive
development in the field of art. Notably, 27.02% of artists selected ’neutral’
on the Likert scale for this question. This analysis pertains to survey
question number 14 (see the section on survey questions B). We ran an ordinal
logistic regression (OLR) to explore the relationship between the Likert scale
answers to RQ2 (the dependent variable) and the secondary variables (as seen
in Figure 4, which shows the odds ratio of the OLR).

Our analysis indicates a strong association (p-value ¡ 0.05, as seen in Table
4) between agreeing that AI art models are a positive development in the field
of art and various factors such as art practice, age, gender, artistic status,
race, whether or not they have used AI to make art and compensation.

Figure 4. The ordinal logistic regression results show the association between
the answering ‘Agree’ or ‘Strongly Agree’ to RQ2 (AI models as a positive
development) and survey secondary variables. Results are represented by odds
ratios on a log scale on the x-axis, with bars conveying a 95% confidence
interval and red indicating a p-value ¡ 0.05 and a confidence interval that
does not cross the threshold of the dotted line in the figure.

A forest plot centered on $10^{0}$ that shows the odds ratio and the 95%
confidence interval on the x-axis and the secondary variable on the y-axis.
The ordinal logistic regression results show the association between answering
‘Agree’ or ‘Strongly Agree’ to RQ2 and the survey’s secondary variables.
Results are represented by odds ratios on a log scale on the x-axis, with bars
conveying a 95% confidence interval and red indicating a p-value ¡ 0.05 and a
confidence interval that does not cross the threshold of the dotted line in
the figure.

###  3.3. RQ3: What types of disclosure for the use of artwork to train an AI
art model do artists consider fair?

We see from our survey results that 80.17% of the survey participants agree
that creators of AI models should disclose the art and images used to train
their models in detail. The analysis for RQ3 is related to survey question 13
(seen in Section B), which we used to run an ordinal logistic regression (OLR)
to explore the relationship between the Likert scale answers to RQ3 and
secondary variables. The results of the OLR, displayed in Figure 5 as the odds
ratio, showed a strong association (p-value ¡ 0.05, as seen in Table 4)
between agreeing with the disclosure requirement and factors such as art
practice, age, familiarity with AI, whether or not they have used AI to create
art and compensation preference.

Figure 5. The ordinal logistic regression results show the association between
answering ‘Agree’ or ‘Strongly Agree’ to RQ3 (Should model creators be
required to disclose in detail what art & images they use to train their AI
models) and survey secondary variables. Results are represented by odds ratios
on a log scale on the x-axis, with bars conveying a 95% confidence interval
and red indicating a p-value ¡ 0.05 and a confidence interval that does not
cross the threshold of the dotted line in the figure.

A forest plot centered on $10^{0}$ that shows the odds ratio and the 95%
confidence interval on the x-axis and the secondary variable on the y-axis.
The ordinal logistic regression results show the association between answering
‘Agree’ or ‘Strongly Agree’ to RQ3 and the survey’s secondary variables.
Results are represented by odds ratios on a log scale on the x-axis, with bars
conveying a 95% confidence interval and red indicating a p-value ¡ 0.05 and a
confidence interval that does not cross the threshold of the dotted line in
the figure.

###  3.4. RQ4: From an artist’s perspective, who should own the derivative
artwork created by AI art models?

Finally, we look at the question that asks participants to think about a
scenario where an AI art model was used by someone else to produce artwork
recognizably in their (the survey participant’s) style and think about who
should own the work and its derivatives. The analysis for RQ4 is related to
survey questions B No. 8,9 and 10.

RQ4_A: If an AI art model was used by someone else to produce artwork
recognizably in your style (e.g., in the style of Van Gogh), Should that work
and its derivatives be considered the property of the person who used the AI
model to generate the artwork? We see that 39.22% of participants agreed that
the work and its derivatives be considered the property of the person who used
the AI model to generate the artwork. We run an ordinal logistic regression
(OLR) to explore the relationship between our dependent variable (the Likert
scale answers to RQ4_A) and our secondary variables, as seen in Figure 6 as
the odds ratio of the OLR.

Figure 6. The ordinal logistic regression results show the association between
the answering ‘Agree’ or ‘Strongly Agree’ to RQ4_A (If an AI art model was
used by someone else to produce artwork recognizably in your style (e.g., in
the style of Van Gogh) Should that work and its derivatives be considered the
property of the person who used the AI model to generate the artwork?) and
survey secondary variables. Results are represented by odds ratios on a log
scale on the x-axis, with bars conveying a 95% confidence interval and red
indicating a p-value ¡ 0.05 and a confidence interval that does not cross the
threshold of the dotted line in the figure.

A forest plot centered on $10^{0}$ that shows the odds ratio and the 95%
confidence interval on the x-axis and the secondary variable on the y-axis.
The ordinal logistic regression results show the association between answering
‘Agree’ or ‘Strongly Agree’ to RQ4_Aand the survey’s secondary variables.
Results are represented by odds ratios on a log scale on the x-axis, with bars
conveying a 95% confidence interval and red indicating a p-value ¡ 0.05 and a
confidence interval that does not cross the threshold of the dotted line in
the figure.

We see evidence of a strong association (p-value ¡ 0.05, seen in Table 4)
between agreeing that the person who used the AI model to generate the artwork
should be the owner, and gender, having purchased art, race, compensation, and
having used AI to make art.

RQ4_B: If an AI art model was used by someone else to produce artwork
recognizably in your style (e.g., in the style of Van Gogh and you are Van
Gogh), Should that work and its derivatives be considered yours?

Our survey shows that 41.39% of the participants agreed that the work and its
derivatives should be considered their property (the artist whose style is
being recognizable and mimicked by the Generative AI art model). To explore
the relationship between the Likert scale answers to RQ4_B and the secondary
variables, we ran an ordinal logistic regression (OLR), as shown in Figure 7
with the odds ratio of the OLR. Table 4 shows that there is strong evidence of
an association (p-value ¡ 0.05) between agreeing that the artist whose style
is recognizable in the output should be the owner of the art and the
participant’s race and whether they have used AI art models to create art.

Figure 7. The ordinal logistic regression results show the association between
the answering ‘Agree’ or ‘Strongly Agree’ to RQ4_B (If an AI art model was
used by someone else to produce artwork recognizably in your style (e.g., in
the style of Van Gogh and you are Van Gogh) Should that work and its
derivatives be considered yours? and survey secondary variables. Results are
represented by odds ratios on a log scale on the x-axis, with bars conveying a
95% confidence interval and red indicating a p-value ¡ 0.05 and a confidence
interval that does not cross the threshold of the dotted line in the figure.

A forest plot centered on $10^{0}$ that shows the odds ratio and the 95%
confidence interval on the x-axis and the secondary variable on the y-axis.
The ordinal logistic regression results show the association between answering
‘Agree’ or ‘Strongly Agree’ to RQ4_B and the survey’s secondary variables.
Results are represented by odds ratios on a log scale on the x-axis, with bars
conveying a 95% confidence interval and red indicating a p-value ¡ 0.05 and a
confidence interval that does not cross the threshold of the dotted line in
the figure.

RQ4_C: If an AI art model was used by someone else to produce artwork
recognizably in your style (e.g., in the style of Van Gogh), Should that work
and its derivatives be considered the property of the AI model creators?

Figure 8. The ordinal logistic regression results show the association between
the answering ‘Agree’ or ‘Strongly Agree’ to RQ4_C (If an AI art model was
used by someone else to produce artwork recognizably in your style (e.g., in
the style of Van Gogh) Should that work and its derivatives be considered the
property of the AI model creators?) and survey secondary variables. Results
are represented by odds ratios on a log scale on the x-axis, with bars
conveying a 95% confidence interval and red indicating a p-value ¡ 0.05 and a
confidence interval that does not cross the threshold of the dotted line in
the figure.

A forest plot centered on $10^{0}$ that shows the odds ratio and the 95%
confidence interval on the x-axis and the secondary variable on the y-axis.
The ordinal logistic regression results show the association between answering
‘Agree’ or ‘Strongly Agree’ to RQ4_C and the survey’s secondary variables.
Results are represented by odds ratios on a log scale on the x-axis, with bars
conveying a 95% confidence interval and red indicating a p-value ¡ 0.05 and a
confidence interval that does not cross the threshold of the dotted line in
the figure.

According to our survey results, only 26.80% of the participants believed that
the work and its derivatives should be considered the property of the creators
of AI models. To examine the relationship between the answers to RQ4_C on the
Likert scale and other secondary variables, we conducted an ordinal logistic
regression (OLR), as illustrated in Figure 8.

Our analysis suggests a strong association (with a p-value below 0.05,
presented in Table 4) between agreement on AI companies owning the resulting
artwork and art practice, race, gender, familiarity with AI art models, and
compensation.

##  4\. Discussion

The impact of Generative AI on artists is a complex global issue with impacts
in many arenas, and it must be investigated through further multidisciplinary
collaboration. We hope this work contributes to the existing literature on the
ethics of AI-generated art and helps to provide a voice for artists on this
topic.

In this study, we surveyed how artists feel about Generative Artificial
Intelligence (AI) in art. Most participants believe that AI model creators
should be required to disclose the art and images used to train them – an
aspect of Generative AI tools that is currently far from transparent (see Sec.
2.4). However, only a minority of participants see AI art models as a positive
development in art, while the majority see them as a threat to art workers.

When asked who should own the work and its derivatives when an AI art model is
used to produce artwork in a particular artist’s style, the minority of
participants believed that the AI model creators should own the work. In
contrast, the majority of survey respondents were split between thinking it
should belong to the person who used the AI model or the artist whose style is
represented in the output.

