#  Analog Schwarzschild black hole from a nonisentropic fluid

Neven Bilić and Hrvoje Nikolić bilic<EMAIL_ADDRESS>
###### Abstract

We study the conditions under which an analog acoustic geometry of a
relativistic fluid in flat spacetime can take the same form as the
Schwarzschild black hole geometry. We find that the speed of sound must
necessarily be equal to the speed of light. Since the speed of the fluid
cannot exceed the speed of light, this implies that analog Schwarzschild
geometry necessarily breaks down behind the horizon.

##  1 Introduction

The analog acoustic metric was first introduced by Unruh [1], with the
motivation of explaining Hawking radiation produced by black holes [2]. Since
then, extensive research on analog gravity has been done (see e.g. [3, 4] for
reviews) but analog black holes do not seem to have shed much light on one of
the most difficult problems with Hawking radiation - the black hole
information paradox [5, 6, 7, 8, 9, 10, 11, 12, 13].

In general, the analog acoustic metric $G_{\mu\nu}$ takes the form [14]

$G_{\mu\nu}=\omega[g_{\mu\nu}-(1-c_{\rm s}^{2})u_{\mu}u_{\nu}],$ (1)

where $u_{\mu}$ is the 4-velocity of a relativistic fluid in the background
metric $g_{\mu\nu}$ usually taken to be the flat Minkowski metric, $c_{\rm s}$
is the speed of sound, and the conformal factor $\omega$ is related to the
equation of state of the fluid. Normally, the fluid is assumed to satisfy the
Euler equation without external pressure and particle number conservation is
assumed.

Unfortunately, with these assumptions the acoustic metric of the form (1)
which mimics the Schwarzschild black hole cannot be found. It has been noted
that a non-relativistic version of the metric (1) can be found which differs
from the Schwarzschild metric by a non-constant conformal factor [3, 4, 15].
But even in this case one must assume an external force field to satisfy the
Euler equation. De Oliveira et al [16] have recently obtained a closed form of
an analog Schwarzschild geometry in a setup with an external force.
Unfortunately, their solution is subject to a slightly modified analog
geometry that is not exactly supported by fluid dynamics. For nonisentropic
fluids, recently studied in [17], one is more flexible and has the possibility
to choose $\omega(x)$ such that the analog metric exactly reproduces the
Schwarzschild metric. Since $g_{\mu\nu}$ is the flat metric that is not
proportional to the Schwarzschild metric, one would naively expect
$G_{\mu\nu}$ to be equal to the Schwarzschild metric only if $c_{\rm s}$ in
(1) is not equal to unity.

On the contrary, we find here that $G_{\mu\nu}$ can be equal to the
Schwarzschild metric if $c_{\rm s}\rightarrow 1$, so that $(1-c_{\rm
s}^{2})u_{\mu}u_{\nu}$ remains non-zero. Since the acoustic horizon is by
definition a surface beyond which the fluid is faster than the speed of sound,
it follows that the fluid beyond the horizon should be superluminal, which is
physically forbidden. This means that analogue metric can take the form of a
Schwarzschild geometry only outside the black hole and not in the black hole
interior. The horizon thus represents a physical boundary beyond which the
Schwarzschild geometry necessarily breaks down. This is consistent with the
expected behavior of black hole firewalls [18, 19].

The remainder of the paper is organized as follows. In Section 2 we derive the
Schwarzschild geometry as an analog gravity model in a relativistic fluid. In
Section 3 we briefly discuss the field theoretic description of the fluid
relevant to the model considered in Section 2. The concluding section, Section
4, is devoted to discussion and conclusions.

##  2 Analog Schwarzschild geometry

In applications of analog geometry, particle number conservation is usually
assumed in addition to energy-momentum conservation or the Euler equation.
However, with this assumption, some interesting geometries cannot be mimicked
by analog geometry. The fluid in which the particle number is not conserved is
generally nonisentropic.