Regarding compensation for using their artwork to train AI art models, over
half of the participants did not require any compensation but were concerned
about who was profiting from their artwork. Tentatively, we think this answer
could reflect a belief within the artistic community: that art is the joint
responsibility of all people (Huyghe, 1970). Corollary to that, there would be
some onus on artists to share their work with each other and with the public,
and there may be resistance to perceived exploitation of that practice,
especially in the service of profit. We think this interpretation is
consistent with the result that some participants stated that they do not
require profit but do not want for-profit companies to profit from their art.
Others considered some compensation fair, while a minority felt taxing
companies and individuals who profit from AI art outputs would be a fair
option. Finally, some participants stated they would feel uncomfortable with
the compensation options listed. Understanding how the field of Generative AI
falls short in respecting the values of the people who make the cultural
artifacts it relies upon – and how that could be changed – is an important
ethical question that we hope to expand on in future work.

In our future work, we plan to increase the sample size and expand it to a
more representative sample. This will increase the statistical power of our
study and also allow us to look at how opinions change over time. Moreover, we
would like to put the current flourishing of Generative AI within the context
of art history and taxonomy. We believe that a longer timeline would help
consolidate, unify, and clarify the relationship between technology and art,
which we only briefly touched upon in Section 2.5. While it is essential to
understand what artists think about Generative AI, we need to collaborate with
experts from various fields, including art, history, philosophy, psychology,
cognitive science, and others, to understand the implications of Generative AI
on our understanding of art and its role in our lives.

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###### Acknowledgements.

Institutional Review Board Approval: The survey in this project is CHRBSS
(Behavioral) STUDY00002379, approved by the University of Vermont I.R.B. on
3/14/2023. The authors would like to thank Maria Sckolnick, John Meluso,
Kendall Fortney, Randall Harp, Laurent Hébert-Dufresne, Jean-Gabiel Young, and
Winn Gillen, for their comments on early versions of this work. The Alfred P.
Sloan Foundation, The National Science Foundation award #2242829, and
MassMutual under the MassMutual Center of Excellence in Complex Systems and
Data Science support this work. Any opinions, findings, conclusions, or
recommendations expressed in this material are those of the author(s) and do
not necessarily reflect the views of the aforementioned financial supporters.

##  Appendix A Research Methods

###  A.1. Survey Methodology

Our anonymous original survey of artists is an observational study (N=516) of
artists over the age of 18 years. To compare, we present a table of the
distributions of the US Census art workforce 5. Incomplete surveys were
dropped from the analysis, leaving a final N of 459.

Survey participants start by consenting to the IRB-approved survey information
sheet and consent agreement. We then asked participants to answer several
questions about their artistic identity and financial relationship with art.
Before continuing, participants are asked to read a brief describing AI art
and present an example. We then ask participants to disclose their familiarity
with AI art models and tools. Then, we asked participants several questions
about who they think “owns” art generated by AI models rated on a 5-point
Likert scale. We ask several questions that invite artists to identify their
ideal compensation criteria for their art being included in AI art models. We
then ask participants to assess if AI art models are positive for their field,
if they are a threat to their field, and if they think model creators should
be required to disclose their data sources on a 5-point Likert scale. Lastly,
we collect demographic information on the survey participants’ backgrounds.

Phase 1 Sampling: Survey participants for phase 1 (n = 252) of our
observational study were recruited broadly on social media, using flyers
locally at the University of Vermont, emailing artist groups, and direct
contacts who are professional artists. Survey participants who are over the
age of 18 and identify as an artist or work in an arts-related field were
eligible. Phase 1 of the surveys was collected through a convenience sample
from March 25, 2023, through September 8, 2023. Survey responses were
collected on Qualtrics, an online survey platform.

Phase 2 Sampling: Phase 2 (n = 264) was collected via a targeted survey panel
conducted by Qualtrics from September 12-23, 2023 on the Qualtrics online
survey platform. A link to the complete survey and survey questions is
available in Section B.

Descriptive statistics about the U.S. artist workforce are categorized as
artists-related workers (US Census code 27-1010), which can be seen in Table
5. Data comes from the 2020 US Census, US Census Bureau, and ACS PUMS 5-Year
Estimate. The occupational categories include Art Directors, Craft Artists,
Fine Artists, Painters, Sculptors, Illustrators, Special Effects Artists and
Animators, Artists and Related Workers, All Other Designers, Commercial and
Industrial Designers, Fashion Designers, Floral Designers, Graphic Designers,
Interior Designers, Merchandise Displayers and Window Trimmers, Set and
Exhibit Designers, Designers, All Other. This data demonstrates the number of
US workers potentially implicated by Generative AI art.

Type | Number
---|---
U.S. artist workforce | 144,000
Avg. Age | 41
Avg. Salary | $57,958
Avg Male Salary | 67,353
Avg Female Salary | 45,618
Male | 56.70%
Female | 43.30%
White (Non-Hispanic) | 73.20%
Asian (Non-Hispanic) | 7.92%
Black | 3.65%
Two or more | 3.44%
White (Hispanic) | 2.88%
American Indian | 0.01%
Native Hawaiian | 0.13%
Native other | 0.04%
Table 5. Descriptive statistics about the US artist workforce categorized as
artists-related workers (US Census code 27-1010). Data comes from the 2020 US
Census, US Census Bureau, and ACS PUMS 5-Year Estimate.

###  A.2. Data

Original Data

Our original survey of artists is a convenience survey (N=459). For comparison
of the distributions of the US art workforce) based on the demographics is
presented in Table 5.

We transformed all survey response variables of interest into numerical form
to analyze our survey results (survey questions 8,9,10,13,14,15 as seen in
Section B). All Likert survey questions were converted from ‘Strongly Agree,’
‘Agree,’ ‘Neutral,’ ‘Disagree,’ and ‘Strongly Disagree’ to an ordinal scale of
1,2,3,4,5. For our ordinal logistic regression analysis, we combine ‘Strongly
Agree’ and ‘Agree’ to be equal to 3, ’Neutral’ to be equal to 2, and
‘Disagree’ and ‘Strongly Disagree’ to be equal to 1. In the original logistic
regression, we reduced the dimensionality of the Likert scale to investigate
the positive, neutral, and negative sentiment of the response questions in
relation to our secondary variables.

All nominal and categorical variables (survey questions
1,2,3,4,5,11,12,16,17,18,19 as seen in Section B) were transformed into binary
variables. Survey questions that included an “other” option or write-in
answers were also categorized (e.g., in question 2, we added an additional art
practice, which included digital art from the write-in answers).

Demographic questions (survey questions 16, 17, 18, 19 as seen in Section B)
included asking survey participants their gender identity to identify whether
they were white or a person of color, choosing their age range and country of
residence. All demographic questions were transformed into binary variables.

###  A.3. Analytical Methods

Kruskal–Wallis Test The Kruskal-Wallis H-test is a non-parametric test that
extends the Mann-Whitney U-test to compare differences between groups for an
ordinal or continuous dependent variable. The explanatory groups may be ranked
or unranked (Statistics, 2015a). Its null hypothesis is stochastic
homogeneity, which is equivalent to equality of the expected value of rank
means, conditional on the homogeneity of rank variances (Vargha and Delaney,
1998).

Cochran–Armitage Test The Cochran-Armitage test for trend uses weighted linear
regression coefficients to test for trends in proportion between ordered
levels of the response variable for a binary response variable. Its null
hypothesis is a lack of trend, meaning that the proportions of each outcome
are the same for each explanatory level (Iskander et al., 1983).

Odds ratio In general, the odds of an event are the probability of success
over the probability of failure. This can be written

(1) $\frac{p}{1-p}$

where p is the probability of success. Unlike probability, odds can be from 1
to positive infinity (Bruin, 2011a). The odds ratio (OR, or $\theta$) is
simply the odds of one event divided by the odds of another. The OR can be
from 0 to positive infinity, with an OR equal to 1 indicating equal odds of
success between events. The natural logarithm of the OR is often used, as the
transformation renders the distribution symmetric about 0 (Agresti, 2012).

Ordinal Logistic Regression Ordinal logistic regression (OLR) is a
generalization of regression (either multiple linear or binomial logistic)
used for an ordered categorical response variable and any number of
categorical or numeric secondary variables (Statistics, 2015b). OLR finds the
logit (also known as log-odds) of the dependent variable (Y) being less than
or equal to a certain level (denoted j) (Bruin, 2011b). which can be written

(2) $logit(P(Y\leq j))=log\frac{P(Y\leq j)}{P(Y>j)}$

It is important to note that OLR assumes proportional odds, meaning the effect
of the secondary variable(s) is the same between all levels of the dependent
variable (Unit, 2016).

##  Appendix B Survey Questions

  1. (1)

Would you consider yourself to be an artist? (options: Yes; Somewhat; No; I’m
not sure)

  2. (2)

What kind(s) of art do you practice? (select all that apply) (options:
Photography; Paint; Drawing; Tattoo; Graphic Design; Illustration; Textile;
Cinematography; Sculpture; Craft; Maker; Designer; Architecture; Jewelry;
Writing; Music; Performance; Prefer not to respond; Not an artist but I work
in an art-related field; Not an artist, but I am an art enthusiast; Not an
artist; Other, please specify)

  3. (3)

Have you ever purchased a piece of art? (options: Yes; No; I’m not sure)

  4. (4)

Do you consider yourself to be a professional artist? (options: Yes; No;
Prefer not to respond)

  5. (5)

Please read the following description of Artificial Intelligence (AI) art
before continuing: AI art is any computer-generated artwork created through
the use of artificial intelligence. There are several ways to generate AI art.
Models used to generate AI art: Artificial intelligence algorithms use two or
more images to train (training is a process used where an algorithm is fed
data to learn from) a model to generate new AI art (e.g., Lensa, DeepDream).
Large language models use artificial intelligence, which uses large amounts of
text data to train a model to generate new AI language (e.g., GPT-3). Text-to-
image models use textual prompts (e.g., “a yellow elephant”) from a user to
generate AI art that depicts the textual prompt. The AI art is trained on
large amounts of image and text data to train the model and generate new AI
art (e.g., DALL-E, Imagen). Example: Generated by DALL-E mini/Craiyon using
the prompt “a cat in the style of Van Gogh.” According to its creators, “[t]he
model was trained on unfiltered data from the Internet, limited to pictures
with English descriptions.” Includes image seen in Figure 1.