Here we study the conditions under which one can mimic the Schwarzschild
geometry. We assume the metric that is conformally equivalent to the
Schwarzschild metric, i.e.,

$\displaystyle
ds^{2}=\omega(r,t)\left[\gamma(r)dt^{2}-\gamma(r)^{-1}dr^{2}-r^{2}d\Omega^{2}\right],$
(2)

where

$\displaystyle\gamma(r)=1-\frac{2MG}{r}.$ (3)

We look for an analog fluid model that mimics the metric of the form (2) and
we impose the necessary conditions so that the conformal factor $\omega$ can
be set to one. The basic idea is to find a suitable coordinate transformation
$t\to\tilde{t}$ such that the new metric takes the form of the relativistic
acoustic metric

$G_{\mu\nu}=\frac{n}{m^{2}c_{\rm s}w}[g_{\mu\nu}-(1-c_{\rm
s}^{2})u_{\mu}u_{\nu}]\,,$ (4)

where $g_{\mu\nu}$ is the Minkowski metric in spherical coordinates, $u_{\mu}$
is the four-velocity with non-vanishing radial component, $m$ is an arbitrary
mass scale, $n$ is the particle number density, and $w$ is the specific
enthalpy defined as

$w=\frac{p+\rho}{n}.$ (5)

We assume that the fluid is irrotational and satisfies Euler’s equation.
Accordingly we assume that the enthalpy flow $wu_{\mu}$ is a gradient of a
scalar potential, i.e.,

$wu_{\mu}=\theta_{,\mu},$ (6)

and that the entropy gradient is proportional to the gradient of $\theta$ [17]

$s_{,\mu}=\frac{u^{\nu}s_{,\nu}}{w}\theta_{,\mu}.$ (7)

Next, following [20] we apply a coordinate transformation

$t=\kappa\tilde{t}+f(r),$ (8)

where $\kappa$ is a constant and the function $f(r)$ is to be determined by
the condition that the transformed metric is of the form (4). The line element
(2) in the new coordinates is of the form

$\displaystyle
ds^{2}=\omega(r,t)\left[\kappa^{2}\gamma(r)d\tilde{t}^{2}+2\kappa\gamma
f^{\prime}d\tilde{t}dr-\left(\gamma(r)^{-1}-\gamma(r)f^{\prime
2}\right)dr^{2}-r^{2}d\Omega^{2}\right],$ (9)

similar to the Painlevé–Gullstrand metric. Comparing this with (4) we obtain a
set of equations

$\kappa^{2}\gamma=1-(1-c_{\rm s}^{2})u_{\tilde{t}}^{2},$ (10) $\kappa\gamma
f^{\prime}=-(1-c_{\rm s}^{2})u_{\tilde{t}}u_{r},$ (11)
$\left(\gamma(r)^{-1}-\gamma(r)f^{\prime 2}\right)=1+(1-c_{\rm
s}^{2})u_{r}^{2},$ (12) $u_{\tilde{t}}^{2}-u_{r}^{2}=1,$ (13)

and we require that the conformal factor in (2) is equal to that of (4), that
is,

$\omega=\frac{n}{m^{2}c_{\rm s}w}.$ (14)

Equations (10)-(13) give

$c_{\rm s}=\kappa,$ (15)
$u_{\tilde{t}}=\frac{(1-\kappa^{2}\gamma)^{1/2}}{(1-\kappa^{2})^{1/2}},\quad
u_{r}=-\frac{\kappa(1-\gamma)^{1/2}}{(1-\kappa^{2})^{1/2}},$ (16)
$f^{\prime}=\frac{(1-\kappa^{2}\gamma)^{1/2}(1-\gamma)^{1/2}}{\gamma}.$ (17)

We could absorb $\kappa$ in the $\tilde{t}$-coordinate in (9) and formally set
$\kappa$ to a suitable constant in (17), e.g., $\kappa=0$ or $\kappa=1$. With
$\kappa=0$ we would obtain the line element in the form

$\displaystyle ds^{2}=\frac{n}{m^{2}c_{\rm s}w}[\gamma
d\tilde{t}^{2}+2(1-\gamma)^{1/2}d\tilde{t}drdr^{2}-r^{2}d\Omega^{2}],$ (18)

conformally equivalent to the Painlevé-Gullstrand metric. With $\kappa=1$ we
would obtain

$\displaystyle ds^{2}=\frac{n}{m^{2}c_{\rm s}w}[\gamma
d\tilde{t}^{2}+2(1-\gamma)d\tilde{t}dr-(2-\gamma)dr^{2}-r^{2}d\Omega^{2}].$
(19)

So far we basically agree with previous works [3, 4, 15] which have rendered
the metric conformally equivalent to the Schwarzschild metric in Painlevé-
Gullstrand coordinates. We differ only in the conformal factor since these
papers use a non-relativistic acoustic metric.

From now on we depart from the approach of Refs. [3, 4, 15] in which the
continuity equation is imposed and the external force is invoked to preserve
the consistency of the definition of the speed of sound with the Euler
equation. Instead we adopt the approach of Ref. [17] which is different from
the approaches in Refs. [3, 4, 15] basically in two assumptions. First, we do
not require isentropy of our fluid and therefore the continuity equation need
not be imposed. Second, we adhere to the standard definition of the the speed
of sound without invoking any external force.

By applying the potential-flow equation (6) we derive closed-form expressions
for $w$, $n$, and $\omega$. Since the metric (9) is stationary, except for the
conformal factor $\omega$, the velocity potential must be of the form

$\theta=m(\tilde{t}+g(r)),$ (20)

where $g(r)$ is a function of $r$ and $m$ is an arbitrary mass. Then Eq. (6)
gives

$w=\frac{m}{u_{\tilde{t}}}=m\frac{(1-\kappa^{2})^{1/2}}{(1-\kappa^{2}\gamma)^{1/2}}.$
(21)

Moreover, it follows from (6) that the function $g$ in (20) must satisfy

$g^{\prime}=\frac{w}{m}u_{r}=-\frac{\kappa(1-\gamma)^{1/2}}{(1-\kappa^{2}\gamma)^{1/2}}.$
(22)

From the definition of the speed of sound

$c_{\rm s}^{2}=\left.\frac{\partial
p}{\partial\rho}\right|_{s}=\frac{n}{w}\left(\left.\frac{\partial n}{\partial
w}\right|_{s}\right)^{-1},$ (23)

where the subscript s denotes that the specific entropy $s$ is kept fixed, we
find

$n(w,s)=c_{1}(s)w^{1/\kappa^{2}},$ (24)

where $c_{1}(s)$ is an arbitrary function of $s$. The specific entropy $s$ is
generally a function of $\theta$ [17] so (20) implies $s=s(\tilde{t}+g(r))$.
Then, from (14) we obtain

$\omega=\frac{c_{1}(s)}{\kappa
m^{3-1/\kappa^{2}}}\left(\frac{1-\kappa^{2}}{1-\kappa^{2}\gamma(r)}\right)^{(1/\kappa^{2}-1)/2}.$
(25)

Clearly, if the speed of sound $c_{\rm s}\equiv\kappa\neq 1$, the conformal
factor is a nontrivial function of $r$ and $\tilde{t}$, so the acoustic metric
in ordinary fluids with $c_{\rm s}<1$ can only describe the metric conformally
invariant to the Schwarzschild metric. However, if we choose

$c_{1}(s)=m^{3-1/\kappa^{2}}h(s)^{1/\kappa^{2}-1},$ (26)

where $h$ is a function of $s$ independent of $\kappa$, we get
$\omega\rightarrow 1$ in the ultra-relativistic limit $c_{\rm s}\rightarrow 1$
of a stiff fluid and the acoustic metric approaches the Schwarzschild metric
arbitrarily close.

At first sight it looks as if the metric (4) is ill defined in the limit
$c_{\rm s}\rightarrow 1$. However, we can absorb the factor $(1-c_{\rm
s}^{2})$ into the velocity vector by redefining

$u_{\mu}=(1-c_{\rm s}^{2})^{-1/2}\tilde{u}_{\mu},$ (27)

with normalization

$g^{\mu\nu}\tilde{u}_{\mu}\tilde{u}_{\nu}=1-c_{\rm s}^{2},$ (28)

so that in the limit $c_{\rm s}\rightarrow 1$ the vector $\tilde{u}_{\mu}$
becomes light-like. In that limit we have

$\tilde{u}_{\tilde{t}}=-\tilde{u}_{r}=\sqrt{2MG/r},$ (29)
$f=2MG\ln\left(\frac{r}{2MG}-1\right).$ (30)

Now, the acoustic metric

$G_{\mu\nu}=g_{\mu\nu}-\tilde{u}_{\mu}\tilde{u}_{\nu}$ (31)

is identical to the Schwarzschild metric in the form similar to Painlevé-
Gullstrand coordinates with line element

$\displaystyle ds^{2}=\gamma
d\tilde{t}^{2}+2(1-\gamma)d\tilde{t}dr-(2-\gamma)dr^{2}-r^{2}d\Omega^{2}.$
(32)

It may be easily checked that, by applying the coordinate transformation
inverse to (8) with $\kappa=1$ and $f$ defined in (30), the Schwarzschild
metric is recovered in the standard diagonal form.