  6. (6)

Which method of AI art listed below had you heard of before starting this
survey? (select all that apply) (options: AI art generally; Artificial
intelligence or deep learning algorithms, such as generative adversarial
networks (GANs); Large Language Models; Text-to-image models: None of the
above)

  7. (7)

Have you ever used AI to generate art? (options: Yes; No; I don’t know)

  8. (8)

If an AI art model was used by someone else to produce artwork recognizably in
your style (e.g., in the style of Van Gogh), Should that work and its
derivatives be considered the property of the person who used the AI model to
generate the artwork? (options: Strongly agree; Agree; Neutral; Disagree;
Strongly disagree)

  9. (9)

If an AI art model was used by someone else to produce artwork recognizably in
your style (e.g., in the style of Van Gogh), Should that work and its
derivatives be considered yours? (options: Strongly agree; Agree; Neutral;
Disagree; Strongly disagree)

  10. (10)

If an AI art model was used by someone else to produce artwork recognizably in
your style (e.g., in the style of Van Gogh), Should that work and its
derivatives be considered the property of the AI model creators? (options:
Strongly agree; Agree; Neutral; Disagree; Strongly disagree)

  11. (11)

What type of compensation for the use of your artwork to train an AI art model
would you consider fair? (select all that apply) (options: I would donate the
use of my artwork to train AI art models (no compensation); I don’t need to
profit; I don’t need to profit, but I don’t want for-profit-companies to
profit from my art; I don’t need to profit, but I don’t want anyone else to
profit from my art; A portion of any profit made; A portion of any profit made
From the model creators; A portion of any profit from people who profit from
derivative works made using the model; A flat fee; A flat fee from the model
creators, for the use of my artwork as training data; flat fee for each
derivative work sold (by anyone using the model); companies and individuals
should be subject to a new tax by the government if they profit from AI art
trained on other people’s work; I would not feel comfortable with any of the
options listed above; Other, please specify)

  12. (12)

You selected the response: Companies and individuals should be subject to a
new tax in order to use AI art that was trained with your artwork. In your
opinion, what should these taxes support? (option: open text field)

  13. (13)

Do you think model creators should be required to disclose in detail what art
and images they use to train their AI models? (options: Strongly agree; Agree;
Neutral; Disagree; Strongly disagree)

  14. (14)

Do you agree with the following statement? ”I see AI art models as a positive
development in the field of art.” (options: Strongly agree; Agree; Neutral;
Disagree; Strongly disagree)

  15. (15)

Do you agree with the following statement? ”I see AI models as a threat to art
workers?” (options: Strongly agree; Agree; Neutral; Disagree; Strongly
disagree)

  16. (16)

What is your age (options: 18-29; 30-49; 50-64, 65 or older)

  17. (17)

Do you identify as a person of color? (options: Yes, No)

  18. (18)

What is your gender identity? (options: Non-binary; Female; Male)

  19. (19)

In which country do you currently reside? (options: drop-down menu of all
countries)

##  Appendix C Full table of descriptive statistics

A complete table of descriptive statistics can be found in Table 6.

Type | Count %
---|---
Male | 273 (59.48%)
Female | 163 (35.51%)
Non-binary | 23 (5.01%)
White | 361 (78.65%)
Person of Color | 98 (21.35%)
Age 30-49 | 233 (50.76%)
Age 18-29 | 104 (22.66%)
Age 50-64 | 79 (17.21%)
Age 65 or older | 43 (9.37%)
Used AI Models - No | 237 (49.17%)
Used AI Models - Yes | 231 (47.93%)
Used AI Models - I don’t know | 10 (2.07%)
Used AI Models - None of the above | 4 (0.83%)
Familiarity - AI art generally | 379 (78.63%)
Familiarity - AI/Deeplearning/GANS | 42 (8.71%)
Familiarity - Text-to-image | 12 (2.49%)
Familiarity - LLMS | 5 (1.04%)
Familiarity - None | 44 (9.13%)
Professional Artist - No | 256 (52.57%)
Professional Artist - Yes | 214 (43.94%)
Professional Artist - Prefer not to respond | 17 (3.49%)
Artist - Yes | 310 (62.63%)
Artist - Somewhat | 176 (35.56%)
Artist - No | 7 (1.41%)
Artist - I’m not sure | 2 (0.40%)
Purchased Art - Yes | 422 (86.65%)
Purchased Art - No | 59 (12.11%)
Purchased Art - I’m not sure | 6 (1.23%)
Art Practice - Photography | 116 (23.58%)
Art Practice - Paint | 167 (33.94%)
Art Practice - Writing | 52 (10.57%)
Art Practice - Designer | 43 (8.74%)
Art Practice - Craft | 20 (4.07%)
Art Practice - Music | 18 (3.66%)
Art Practice - Digital | 14(2.85%)
Art Practice - Not artist | 13(2.64%)
Art Practice - Other | 11(2.24%)
Art Practice - Illustration | 9 (1.83%)
Art Practice - Tattoo | 9 (1.83%)
Art Practice - Drawing | 8 (1.63%)
Art Practice - Sculpture | 7 (1.42%)
Art Practice - Maker | 5 (1.02%)
Table 6. Descriptive statistics for our survey participants.

##  Appendix D All tables for ordinal logistic regressions

We ran an ordinal logistic regression (OLR) to explore the relationship
between our dependent variable (the Likert scale answers to each primary
research question). Our secondary variables, the results of these original
logistic regressions, their odds-ratios, their p-values, and their confidence
intervals (CI) can be seen in Tables 7, 8, 9, 10, 11, 12.