##  3 Field theoretic description of the nonisentropic fluid flow

In general, the fluid can be described as a classical field theory in terms of
a scalar field $\theta(x)$ and the kinetic term
$X=g^{\mu\nu}\theta_{,\mu}\theta_{,\nu}$ through the prescription [17]:
$p(w,s)={\cal L}(X,\theta)$, $w=\sqrt{X}$, $n=2\sqrt{X}{\cal L}_{X}$, where
the subscript X denotes a derivative with respect to $X$. The specific entropy
$s$ may be identified with $\theta$ or more generally with an unknown function
of $\theta$.

From the thermodynamic relation

$n=\left.\frac{\partial p}{\partial w}\right|_{s}$ (33)

we have

$p(w,s)=\int dw\,n(w,s).$ (34)

Using (24), this gives

$p(w,s)=c_{1}(s)\frac{w^{1+1/\kappa^{2}}}{1+1/\kappa^{2}}+c_{2}(s),$ (35)

where the integration constant $c_{2}$ is a function of $s$. This gives the
on-shell Lagrangian

${\cal
L}=W(\theta)\frac{\sqrt{X^{1+1/\kappa^{2}}}}{1+1/\kappa^{2}}-V(\theta),$ (36)

where we have denoted $c_{1}(s)=W(\theta)$, $c_{2}(s)=-V(\theta)$. The
function $V(\theta)$ is not arbitrary since we must also satisfy the equation
of motion [17]

$(2{\cal L}_{X}\,g^{\mu\nu}\theta_{,\mu})_{;\nu}-\partial{\cal
L}/\partial\theta=0.$ (37)

Consider next the limit $1-\kappa^{2}\equiv\epsilon\rightarrow 0$. In this
limit we find

$w=\sqrt{X}=m\epsilon^{1/2}\left(\frac{r}{2MG}\right)^{1/2},$ (38) ${\cal
L}=\frac{m^{3-\epsilon}}{2}h(s(\theta))^{\epsilon}XX^{\epsilon/2}-V(\theta),$
(39)
$n=m^{3-\epsilon}h(s(\theta))^{\epsilon}\sqrt{X}X^{\epsilon/2}=m^{3}h(s(\theta))^{\epsilon}\epsilon^{1/2}\left(\frac{r}{2MG}\right)^{1/2}.$
(40)

The equation of motion (37) can be written as

$2\partial_{\tilde{t}}({\cal
L}_{X}\partial_{\tilde{t}}\theta)-\frac{2}{r^{2}}\partial_{r}(r^{2}{\cal
L}_{X}\partial_{r}\theta)=\frac{\partial{\cal L}}{\partial\theta}.$ (41)

This, together with (20) and (22), gives at leading order in $\epsilon$

$\frac{2m^{3}}{r}=-\frac{\partial V}{\partial\theta}.$ (42)

This equation can be solved by making use of the explicit functional relation
between $\theta$ and $r$. From (20) and (22) one finds

$\theta=-4mMG\kappa\left(\frac{r}{2MG\epsilon}+\frac{\kappa^{2}}{\epsilon^{2}}\right)^{1/2}+m\tilde{t}+\mbox{const.}$
(43)

In the limit $\epsilon\rightarrow 0$ we may neglect the $\tilde{t}$ dependence
and keeping the leading orders in $\epsilon$ we obtain

$r=\frac{\epsilon\theta^{2}}{8m^{2}MG}-\frac{2MG}{\epsilon}.$ (44)

Then, substituting this for $r$ in (42) and integrating we obtain

$V(\theta)=2m^{4}\ln\left|\frac{\epsilon\theta+4mMG}{\epsilon\theta-4mMG}\right|.$
(45)

In the limit $\epsilon\rightarrow 0$ the potential tends to zero as
$V\sim\epsilon\theta m^{3}/(2MG)$ so that the Lagrangian (39) in this limit
becomes a free massless scalar field Lagrangian. This Lagrangian describes a
stiff fluid with the equation of state $p=\rho$ and the sound speed equal to
the speed of light, as it should according to the discussion in Sec. 2.

##  4 Conclusions

By making use of a nonisentropic fluid dynamics we have succeeded to model an
analog of the Schwarzschild spacetime. Applying an analog metric in the form
similar to the metric in Painlevé–Gullstrand coordinates we have been able to
reproduce the exterior of a Schwarzschild black hole in the limit when the
speed of sound approaches the speed of light. In this case the acoustic
horizon is a physical boundary beyond which the Schwarzschild geometry breaks
down in a way similar to the expected behavior of black hole firewalls [18,
19].

It is important to stress that neither the firewall proposal nor our analog
geometry model can make specific predictions for physics in the black hole
interior. Nevertheless, our analog model offers a physically clear explanation
why the interior cannot be described by Schwarzschild geometry. This
explanation does not have any explicit dependence on quantum theory and small
distance physics, nor it depends on the existence of analog Hawking radiation.
It only depends on the assumption that curved geometry emerges from acoustic
properties of a relativistic fluid. This result is compatible with the general
idea that classical Einstein gravity could be emergent as a fluid phase of
some more fundamental degrees of freedom [21, 22].

It is worth mentioning that the analog metric in this paper and in other
papers on analog gravity describes an effective geometry in which fluid
acoustic perturbations propagate and cannot be related to the Einstein
equations as in General Relativity. In particular, the classical BH
thermodynamics (first, second and third law) are tightly related to the
Einstein equations, and hence, these thermodynamic laws cannot be reproduced
by analog metric. However, as demonstrated in previous works, e.g., Refs. [3,
4, 14], the analog horizon radiation, dubbed also the analog Hawking
radiation, can be reproduced in the usual way. The analog horizon radiation is
a genuine quantum effect in which the phonon quanta participate.

Finally, we would like to mention that we are not aware of any astrophysical
research related to the study presented here. However, in the framework of the
relativistic acoustic geometry in general, possible effects could be observed
in the BH accretion [23] and in high energy experiments [24, 25].

## Acknowledgments

The work of N.B. has been partially supported by the ICTP - SEENET-MTP project
NT-03 Cosmology - Classical and Quantum Challenges.

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# A Value-Oriented Investigation of Photoshop’s Generative Fill

Ian P. Swift  University of Illinois ChicagoIllinoisUSA<EMAIL_ADDRESS>and
Debaleena Chattopadhyay  University of Illinois ChicagoChicagoIllinoisUSA
<EMAIL_ADDRESS>
(2024)

###### Abstract.

The creative industry is both concerned and enthusiastic about how generative
AI will reshape creativity. How might these tools interact with the workflow
values of creative artists? In this paper, we adopt a value-sensitive design
framework to examine how generative AI, particularly Photoshop’s Generative
Fill (GF), helps or hinders creative professionals’ values. We obtained 566
unique posts about GF from online forums for creative professionals who use
Photoshop in their current work practices. We conducted reflexive thematic
analysis focusing on usefulness, ease of use, and user values. Users found GF
useful in doing touch-ups, expanding images, and generating composite images.
GF helped users’ values of productivity by making work efficient but created a
value tension around creativity: it helped reduce barriers to creativity but
hindered distinguishing ‘human’ from algorithmic art. Furthermore, GF hindered
lived experiences shaping creativity and hindered the honed prideful skills of
creative work.

Value sensitive design, generative AI, generative fill, values

††journalyear: 2024††copyright: rightsretained††conference: Extended Abstracts
of the 2024 CHI Conference on Human Factors in Computing Systems; April 23–28,
2023; Hamburg, Germany††booktitle: Extended Abstracts of the 2024 CHI
Conference on Human Factors in Computing Systems (CHI EA ’23), April 23–28,
2023, Hamburg, Germany††doi: 10.1145/3544549.3585697††isbn:
978-1-4503-9422-2/23/04††ccs: Human-centered computing Empirical studies in
HCI

##  1\. Introduction

In the last few years, generative models have had a tremendous impact on the
world, including but not limited to creative professionals (Jiang et al.,
2023; Bender et al., 2021; Hacker et al., 2023). For example, AI has impacted
healthcare (Zhang and Kamel Boulos, 2023), mental health support (Jo et al.,
2023), software engineering (Weisz et al., 2022), and eduation (Baidoo-Anu and
Ansah, 2023), to name just a few. Creative professionals are currently
bombarded with a variety of new generative tools. At times there is a question
of how creative users are adapting their work practices (Zamfirescu-Pereira et
al., 2023; Gmeiner et al., 2023). Research on scientifically advancing
generative models (Chung and Adar, 2023; Feng et al., 2023; Brade et al.,
2023; Lawton et al., 2023) and their practical use as tools are occurring in
parallel. In practice, AI tools in creative work such as DALL-E, Stable
Diffusion, Firefly, and Generative Fill have garnered both enthusiasm and
concern among creative professionals (De Cremer et al., 2023; Franzen, 2023).
However, as the technological landscape evolves rapidly, several
sociotechnical questions have emerged and remain unanswered. A major question
is how these models and tools will align creative professionals’ values in
their workflows.