Variable | CI 2.5% | 97.5% | Odds Ratio | p-value
---|---|---|---|---
Artist | 0.364481 | 0.913316 | 0.576963 | ¡0.001
Purchased art | 0.331723 | 1.004549 | 0.577263 | ¡0.001
Person of color | 1.23678 | 3.085823 | 1.953582 | ¡0.001
Professional artist | 0.840948 | 2.063429 | 1.317284 | ¡0.001
Used AI art models | 3.4805 | 7.703421 | 5.178007 | ¡0.001
Male | 1.105324 | 2.232347 | 1.570817 | 0.011789
Female | 0.417108 | 0.868241 | 0.601789 | ¡0.001
Non-binary | 0.420314 | 2.076117 | 0.934141 | ¡0.001
Age 50-64 | 0.5506 | 1.369427 | 0.868336 | ¡0.001
Age 65 plus | 0.346227 | 1.0817 | 0.611975 | ¡0.001
Age 18-29 | 0.56397 | 1.493795 | 0.917854 | 0.730133
Age 30-49 | 1.022423 | 2.304733 | 1.535061 | 0.038743
Familiarity AI art gen | 0.594964 | 1.937282 | 1.073598 | 0.813584
Familiarity AI/DL | 0.460722 | 2.345589 | 1.039551 | 0.925566
Familiarity LLM | 0.185243 | 8.879363 | 1.282512 | 0.801008
Familiarity text to image | 0.256932 | 3.219854 | 0.90955 | 0.883141
Photographer | 0.267789 | 3.76309 | 1.003849 | 0.995454
Crafter | 0.059216 | 1.147651 | 0.260691 | 0.075429
Designer | 0.203683 | 3.386843 | 0.830568 | 0.795733
Digital Artist | 0.126946 | 2.668446 | 0.582021 | 0.486019
Sketch artist | 0.222575 | 9.567887 | 1.459305 | 0.693624
Illustrator | 0.100764 | 3.509196 | 0.594644 | 0.566036
Musician | 0.11112 | 2.489955 | 0.526007 | 0.417996
Maker | 0.005815 | 0.967095 | 0.074991 | 0.047077
Sculptor | 0.016156 | 1.091709 | 0.132805 | 0.060336
Other artist | 0.100764 | 3.509204 | 0.594644 | 0.566036
Painter | 0.188397 | 2.563246 | 0.694917 | 0.5847
Tattoist | 0.269396 | 11.050416 | 1.725382 | 0.564828
Writer | 0.095046 | 1.50597 | 0.378334 | 0.167879
None, don’t need profit | 0.779115 | 12.922381 | 3.173015 | 0.107049
None, donate use of artwork to train models | 4.162129 | 57.308125 | 15.444217 | ¡0.001
Flat fee | 0.445466 | 5.012977 | 1.49436 | 0.515373
None, but don’t want for-profit comp to profit | 0.305542 | 3.134003 | 0.978555 | 0.970882
None, but don’t want anyone else to profit | 0.387164 | 5.704167 | 1.486085 | 0.563771
Not comfortable with any listed | 0.0935 | 1.160233 | 0.329365 | 0.083874
Portion of any profit from derivatives | 0.296462 | 3.932296 | 1.079711 | 0.907418
Portion of any profit from model creators | 0.295023 | 3.38909 | 0.999929 | 0.999909
Portion of any profit | 0.319872 | 4.208076 | 1.160191 | 0.821179
Tax | 0.327069 | 13.554205 | 2.105507 | 0.433234
Table 7. Ordinal logistic regression (OLR). Dependent variable: agreeing or strongly agreeing that AI is a positive development for the field of art. Strong evidence follows a threshold of a p-value less than 0.05. Variable | CI 2.5% | CI 97.5% | Odds Ratio | p-value
---|---|---|---|---
Artist | 0.673062 | 1.734431 | 1.080453 | ¡0.001
Purchased art | 0.56515 | 1.850283 | 1.022589 | ¡0.001
Person of color | 0.835831 | 2.157276 | 1.342802 | ¡0.001
Professional artist | 0.511389 | 1.27991 | 0.809031 | ¡0.001
Used AI art models | 0.381466 | 0.843053 | 0.567094 | ¡0.001
Male | 0.68315 | 1.452222 | 0.996035 | ¡0.001
Female | 0.678301 | 1.484131 | 1.003338 | ¡0.001
Non-binary | 0.41613 | 2.447633 | 1.009224 | ¡0.001
Age 50-64 | 0.399295 | 1.029979 | 0.641299 | ¡0.001
Age 65 plus | 0.385716 | 1.286586 | 0.704455 | ¡0.001
Age 18-29 | 1.337791 | 4.055724 | 2.329316 | ¡0.001
Age 30-49 | 0.829556 | 1.938754 | 1.26819 | ¡0.001
Familiarity AI art gen | 0.55586 | 2.015918 | 1.058569 | 0.862508
Familiarity AI/DL | 0.644924 | 4.062339 | 1.61861 | 0.305025
Familiarity LLM | 0.160508 | 6.218764 | 0.999079 | 0.999212
Familiarity text to image | 0.143725 | 1.825538 | 0.512226 | 0.302196
Photographer | 0.624162 | 8.7873 | 2.341944 | 0.207189
Crafter | 0.691218 | 18.224995 | 3.549288 | 0.129129
Designer | 0.625025 | 10.555753 | 2.568582 | 0.190796
Digital Artist | 0.349361 | 7.951837 | 1.666752 | 0.521639
Sketch artist | 0.274778 | 13.110306 | 1.898007 | 0.51577
Illustrator | 0.35758 | 15.847783 | 2.380514 | 0.369873
Musician | 0.845339 | 24.983415 | 4.59559 | 0.077486
Maker | 0.419635 | 68.154064 | 5.347879 | 0.196624
Sculptor | 0.274778 | 13.110325 | 1.898006 | 0.515771
Other artist | 0.603302 | 34.971648 | 4.593306 | 0.141009
Painter | 0.716997 | 9.797998 | 2.650498 | 0.14395
Tattoist | 0.603301 | 34.971718 | 4.593306 | 0.141009
Writer | 0.58997 | 9.564791 | 2.375487 | 0.223434
None, don’t need profit | 0.318227 | 4.551266 | 1.203467 | 0.784937
None, donate use of artwork to train models | 0.366867 | 3.7479 | 1.172597 | 0.788264
Flat fee | 0.492379 | 5.558458 | 1.654347 | 0.415569
None, but don’t want for-profit comp to profit | 0.672149 | 6.774345 | 2.133862 | 0.198466
None, but don’t want anyone else to profit | 0.427385 | 6.406989 | 1.654767 | 0.465872
Not comfortable with any listed | 0.880466 | 11.318314 | 3.156802 | 0.077639
Portion of any profit from derivatives | 0.76601 | 11.308242 | 2.943165 | 0.11599
Portion of any profit from model creators | 0.690259 | 8.054443 | 2.357891 | 0.171143
Portion of any profit | 0.416284 | 5.403195 | 1.499755 | 0.535396
Tax | 0.131044 | 3.68014 | 0.69445 | 0.668239
Table 8. Ordinal logistic regression (OLR). Dependent variable: agreeing or strongly agreeing that AI models are a threat to art workers. Strong evidence follows a threshold of a p-value less than 0.05. Variable | CI 2.5% | CI 97.5% | Odds Ratio | p-value
---|---|---|---|---
Artist | 0.618355 | 1.949844 | 1.098041 | ¡0.001
Purchased art | 0.851732 | 3.164821 | 1.641822 | ¡0.001
Person of color | 0.809301 | 2.740342 | 1.489215 | ¡0.001
Professional artist | 0.640443 | 2.00734 | 1.133837 | ¡0.001
Used AI art models | 0.38645 | 1.045068 | 0.635505 | ¡0.001
Male | 0.587018 | 1.506886 | 0.940515 | ¡0.001
Female | 0.669859 | 1.797992 | 1.097452 | ¡0.001
Non-binary | 0.30543 | 2.406881 | 0.857399 | ¡0.001
Age 50-64 | 0.236998 | 0.712557 | 0.410944 | ¡0.001
Age 65 plus | 0.325902 | 1.57366 | 0.716142 | ¡0.001
Age 18-29 | 1.427261 | 5.790101 | 2.874715 | ¡0.001
Age 30-49 | 1.061875 | 2.97578 | 1.777612 | ¡0.001
Familiarity AI art gen | 1.363085 | 5.05368 | 2.624614 | ¡0.001
Familiarity AI/DL | 0.879414 | 6.605245 | 2.410134 | ¡0.001
Familiarity text to image | 0.131881 | 1.59315 | 0.458373 | ¡0.001
Photographer | 0.026665 | 1.656513 | 0.21017 | 0.138655
Crafter | 0.020146 | 1.971367 | 0.199289 | 0.167743
Designer | 0.021091 | 1.532809 | 0.179801 | 0.116568
Digital Artist | 0.013077 | 1.343422 | 0.132543 | 0.087241
Sketch artist | 0.018934 | 6.359606 | 0.347009 | 0.475682
Illustrator | 0.006274 | 0.725497 | 0.067464 | 0.026098
Musician | 0.022443 | 2.549968 | 0.239228 | 0.23615
Other artist | 0.01451 | 2.365917 | 0.185285 | 0.194532
Painter | 0.036576 | 2.225627 | 0.285316 | 0.231452
Tattoist | 0.022214 | 7.300981 | 0.402718 | 0.538408
Writer | 0.017016 | 1.172106 | 0.141224 | 0.069849
None, don’t need profit | 0.105173 | 1.407149 | 0.384701 | 0.14881
None, donate use of artwork to train models | 0.446605 | 4.575144 | 1.429434 | 0.547225
Flat fee | 0.670956 | 8.716645 | 2.418365 | 0.177032
None, but don’t want for-profit comp to profit | 0.639246 | 6.378818 | 2.019314 | 0.231116
None, but don’t want anyone else to profit | 0.643891 | 15.548169 | 3.164068 | 0.156186
Not comfortable with any listed | 0.559388 | 7.355529 | 2.028445 | 0.281879
Portion of any profit from derivatives | 1.100951 | 37.925791 | 6.461768 | 0.038783
Portion of any profit from model creators | 1.051359 | 16.889636 | 4.213914 | 0.042288
Portion of any profit | 0.327584 | 4.283127 | 1.184518 | 0.796243
Table 9. Ordinal logistic regression (OLR). Dependent variable: agreeing or strongly agreeing that model creators should be required to disclose in detail what art & images they use to train their AI models. Strong evidence follows a threshold of a p-value less than 0.05. Variable | CI 2.5% | CI 97.5% | Odds Ratio | p-value
---|---|---|---|---
Artist | 0.468541 | 1.160369 | 0.737347 | 0.187823
Purchased art | 0.439453 | 1.302365 | 0.756523 | 0.314052
Person of color | 1.603519 | 3.87071 | 2.491336 | ¡0.001
Professional artist | 0.539563 | 1.334183 | 0.848455 | 0.476731
Used AI art models | 0.684392 | 1.486553 | 1.008655 | 0.965265