AI-generated art is having a growing impact on the creative world,
specifically appearing in the form of text-to-image (T2I) generation. T2I is
the transformation of a text prompt input into a generated image. However such
text prompts require prompt engineering, which is often a struggle. One study
showed that individuals were challenged by systematic prompt design
(Zamfirescu-Pereira et al., 2023). Users experienced difficulties with
generating prompts and evaluating the effectiveness of their prompts.
Accordingly, several works have emerged on how to improve the prompt
engineering process. To address struggles with T2I prompt engineering, the
tool ”RePrompt” was designed for refining T2I prompts (Wang et al., 2023).
RePrompt automates the process of transforming user-generated prompts by
adding and removing parts of speech to increase the emotional expression of
image generation. A similar work is the Promptify tool (Brade et al., 2023).
This tool allows users to specify subject and style information and generate a
more thorough image prompt. Other tools address the T2I method through further
abstractions. PromptPaint offers an alternative approach borrowing from
painting concepts (Chung and Adar, 2023). This tool introduces vectors between
discrete semantics (interpolating between ”cat” and ”dog”), adding a
directional semantic (shifting ”dog” towards ”fluffy”), as well as
interventions during the generation process, all while utilizing analogies to
art creation metaphors (mixing paints, layering paints, etc). Another T2I tool
Reframer allows the user to prompt the AI for a drawing that is created with
strokes, and then add or modify strokes alongside the AI (Lawton et al.,
2023).

The experience of creative professionals with generative models is an
important factor in the advent of generative models; as is the effect of
generative models on society at large. One research group found that self-
identified ”creative professionals” were largely not worried about the
existence of AI tools (Inie et al., 2023). They found that a reason they
_were_ excited was because of changes in productivity, a finding confirmed in
our study. A focus on the relationship between writers and AI revealed the
nuances of how writers relate to assistance from other writers versus how they
relate to AI assistance (Gero et al., 2023). Similar to our work, these
authors looked into the values of creative professionals. The authors noted
that creative writers value intention, authenticity, and creativity and that
particularly the values of authenticity and creativity impacted whether a
writer would or would not consider using a computer for support. The existence
of such tools also led to a variety of ethical concerns: the
anthropomorphization of AI is problematic, in suggesting that the image
generator is as much to credit for the result as a human creator; there is
concern about the usage of generative AI to forge the style of artists; and,
among other concerns, the uncertainty of the relationship between copyright
law and training image generators (Jiang et al., 2023). Beyond creative
concerns, concerns about generative AI are prevalent across the field. For
example, one set of authors worries that 1) the generative models being as
large comes at a cost of money and carbon emissions, and 2) by training on
large sets of data across the internet, these models will amplify a hegemonic
world views, negatively impacting marginalized populations (Bender et al.,
2021). The ethical landscape of generative AI is still clearly an important
issue, which is why our work on generative AI offers another unique
perspective, incorporating moral and ethical considerations as part of our
process.

In this paper, we discuss results from an empirical study of Photoshop’s
generative fill (GF), adopting a value sensitive design (VSD) approach
(Friedman, 1996). VSD defines values as what is important to people in their
lives, with an emphasis on ethics and morality, e.g., autonomy, ownership, and
usability. We focus on currently active creative professionals, particularly
Photoshop users, and examine how they interacted with GF—and through those
interactions what values relationships are manifested. Specifically, we
examined the following research question:

> _How does the generative fill (GF) being useful for certain tasks, and
> making certain tasks easy to accomplish help or hinder users in embodying
> their values?_

Results from our study identified specific ways that GF was useful in users’
work practices and was easy to use helped or hindered values of the creators.
For example, GF users found the tool useful in doing touch-ups (e.g., removing
blemishes), expanding images (e.g., changing dimensions or adding space for
text), and generating and tailoring prototypical images (i.e., images based on
an idea in the user’s mind, e.g., stock photos). Consequently, GF helped users
with their value of productivity by making work practices efficient. However,
a value tension was created around creativity: GF helped values of reducing
the barriers to being more creative, but as a result hindered the value of
distinguishability of ‘authentic’ art created by a human without any AI
assistance. Specifically, users languished about people gradually losing
appreciation for creativity and creative work.

##  2\. Methods

We collected data about GF use from online forums, including posts on Reddit
r/photography, r/graphic_design, and r/photoshop, as well as posts on the
website DPReview. Data collection occurred on November 28th, 2023, using the
Google search API. 2666 unique posts were obtained from threads that contained
mentions of GF. We filtered out posts that were not relevant to our research
question through a multi-step process. First, we used a randomized BERTopic
model to identify posts that did not contain information relevant to
generative fill. Next, the remaining posts were manually reviewed by the first
author and eliminated if determined not relevant. When the relevance of a post
was not immediately clear, additional review was conducted to examine
contextual information, including finding the thread of “replied to” posts
starting at a given post, and reviewing all posts by a particular user. A
final corpus of 566 posts was then used for a qualitative analysis.

We used reflexive thematic analysis to analyze data from the 566 posts (Braun
and Clarke, 2021, 2012). _ATLAS.ti_ was used. A subset of the posts was open
coded by the first author and then discussed and iterated upon in group data
analysis sessions to define the scope of all subsequent analyses—different
uses of GF, perceived ease of using GF, and user values embodying GF’s use and
usability. With that scope in mind, we focused later analysis (selective
coding) on the ease and difficulty of using different features of GF, useful
and non-useful aspects of GF, and user values manifested in their posts. After
over two months of iterative coding, we identified four overarching themes
that interweave to answer our research question.

##  3\. Results

Following the thematic analysis of our data, we identified four overarching
themes that addressed our research question about how different user values
are embodied through GF use and usability. Some values were supported, some
were hindered, while some competed against each other (value tension
(Friedman, 1996)). Primarily, some users found that generative fill was useful
in performing touch-ups to images (such as removing blemishes), expanding
images, and generating prototypical images (borrowing the concept of idealized
prototypes from psychology (Rosch, 1973)). This relationship helped and
hindered users’ values in a variety of ways. The inclusion of these useful
features (touch-ups, etc.) reinforced users’ value of productivity by
replacing complex repetitive tasks with simpler streamlined commands.
Simultaneously, the useful aspects created a value tension within the spectrum
of creativity – both enabling users to do more with less and also adding the
doubt of “Was this made by an AI?”. The change of methods led to hindering
both lived experiences as well as a sense of accomplishment. Finally,
generative fill inherently put designers in both a personal and interpersonal
value tension where regulating content is either too loose or too restrictive.
We conclude that the impact of this technology on creative workers is notable
and that the resolution of value tensions may be found not only in the careful
design of future tools but also in the broader response by society to
sociotechnical progress.

###  3.1. Generative fill is useful

The three tasks that we observed users describing as easy and useful, in a way
that impacted their values, are performing touch-ups, generating prototypical
content, and expanding images. _Performing touch-ups_ is defined as when a
user makes a small change, such as removing a tattoo or removing powerlines to
showcase the subject of the picture better. _Generating prototypical content_
is when a user has an image in mind and has to create a corresponding image to
work with. _Expanding images_ is changing the dimensions of the image,
providing space for marketing text on the image, or changing the background of
images. Before discussing the value implication from these observations, we
will first describe these tasks in more detail.

####  3.1.1. Performing Touch-ups

Without generative fill, touch-ups are complicated, often involving several
tools, and can take a significant amount of time (Chen, 2010). With the tool,
it is simplified. One user commented on the speed-up saying ”30 min work in 30
seconds.” We found users describing the difference with and without the tool:

> ”The time I spent doing selections, clone stamp, repainting, color match…. I
> can now do in seconds.”

Additionally, we found that users described how they could perform the task in
a wide variety of cases. This speaks to the versatility of the method. For
example, one user listed several ways in which they could perform touch-ups
with the tool:

> ”I like to use gen fill to clean up movie and book covers, e.g. remove text
> labels, logos, watermarks.”

####  3.1.2. Generating Prototypical Content

Generating prototypical content is an ideal use case for text-to-image
generative models. GF replaces the need for a creator to search through
existing (stock) photographs until they find one that meets their needs.
Surprisingly, the reaction of users to this particular task was mixed. For
example, while some users approved of GF’s ability to generate new images,

> ”With generative fill I can start with an empty frame and zero stock and end
> with something quite presentable.”