Male | 1.394213 | 2.965517 | 2.033362 | ¡0.001
Female | 0.34673 | 0.759067 | 0.513022 | ¡0.001
Non-binary | 0.136972 | 0.921049 | 0.355187 | 0.033244
Age 50-64 | 0.547054 | 1.443719 | 0.888703 | 0.633636
Age 65 plus | 0.621558 | 2.010455 | 1.117861 | 0.709854
Age 18-29 | 0.488074 | 1.356759 | 0.813756 | 0.429419
Age 30-49 | 0.750917 | 1.753871 | 1.147611 | 0.524626
Familiarity AI art generally | 0.152611 | 0.486134 | 0.272377 | ¡0.001
Familiarity AI/DL | 0.375901 | 1.823414 | 0.827903 | 0.639206
Familiarity LLM | 0.120181 | 3.748762 | 0.671215 | 0.649641
Familiarity text to image | 0.140257 | 1.564695 | 0.468465 | 0.217808
Photographer | 0.487757 | 6.18452 | 1.73682 | 0.394225
Crafter | 0.174597 | 3.568943 | 0.789385 | 0.758671
Designer | 0.207425 | 3.227255 | 0.818176 | 0.774409
Digital Artist | 0.037919 | 1.787102 | 0.260319 | 0.170916
Sketch artist | 0.289616 | 10.860348 | 1.773507 | 0.535461
Illustrator | 0.239963 | 8.326074 | 1.41349 | 0.702103
Musician | 0.256817 | 5.623486 | 1.201752 | 0.815437
Maker | 0.035642 | 5.508235 | 0.443085 | 0.526711
Sculptor | 0.121091 | 8.227352 | 0.998126 | 0.998609
Other artist | 0.239959 | 8.326229 | 1.41349 | 0.702106
Painter | 0.4663 | 5.715676 | 1.632549 | 0.443291
Tattoist | 1.065367 | 41.810186 | 6.674068 | 0.042601
Writer | 0.088428 | 1.500551 | 0.364267 | 0.16208
None, don’t need profit | 0.76008 | 16.216441 | 3.510811 | 0.107708
None, donate use of artwork to train models | 1.512847 | 24.393075 | 6.074783 | 0.01097
Flat fee | 0.871625 | 14.630981 | 3.571096 | 0.076892
None, but don’t want for-profit comp to profit | 0.30342 | 4.788926 | 1.205429 | 0.790657
None, but don’t want anyone else to profit | 0.177118 | 4.423683 | 0.885163 | 0.881873
Not comfortable with any listed | 0.215123 | 4.09741 | 0.938854 | 0.933113
Portion of any profit from derivatives | 0.285511 | 5.930689 | 1.30126 | 0.733653
Portion of any profit from model creators | 0.292329 | 5.254805 | 1.239407 | 0.770881
Portion of any profit | 0.621704 | 11.541633 | 2.678708 | 0.186106
Tax | 0.355144 | 20.211859 | 2.679203 | 0.339136
Table 10. Ordinal logistic regression (OLR). Dependent variable: agreeing or strongly agreeing that if an AI art model was used by someone else to produce artwork recognizably in your style (e.g., in the style of Van Gogh) that work and its derivatives should be considered the property of the AI model creators. Strong evidence follows a threshold of a p-value less than 0.05. Variable | CI 2.5% | CI 97.5% | Odds Ratio | p-value
---|---|---|---|---
Artist | 0.601364 | 1.425504 | 0.925876 | 0.726499
Purchased art | 0.682946 | 1.985811 | 1.164561 | 0.57583
Person of color | 1.382372 | 3.336151 | 2.147511 | ¡0.001
Professional artist | 0.857238 | 2.014212 | 1.314024 | 0.210158
Used AI art models | 0.453536 | 0.949834 | 0.656341 | 0.025556
Male | 0.929958 | 1.863216 | 1.316326 | 0.121057
Female | 0.540109 | 1.114689 | 0.775921 | ¡0.001
Non-binary | 0.304406 | 1.426784 | 0.659031 | ¡0.001
Age 50-64 | 0.433393 | 1.072792 | 0.681865 | ¡0.001
Age 65 plus | 0.715666 | 2.230618 | 1.263478 | ¡0.001
Age 18-29 | 0.749478 | 1.976648 | 1.21715 | 0.427012
Age 30-49 | 0.76602 | 1.711638 | 1.145054 | 0.508996
Familiarity AI art gen | 0.341862 | 1.085694 | 0.609227 | 0.092752
Familiarity AI/DL | 0.454393 | 2.193558 | 0.998367 | 0.996753
Familiarity LLM | 0.239351 | 9.063794 | 1.472899 | 0.676175
Familiarity text to image | 0.199754 | 2.904406 | 0.761687 | 0.690169
Photographer | 0.767339 | 8.542394 | 2.560257 | 0.126216
Crafter | 0.330235 | 5.508344 | 1.348721 | 0.6769
Designer | 0.350222 | 4.596068 | 1.268718 | 0.71705
Digital Artist | 0.350639 | 7.202454 | 1.58917 | 0.547998
Sketch artist | 0.442624 | 12.710739 | 2.371937 | 0.313265
Illustrator | 0.705023 | 21.346164 | 3.879373 | 0.119181
Musician | 0.846183 | 16.829236 | 3.773675 | 0.081682
Maker | 0.093845 | 6.014805 | 0.751304 | 0.787605
Sculptor | 0.563212 | 23.470174 | 3.635751 | 0.174907
Other artist | 0.423624 | 14.806303 | 2.504456 | 0.311247
Painter | 0.742785 | 7.928239 | 2.426721 | 0.142187
Tattoist | 0.705023 | 21.346176 | 3.879373 | 0.119182
Writer | 0.37674 | 4.75844 | 1.338916 | 0.65191
None, don’t need profit | 0.25232 | 3.961372 | 0.999766 | 0.999734
None, donate use of artwork to train models | 0.513752 | 5.733981 | 1.716346 | 0.380072
Flat fee | 0.358686 | 4.194022 | 1.226515 | 0.744814
None, but don’t want for-profit comp to profit | 0.334713 | 3.531268 | 1.08718 | 0.889399
None, but don’t want anyone else to profit | 0.418543 | 6.478014 | 1.646611 | 0.475448
Not comfortable with any listed | 0.349148 | 4.307525 | 1.226361 | 0.750228
Portion of any profit from derivatives | 0.409981 | 5.41326 | 1.489744 | 0.544844
Portion of any profit from model creators | 0.573956 | 6.71113 | 1.962624 | 0.282421
Portion of any profit | 0.435868 | 5.718306 | 1.578742 | 0.486822
Tax | 0.532577 | 29.762374 | 3.981302 | 0.178264
Table 11. Ordinal logistic regression (OLR). Dependent variable: agreeing or strongly agreeing that if an AI art model was used by someone else to produce artwork recognizably in your style (e.g., in the style of Van Gogh and you are Van Gogh) that work and its derivatives should be considered the property of the artist. Strong evidence follows a threshold of a p-value less than 0.05. Variable | CI 2.5% | CI 97.5% | Odds Ratio | p-value
---|---|---|---|---
Artist | 0.414702 | 1.019737 | 0.650297 | ¡0.001
Purchased art | 0.327522 | 0.974205 | 0.564866 | ¡0.001
Person of color | 1.120118 | 2.725496 | 1.747248 | ¡0.001
Professional artist | 0.702605 | 1.699884 | 1.092862 | ¡0.001
Used AI art models | 2.034051 | 4.377637 | 2.984013 | ¡0.001
Male | 1.41509 | 2.883435 | 2.01998 | ¡0.001
Female | 0.314178 | 0.66174 | 0.455965 | ¡0.001
Non-binary | 0.389118 | 1.943853 | 0.869706 | ¡0.001
Age 50-64 | 0.698376 | 1.745508 | 1.104093 | 0.671752
Age 65 plus | 0.352683 | 1.146873 | 0.63599 | 0.132472
Age 18-29 | 0.570787 | 1.525004 | 0.93298 | 0.782005
Age 30-49 | 0.791308 | 1.788877 | 1.18977 | 0.40368
Familiarity AI art generally | 0.301199 | 1.004236 | 0.549977 | 0.05163
Familiarity AI/DL | 0.388081 | 1.979027 | 0.876369 | 0.750841
Familiarity LLM | 0.086738 | 2.268525 | 0.443584 | 0.328954
Familiarity text to image | 0.221942 | 2.714057 | 0.776121 | 0.691518
Photographer | 0.460161 | 4.790509 | 1.484723 | 0.50843
Crafter | 0.116186 | 1.930897 | 0.473649 | 0.297291
Designer | 0.204141 | 2.595388 | 0.727891 | 0.624392
Digital Artist | 0.174549 | 3.434168 | 0.77423 | 0.736364
Sketch artist | 0.132029 | 4.012481 | 0.727848 | 0.715319
Illustrator | 0.480708 | 17.524118 | 2.902409 | 0.245438
Musician | 0.237822 | 4.606483 | 1.046672 | 0.95189
Maker | 0.065305 | 3.981952 | 0.509941 | 0.520716
Sculptor | 0.15958 | 5.25934 | 0.916126 | 0.921737
Other artist | 0.224562 | 6.596202 | 1.21707 | 0.819786
Painter | 0.432178 | 4.332419 | 1.368348 | 0.593818
Tattoist | 0.554963 | 15.175121 | 2.902004 | 0.206845
Writer | 0.230251 | 2.832793 | 0.807621 | 0.738605
None, don’t need profit | 0.498665 | 8.015598 | 1.999275 | 0.328153
None, donate use of artwork to train models | 0.965801 | 11.105517 | 3.275014 | 0.056896
Flat fee | 0.259222 | 3.002946 | 0.882286 | 0.841163
None, but don’t want for-profit comp to profit | 0.159296 | 1.686612 | 0.518334 | 0.274998
None, but don’t want anyone else to profit | 0.126867 | 1.973433 | 0.500362 | 0.322656
Not comfortable with any listed | 0.05761 | 0.765249 | 0.209967 | 0.018008
Portion of any profit from derivatives | 0.164151 | 2.258827 | 0.608925 | 0.458287
Portion of any profit from model creators | 0.108502 | 1.319073 | 0.378315 | 0.127163
Portion of any profit | 0.315746 | 4.308758 | 1.166393 | 0.817425
Tax | 0.132833 | 5.021371 | 0.816703 | 0.827032
Table 12. Ordinal logistic regression (OLR). Dependent variable: agreeing or
strongly agreeing that if an AI art model was used by someone else to produce
artwork recognizably in your style (e.g., in the style of Van Gogh) that work
and its derivatives should be considered the property of the person who used
the AI model to generate the artwork. Strong evidence follows a threshold of a
p-value less than 0.05.

##  Appendix E Contingency Tables:

All contingency tables for dependent variables with secondary variables can be
seen in Tables 13, 14, 15, 16, 18, 19, 20, 21, 17, 22, 23, 24, and 25.

Secondary Variable (Binary) | Dependent Variable (Likert) | Contingency Table [SD D N A SA]
---|---|---
Not Person of color | AI Positive for Art | [[50 53 90 76 66]
Person of color | AI Positive for Art | [ 6 11 22 24 29]]
Not Person of color | AI Threat to Art | [[ 22 53 54 113 93]
Person of color | AI Threat to Art | [ 6 10 14 32 30]]
Not Person of color | Required Disclosure | [[ 9 21 38 97 170]
Person of color | Required Disclosure | [ 3 2 10 37 40]]
Not Person of color | Owner AI Company | [[124 79 59 41 32]
Person of color | Owner AI Company | [ 18 19 14 23 18]]
Not Person of color | Owner AI User | [[75 67 73 61 59]
Person of color | Owner AI User | [ 8 23 13 27 21]]
Not Person of color | Owner Artist | [[36 94 81 68 56]
Person of color | Owner Artist | [ 5 17 18 24 28]]
Table 13. Contingency table for the secondary variable of interest (person of color) and dependent variables on a 5-point Likert scale strongly disagree [(SD, leftmost number in bracket), disagree (D), neutral (N), agree (A), and strongly agree (SA, rightmost number in bracket)]. Outer brackets indicate the start and end of each contingency table, and the inner brackets indicate the start and end of a row in the table. Secondary Variable (Binary) | Dependent Variable (Likert) | Contingency Table [SD D N A SA]
---|---|---
Not Professional Artist | AI Positive for Art | [[33 34 63 60 39]
Professional Artist | AI Positive for Art | [23 30 49 40 56]]
Not Professional Artist | AI Threat to Art | [[12 31 35 89 62]
Professional Artist | AI Threat to Art | [16 32 33 56 61]]
Not Professional Artist | Required Disclosure | [[ 5 11 31 89 93]
Professional Artist | Required Disclosure | [7 12 17 45 117]]
Not Professional Artist | Owner AI Company | [[59 56 47 45 22]
Professional Artist | Owner AI Company | [83 42 26 19 28]]
Not Professional Artist | Owner AI User | [[37 52 53 52 35]
Professional Artist | Owner AI User | [46 38 33 36 45]]
Not Professional Artist | Owner Artist | [[15 67 59 50 38]
Professional Artist | Owner Artist | [26 44 40 42 46]]
Table 14. Contingency table for the secondary variable of interest (professional artist) and dependent variables on a 5-point Likert scale strongly disagree [(SD, leftmost number in bracket), disagree (D), neutral (N), agree (A), and strongly agree (SA, rightmost number in bracket)]. Outer brackets indicate the start and end of each contingency table, and the inner brackets indicate the start and end of a row in the table. Secondary Variable (Binary) | Dependent Variable (Likert) | Contingency Table [SD D N A SA]
---|---|---
Not Purchased Art | AI Positive for Art | [[ 7 3 17 12 10]
Purchased Art | AI Positive for Art | [49 61 95 88 85]]
Not Purchased Art | AI Threat to Art | [[ 1 6 9 21 12]
Purchased Art | AI Threat to Art | [27 57 59 124 111]]
Not Purchased Art | Required Disclosure | [[ 0 1 13 19 16]
Purchased Art | Required Disclosure | [12 22 35 115 194]]
Not Purchased Art | Owner AI Company | [[ 10 12 14 7 6]
Purchased Art | Owner AI Company | [132 86 59 57 44]]
Not Purchased Art | Owner AI User | [[ 4 11 12 13 9]
Purchased Art | Owner AI User | [79 79 74 75 71]]
Not Purchased Art | Owner Artist | [[ 4 13 13 12 7]
Purchased Art | Owner Artist | [37 98 86 80 77]]
Table 15. Contingency table for the secondary variable of interest (purchased art) and dependent variables on a 5-point Likert scale strongly disagree [(SD, leftmost number in bracket), disagree (D), neutral (N), agree (A), and strongly agree (SA, rightmost number in bracket)]. Outer brackets indicate the start and end of each contingency table, and the inner brackets indicate the start and end of a row in the table. Secondary Variable (Binary) | Dependent Variable (Likert) | Contingency Table [SD D N A SA]
---|---|---
Not Used AI art | AI Positive for Art | [[44 45 65 38 19]
Used AI art | AI Positive for Art | [12 19 47 62 76]]
Not Used AI art AI | Threat to Art | [[ 6 24 35 76 70]
Used AI art AI | Threat to Art | [22 39 33 69 53]]
Not Used AI art | Required Disclosure | [[ 4 8 24 64 111]
Used AI art | Required Disclosure | [ 8 15 24 70 99]]
Not Used AI art | Owner AI Company | [[58 54 47 36 16]
Used AI art | Owner AI Company | [84 44 26 28 34]]
Not Used AI art | Owner AI User | [[49 58 45 29 30]
Used AI art | Owner AI User | [34 32 41 59 50]]
Not Used AI art | Owner Artist | [[17 46 56 48 44]
Used AI art | Owner Artist | [24 65 43 44 40]]
Table 16. Contingency table for the secondary variable of interest (used AI art) and dependent variables on a 5-point Likert scale strongly disagree [(SD, leftmost number in bracket), disagree (D), neutral (N), agree (A), and strongly agree (SA, rightmost number in bracket)]. Outer brackets indicate the start and end of each contingency table, and the inner brackets indicate the start and end of a row in the table. Secondary Variable (binary) | Dependent Variable (Likert) | Contingency Table [SD D N A SA]
---|---|---
Not Artist | Owner AI User | [[ 1 0 4 1 0]
Artist | Owner AI User | [88 102 94 95 84]]
Not Artist | Owner Artist | [[ 0 5 0 0 1]
Artist | Owner Artist | [43 116 115 101 87]]
Not Artist | Owner AI Company | [[ 3 0 2 1 0]
Artist | Owner AI Company | [145 113 84 68 53]]
Not Artist | Required Disclosure | [[ 0 0 0 3 2]
Artist | Required Disclosure | [13 24 55 143 218]]
Not Artist | AI Positive for Art | [[ 1 0 1 3 0]
Artist | AI Positive for Art | [59 69 123 101 100]]
Not Artist | AI Threat to Art | [[ 0 1 2 1 1]
Artist | AI Threat to Art | [28 64 78 154 128]]
Table 17. Contingency table for the secondary variable of interest (Artist) and dependent variables on a 5-point Likert scale strongly disagree [(SD, leftmost number in bracket), disagree (D), neutral (N), agree (A), and strongly agree (SA, rightmost number in bracket)]. Outer brackets indicate the start and end of each contingency table, and the inner brackets indicate the start and end of a row in the table. Secondary Variable (Categorical) | Dependent Variable (Likert) | Contingency Table [SD D N A SA]
---|---|---
Age 18-29 | Owner AI User | [[42 46 46 57 40]
Age 30-49 | Owner AI User | [24 24 17 22 17]
Age 50-64 | Owner AI User | [16 12 19 11 21]
Age 65 plus | Owner AI User | [ 5 16 12 4 6]]
Age 18-29 | Owner Artist | [[21 62 49 61 38]
Age 30-49 | Owner Artist | [10 27 20 18 29]
Age 50-64 | Owner Artist | [10 21 24 10 14]
Age 65 plus | Owner Artist | [ 2 6 17 11 6]]
Age 18-29 | Owner AI Company | [[77 47 36 38 33]
Age 30-49 | Owner AI Company | [33 30 19 12 10]
Age50-64 | Owner AI Company | [29 16 14 12 8]
Age 65 plus | Owner AI Company | [ 6 15 13 7 2]]
Age 18-29 | Required Disclosure | [[ 6 13 25 75 112]
Age 30-49 | Required Disclosure | [ 1 2 10 38 53]
Age 50-64 | Required Disclosure | [ 6 5 14 19 35]
Age 65 plus | Required Disclosure | [ 0 4 5 14 20]]
Age 18-29 | AI Positive for Art | [[22 33 60 59 56]
Age 30-49 | AI Positive for Art | [20 19 22 20 23]
Age 50-64 | AI Positive for Art | [12 9 25 15 18]
Age 65 plus | AI Positive for Art | [ 6 8 17 9 3]]
Age 18-29 | AI Threat to Art | [[16 37 35 78 65]
Age 30-49 | AI Threat to Art | [ 7 9 10 44 34]
Age 50-64 | AI Threat to Art | [ 4 13 21 22 18]
Age 65 plus | AI Threat to Art | [ 1 6 13 11 12]]
Table 18. Contingency table for the secondary variable of interest (Age) and dependent variables on a 5-point Likert scale strongly disagree [(SD, leftmost number in bracket), disagree (D), neutral (N), agree (A), and strongly agree (SA, rightmost number in bracket)]. Outer brackets indicate the start and end of each contingency table, and the inner brackets indicate the start and end of a row in the table. Secondary Variable (Categorical) | Dependent Variable (Likert) | Contingency Table [SD D N A SA]
---|---|---
AI Model Familiarity: general | Owner AI User | [[82 81 67 73 67]
AI Model Familiarity: deep learning | Owner AI User | [ 2 8 14 9 8]
AI Model Familiarity: large language models | Owner AI User | [ 5 8 11 11 7]
AI Model Familiarity: text-to-image | Owner AI User | [ 0 3 4 3 1]
AI Model Familiarity: none | Owner AI User | [ 0 2 2 0 1]]
AI Model Familiarity: general | Owner Artist | [[ 40 106 76 80 68]
AI Model Familiarity: deep learning | Owner Artist | [ 1 3 20 8 8]
AI Model Familiarity: large language models | Owner Artist | [ 2 7 16 8 9]
AI Model Familiarity: text-to-image | Owner Artist | [ 0 4 2 2 3]
AI Model Familiarity: none | Owner Artist | [ 0 1 1 3 0]]
AI Model Familiarity: general | Owner AI Company | [[137 97 49 46 41]
AI Model Familiarity: deep learning | Owner AI Company | [ 3 3 20 11 4]
AI Model Familiarity: large language models | Owner AI Company | [ 7 8 11 10 6]
AI Model Familiarity: text-to-image | Owner AI Company | [ 1 3 5 1 1]
AI Model Familiarity: none | Owner AI Company | [ 0 2 1 1 1]]
AI Model Familiarity: general | Required Disclosure | [[ 10 18 34 109 193]
AI Model Familiarity: deep learning | Required Disclosure | [ 1 3 12 14 10]
AI Model Familiarity: large language models | Required Disclosure | [ 1 2 5 18 13]
AI Model Familiarity: text-to-image | Required Disclosure | [ 1 1 4 2 2]
AI Model Familiarity: none | Required Disclosure | [ 0 0 0 3 2]]
AI Model Familiarity: general | AI Positive for Art | [[52 51 94 82 84]
AI Model Familiarity: deep learning | AI Positive for Art | [ 4 5 17 9 5]
AI Model Familiarity: large language models | AI Positive for Art | [ 4 8 10 10 7]
AI Model Familiarity: text-to-image | AI Positive for Art | [ 0 3 3 1 3]
AI Model Familiarity: none | AI Positive for Art | [ 0 2 0 2 1]]
AI Model Familiarity: general | AI Threat to Art | [[ 26 51 59 117 110]
AI Model Familiarity: deep learning | AI Threat to Art | [ 0 6 11 17 6]
AI Model Familiarity: large language models | AI Threat to Art | [ 1 5 6 16 11]
AI Model Familiarity: text-to-image | AI Threat to Art | [ 1 2 3 3 1]
AI Model Familiarity: none | AI Threat to Art | [ 0 1 1 2 1]]
Table 19. Contingency table for the secondary variable of interest (AI model familiarity) and dependent variables on a 5-point Likert scale strongly disagree [(SD, leftmost number in bracket), disagree (D), neutral (N), agree (A), and strongly agree (SA, rightmost number in bracket)]. Outer brackets indicate the start and end of each contingency table, and the inner brackets indicate the start and end of a row in the table. Secondary Variable (Categorical) | Dependent Variable (Likert) | Contingency Table [SD D N A SA]
---|---|---
Art Practice: Paint | Owner AI User | [[28 30 36 41 28]
Art Practice: Photography | Owner AI User | [11 29 22 27 23]
Art Practice: Writing | Owner AI User | [14 12 6 6 11]
Art Practice: Designer | Owner AI User | [11 12 4 7 7]
Art Practice: Craft | Owner AI User | [ 4 6 7 1 0]
Art Practice: Music | Owner AI User | [ 5 4 1 3 4]
Art Practice: Digital | Owner AI User | [ 4 3 3 2 2]
Art Practice: Not Artist | Owner AI User | [ 2 2 5 1 1]
Art Practice: Other | Owner AI User | [ 4 0 2 4 0]
Art Practice: Tattoo | Owner AI User | [ 0 0 4 2 2]
Art Practice: Illustration | Owner AI User | [ 3 0 0 2 4]
Art Practice: Drawing | Owner AI User | [ 0 3 3 0 1]
Art Practice: Sculpture | Owner AI User | [ 1 1 3 0 1]
Art Practice Maker | Owner AI User | [ 2 0 2 0 0]]
Art Practice: Paint | Owner Artist | [[13 38 41 37 34]
Art Practice: Photography | Owner Artist | [ 6 30 22 30 23]
Art Practice: Writing | Owner Artist | [ 7 15 12 8 7]
Art Practice: Designer | Owner Artist | [ 9 12 8 7 5]
Art Practice: Craft | Owner Artist | [ 1 6 7 2 2]
Art Practice: Music | Owner Artist | [ 1 3 4 7 2]
Art Practice: Digital | Owner Artist | [ 1 5 3 4 1]
Art Practice: Not Artist | Owner Artist | [ 0 6 4 0 1]
Art Practice: Other | Owner Artist | [ 2 2 1 3 2]
Art Practice: Tattoo | Owner Artist | [ 0 1 3 0 4]
Art Practice: Illustration | Owner Artist | [ 1 1 2 1 4]
Art Practice: Drawing | Owner Artist | [ 0 1 4 1 1]
Art Practice: Sculpture | Owner Artist | [ 0 1 2 1 2]
Art Practice Maker | Owner Artist | [ 2 0 2 0 0]]
Art Practice: Paint | Owner AI Company | [[42 39 29 28 25]
Art Practice: Photography | Owner AI Company | [30 25 20 22 15]
Art Practice: Writing | Owner AI Company | [25 14 7 2 1]
Art Practice: Designer | Owner AI Company | [17 10 6 5 3]
Art Practice: Craft | Owner AI Company | [ 6 5 6 1 0]
Art Practice: Music | Owner AI Company | [ 3 7 3 2 2]
Art Practice: Digital | Owner AI Company | [ 6 6 1 1 0]
Art Practice: Not Artist | Owner AI Company | [ 4 2 4 1 0]
Art Practice: Other | Owner AI Company | [ 4 0 4 2 0]
Art Practice: Tattoo | Owner AI Company | [ 1 0 2 1 4]
Art Practice: Illustration | Owner AI Company | [ 3 2 1 1 2]
Art Practice: Drawing | Owner AI Company | [ 1 2 2 2 0]
Art Practice: Sculpture | Owner AI Company | [ 4 0 0 1 1]
Art Practice Maker | Owner AI Company | [ 2 1 1 0 0]]
Table 20. Contingency table for the secondary variable of interest (Art Practice) and dependent variables on a 5-point Likert scale strongly disagree [(SD, leftmost number in bracket), disagree (D), neutral (N), agree (A), and strongly agree (SA, rightmost number in bracket)]. Outer brackets indicate the start and end of each contingency table, and the inner brackets indicate the start and end of a row in the table. Secondary Variable (Categorical) | Dependent Variable (Likert) | Contingency Table [SD D N A SA]
---|---|---
Art Practice: Paint | Required Disclosure | [[ 2 5 20 64 71]
Art Practice: Photography | Required Disclosure | [ 2 7 13 28 59]
Art Practice: Writing | Required Disclosure | [ 5 3 4 14 20]
Art Practice: Designer | Required Disclosure | [ 3 3 3 9 22]
Art Practice: Craft | Required Disclosure | [ 0 1 3 6 8]
Art Practice: Music | Required Disclosure | [ 0 1 2 6 7]
Art Practice: Digital | Required Disclosure | [ 0 2 2 2 8]
Art Practice: Not Artist | Required Disclosure | [ 0 1 1 5 3]
Art Practice: Other | Required Disclosure | [ 0 0 3 2 4]
Art Practice: Tattoo | Required Disclosure | [ 0 0 1 3 4]
Art Practice: Illustration | Required Disclosure | [ 1 1 2 0 5]
Art Practice: Drawing | Required Disclosure | [ 0 0 1 4 2]
Art Practice: Sculpture | Required Disclosure | [ 0 0 0 1 5]
Art Practice Maker | Required Disclosure | [ 0 0 0 2 2]]
Art Practice: Paint | AI Positive for Art | [[23 26 33 35 44]
Art Practice: Photography | AI Positive for Art | [ 8 14 29 35 23]
Art Practice: Writing | AI Positive for Art | [12 7 12 7 8]
Art Practice: Designer | AI Positive for Art | [ 5 7 7 11 10]
Art Practice: Craft | AI Positive for Art | [ 2 4 12 0 0]
Art Practice: Music | AI Positive for Art | [ 2 3 5 4 2]
Art Practice: Digital | AI Positive for Art | [ 0 1 10 0 3]
Art Practice: Not Artist | AI Positive for Art | [ 2 1 2 5 0]
Art Practice: Other | AI Positive for Art | [ 1 1 4 3 0]
Art Practice: Tattoo | AI Positive for Art | [ 0 0 3 0 5]
Art Practice: Illustration | AI Positive for Art | [ 2 1 2 2 2]
Art Practice: Drawing | AI Positive for Art | [ 0 0 3 2 2]
Art Practice: Sculpture | AI Positive for Art | [ 1 3 1 0 1]
Art Practice Maker | AI Positive for Art | [ 2 1 1 0 0]]
Art Practice: Paint | AI Threat to Art | [[ 9 23 27 54 48]
Art Practice: Photography | AI Threat to Art | [ 5 17 22 35 30]
Art Practice: Writing | AI Threat to Art | [ 5 5 8 14 14]
Art Practice: Designer | AI Threat to Art | [ 4 4 6 15 11]
Art Practice: Craft | AI Threat to Art | [ 1 3 1 8 5]
Art Practice: Music | AI Threat to Art | [ 0 2 2 8 4]
Art Practice: Digital | AI Threat to Art | [ 1 1 6 5 1]
Art Practice: Not Artist | AI Threat to Art | [ 0 5 1 2 2]
Art Practice: Other | AI Threat to Art | [ 0 1 2 4 2]
Art Practice: Tattoo | AI Threat to Art | [ 0 1 1 3 3]
Art Practice: Illustration | AI Threat to Art | [ 2 1 0 2 4]
Art Practice: Drawing | AI Threat to Art | [ 1 1 1 3 1]
Art Practice: Sculpture | AI Threat to Art | [ 0 1 2 1 2]
Art Practice Maker | AI Threat to Art | [ 0 0 1 1 2]]
Table 21. Contingency table for the secondary variable of interest (Art Practice) and dependent variables on a 5-point Likert scale strongly disagree [(SD, leftmost number in bracket), disagree (D), neutral (N), agree (A), and strongly agree (SA, rightmost number in bracket)]. Outer brackets indicate the start and end of each contingency table, and the inner brackets indicate the start and end of a row in the table. Secondary Variable (Categorical) | Dependent Variable (Likert) | Contingency Table [SD D N A SA]
---|---|---
Compensation: Tax | Owner AI User | [[ 0 2 2 2 0]
Compensation: None listed | Owner AI User | [20 9 9 2 2]
Compensation: No profit needed but no profit for companies | Owner AI User | [26 26 26 20 8]
Compensation: Flat fee | Owner AI User | [ 5 13 14 8 14]
Compensation: Donate art to train models | Owner AI User | [ 4 6 12 26 33]
Compensation: No profit needed but no profit for anyone | Owner AI User | [ 5 9 3 7 3]
Compensation: Other | Owner AI User | [ 3 0 4 1 2]
Compensation: Portion from users derivatives | Owner AI User | [ 3 13 5 8 3]
Compensation: Portion of profit from anyone | Owner AI User | [ 4 7 5 9 7]
Compensation: Portion of profit from AI company | Owner AI User | [16 14 9 7 5]
Compensation: Don’t need profit | Owner AI User | [ 1 2 6 6 7]]
Compensation: Tax | Owner Artist | [[ 0 1 1 3 1]
Compensation: None listed | Owner Artist | [ 6 10 10 5 11]
Compensation: No profit needed but no profit for companies | Owner Artist | [11 32 29 23 11]
Compensation: Flat fee | Owner Artist | [ 5 16 13 11 9]
Compensation: Donate art to train models | Owner Artist | [13 17 8 17 26]
Compensation: No profit needed but no profit for anyone | Owner Artist | [ 1 9 3 9 5]
Compensation: Other | Owner Artist | [ 1 3 4 1 1]
Compensation Portion from users derivatives | Owner Artist | [ 0 9 11 9 3]
Compensation: Portion of profit from anyone | Owner Artist | [ 1 8 10 6 7]
Compensation: Portion of profit from AI company | Owner Artist | [ 1 9 18 12 10]
Compensation | Owner Artist | [ 4 6 5 4 3]]
Compensation: Tax | Owner AI Company | [[ 0 3 1 2 0]
Compensation: None listed | Owner AI Company | [20 10 8 2 2]
Compensation: No profit needed but no profit for companies | Owner AI Company | [39 33 18 10 6]
Compensation: Flat fee | Owner AI Company | [10 12 12 13 7]
Compensation: Donate art to train models | Owner AI Company | [18 9 12 18 24]
Compensation: No profit needed but no profit for anyone | Owner AI Company | [ 9 10 2 4 2]
Compensation: Other | Owner AI Company | [ 6 1 3 0 0]
Compensation Portion from users derivatives | Owner AI Company | [11 11 3 4 3]
Compensation: Portion of profit from anyone | Owner AI Company | [ 6 8 10 5 3]
Compensation: Portion of profit from AI company | Owner AI Company | [23 12 6 7 3]
Compensation: Portion of profit from AI company | Owner AI Company | [ 4 4 7 4 3]]
Table 22. Contingency table for the secondary variable of interest (Fair compensation) and dependent variables on a 5-point Likert scale strongly disagree [(SD, leftmost number in bracket), disagree (D), neutral (N), agree (A), and strongly agree (SA, rightmost number in bracket)]. Outer brackets indicate the start and end of each contingency table, and the inner brackets indicate the start and end of a row in the table. Secondary Variable (Categorical) | Dependent Variable (Likert) | Contingency Table [SD D N A SA]
---|---|---
Compensation: Tax | Required Disclosure | [[ 0 0 0 4 2]
Compensation: None listed | Required Disclosure | [ 1 0 7 7 27]
Compensation: No profit needed but no profit for companies | Required Disclosure | [ 2 4 13 28 56]
Compensation: Flat fee | Required Disclosure | [ 2 4 2 26 19]
Compensation: Donate art to train models | Required Disclosure | [ 3 8 8 27 35]
Compensation: No profit needed but no profit for anyone | Required Disclosure | [ 0 2 1 10 14]
Compensation: Other | Required Disclosure | [ 0 2 4 2 2]
Compensation Portion from users derivatives | Required Disclosure | [ 0 1 1 13 16]
Compensation: Portion of profit from anyone | Required Disclosure | [ 2 1 6 9 14]
Compensation: Portion of profit from AI company | Required Disclosure | [ 0 0 5 17 29]
Compensation: Portion of profit from AI company | Required Disclosure | [ 3 2 8 3 6]]
Compensation: Tax | AI Positive for Art | [[ 0 1 2 1 2]
Compensation: None listed | AI Positive for Art | [17 8 12 2 3]
Compensation: No profit needed but no profit for companies | AI Positive for Art | [14 21 34 17 17]
Compensation: Flat fee | AI Positive for Art | [ 3 9 20 13 8]
Compensation: Donate art to train models | AI Positive for Art | [ 1 1 7 29 42]
Compensation: No profit needed but no profit for anyone | AI Positive for Art | [ 6 2 6 9 4]
Compensation: Other | AI Positive for Art | [ 3 0 5 0 2]
Compensation Portion from users derivatives | AI Positive for Art | [ 3 8 8 9 3]
Compensation: Portion of profit from anyone | AI Positive for Art | [ 4 7 8 7 6]
Compensation: Portion of profit from AI company | AI Positive for Art | [ 8 10 16 11 6]
Compensation: Portion of profit from AI company | AI Positive for Art | [ 1 2 6 6 7]]
Compensation: Tax | AI Threat to Art | [[ 0 1 4 1 0]
Compensation: None listed | AI Threat to Art | [ 2 2 7 11 20]
Compensation: No profit needed but no profit for companies | AI Threat to Art | [ 3 16 15 43 26]
Compensation: Flat fee | AI Threat to Art | [ 5 8 6 22 12]
Compensation: Donate art to train models | AI Threat to Art | [10 13 14 20 23]
Compensation: No profit needed but no profit for anyone | AI Threat to Art | [ 2 5 4 12 4]
Compensation: Other | AI Threat to Art | [ 0 2 5 2 1]
Compensation: Portion from users derivatives | AI Threat to Art | [ 0 5 3 13 10]
Compensation: Portion of profit from anyone | AI Threat to Art | [ 3 5 5 8 11]
Compensation: Portion of profit from AI company | AI Threat to Art | [ 0 7 9 18 17]
Compensation: Portion of profit from AI company | AI Threat to Art | [ 3 1 8 5 5]]
Table 23. Contingency table for the secondary variable of interest (Fair compensation) and dependent variables on a 5-point Likert scale strongly disagree [(SD, leftmost number in bracket), disagree (D), neutral (N), agree (A), and strongly agree (SA, rightmost number in bracket)]. Outer brackets indicate the start and end of each contingency table, and the inner brackets indicate the start and end of a row in the table. Secondary Variable (Categorical) | Dependent Variable (Likert) | Contingency Table [SD D N A SA]
---|---|---
Gender Male | Owner AI User | [[36 54 61 62 60]
Gender Female | Owner AI User | [47 39 29 25 21]
Gender Non-binary | Owner AI User | [ 4 5 4 7 3]]
Gender Male | Owner Artist | [[23 66 63 63 57]
Gender Female | Owner Artist | [18 43 40 32 28]
Gender Non-binary | Owner Artist | [ 2 7 7 5 2]]
Gender Male | Owner AI Company | [[71 59 60 43 40]
Gender Female | Owner AI Company | [65 41 20 23 12]
Gender Non-binary | Owner AI Company | [ 9 8 2 3 1]]
Gender Male | Required Disclosure | [[ 6 15 35 95 122]
Gender Female | Required Disclosure | [ 6 7 17 45 86]
Gender Non-binary | Required Disclosure | [ 1 2 2 6 12]]
Gender Male | AI Positive for Art | [[21 41 80 68 62]
Gender Female | AI Positive for Art | [35 26 38 29 33]
Gender Non-binary | AI Positive for Art | [ 4 2 6 6 5]]
Gender Male | AI Threat to Art | [[13 42 49 92 76]
Gender Female | AI Threat to Art | [12 20 28 51 50]
Gender Non-binary | AI Threat to Art | [ 3 3 2 12 3]]
Table 24. Contingency table for the secondary variable of interest (Gender) and dependent variables on a 5-point Likert scale strongly disagree [(SD, leftmost number in bracket), disagree (D), neutral (N), agree (A), and strongly agree (SA, rightmost number in bracket)]. Outer brackets indicate the start and end of each contingency table, and the inner brackets indicate the start and end of a row in the table. | Strongly Disagree | Disagree | Neutral | Agree | Strongly Agree
---|---|---|---|---|---
Strongly Disagree | 1 | 0 | 3 | 2 | 22
Disagree | 3 | 4 | 9 | 29 | 20
Neutral | 3 | 6 | 41 | 17 | 12
Agree | 11 | 29 | 59 | 37 | 18
Strongly Disagree | 42 | 29 | 12 | 18 | 27
Table 25. Vertical side of table represents AI as a threat to the art
workforce and the horizontal side of the table represents AI as a positive
development for the field of art. Contingency table for the AI positive
development for art and AI threat to art workforce on a 5-point Likert scale
strongly disagree [(SD, leftmost number in bracket), disagree (D), neutral
(N), agree (A), and strongly agree (SA, rightmost number in bracket)]. Outer
brackets indicate the start and end of each contingency table, and the inner
brackets indicate the start and end of a row in the table.

##  Appendix F Tests for Independence

####  F.0.1. Kruskal-Wallis test for independence:

To test for independence between dependent variables that were ordinal with
variables of interest that are categorical, we ran Kruskall-Wallis tests for
independence. The results of these tests can be seen in Tables 26, 27, 30, 31,
28, 29.

Variable | p-value | H Statistic | Degrees of Freedom
---|---|---|---
Artist | 0.9873871802 | 0.0002499081613 | 1
Gender | 0.01738441229 | 8.104362639 | 2
Art Practice | 0.007501359367 | 28.5814306 | 13
AI Model Familiarity | 0.9798888491 | 0.4306800685 | 4
Age | 0.03938656365 | 8.345451383 | 3
Compensation | 1.05E-16 | 98.58439653 | 10
Table 26. Results of the Kruskall-Wallis test for independence for the dependent variable (5-point Likert) AI art models are a positive development in the field of art. Strong evidence follows a threshold of a p-value less than 0.05. Variable | p-value | H Statistic | Degrees of Freedom
---|---|---|---
Artist | 0.5445539432 | 0.3671644759 | 1
Gender | 0.5726378558 | 1.115003553 | 2
Art Practice | 0.9024842342 | 6.993538631 | 13
AI Model Familiarity | 0.4531083849 | 3.665876796 | 4
Age | 0.1005912351 | 6.237926759 | 3
Compensation | 0.04021463347 | 19.003785 | 10