It was also the case that other users struggled with building up from a blank
canvas or generating new images,

> ”If you’re just trying to generate art from nothing, then I can see how
> you’d think it’s useless. It’s just not there yet.”

Workarounds to this problem included limiting the size of edits and using the
tool as a ”word brush”. However, it is worth mentioning that there have been a
number of research initiatives into redesigning the T2I prompting mechanism
(Wang et al., 2023; Brade et al., 2023; Chung and Adar, 2023), indicating that
perhaps the current selection and text prompt mechanism could be further
evaluated for usability.

####  3.1.3. Expanding Images

The ability to extend images received a wealth of positive reactions, being
referred to as ”jaw-dropping”, ”godly”, and a ”game-changer”. The use of the
technology simply involved selecting the area to expand the image and
prompting (or leaving the prompt blank). A user describes the process as
follows:

> ”Say I’m developing a vertical poster, and the image I want to use across
> the full height is horizontal. You can frame the image how you want it and
> generate the gaps in art.”

The user feedback on expanding images was consistent. Wth touch-ups or
prototypical images, there were the occasional concerns that users raised such
as, ”It’s been useful for cleanup work but even then, it hasn’t done anything
I can’t do myself with honestly more predictable results.” On the other hand,
image extension appears to lack the same divisiveness, except for concerns
that exist for all usage of the technology, such as limitations on the
resolution of the images that can be generated.

###  3.2. Generative fill helps with productivity

We define _productivity_ as ”being able to accomplish tasks with less time or
effort.” We observed this to be a key value in users of generative fill, an
observation which has been repeatedly noted elsewhere (Rao, 2024; Inie et al.,
2023). .” Users who valued productivity were concerned with effectively
getting the desired output. One user discusses how they saw this from GF as
the replacement of complex and time-consuming operations with a quicker and
easier workflow. They said,

> ”It speeds my workflow tremendously eliminating a ton of clone stamp/healing
> brush manipulation that is, frankly, a waste of my time.”

Additionally, users often associate productivity with materialistic gains,
such as an ability to have an increased earning potential or decreased work
hours. One user describes their experience of both gains, resulting from their
increase in productivity.

> ”AI has literally turned my 10 hour day into a 2 hour day. And my work has
> improved to the point where I’m making more money in 2 hours than I was in
> 10”

###  3.3. Creativity: helped or hindered?

Making the technology easier to learn and work with, or _reducing barriers to
entry_ , is another observed value of stakeholders in the technology. In fact,
we found that there were a number of users who explicitly highlighted this
proliferation as a value. Two users spoke to this saying,

> ”It’s a creative medium, people will create what they want to and this just
> makes it easier for many to manipulate images in lots of ways.”

and

> ”This tool just cuts down the time spent on it dramatically, as well as
> opening up the ability to do it to people who don’t have years experience
> mastering every little trick.”

However, in contrast with the benefits to less experienced users, there were
also concerns that _AI art is indistinguishable from human work_. We observed
users who highlighted that such increases in creative capacity leads to an
indistinguishability, cheapening the value of their work,

> ”From a hobbyist standpoint everyone’s “art” will look the same. If
> everything from rough idea to finished work is done by a program what’s the
> fucking point? This is stupid.”

One user spoke to the duality of the problem,

> ”It both terrifies and impresses me equally. Photography and static art in
> general as we know it is going to change in one instant more than ever
> before.”

In VSD this is what is known as a value tension, where multiple values exist
that are in opposition but potentially could be made to coexist. (Friedman,
1996). Since these two values are in tension, resolving the tension would mean
finding a solution that addresses both the concerns around authenticity of
human art while still keeping the creative opportunities afforded by GF.

###  3.4. The Changing craft: a feeling of loss

Values related to what can emerge from the existence of the tool tell only
part of the story. The aspects of life that are lost with the emergence of new
technology are also a key part of the user experience.

The experience of the _”Lived Body” is potentially lost_ if technologies like
GF replace the need for ”in the world” work. ”Lived Body” comes from the work
of Merleau-Ponty (Merleau-Ponty et al., 2013). In his phenomenological
philosophy, he considers the experiences of the body such as the quality of
the air, the smells and sounds, and the overall feeling of being in a place,
as essential components of how we perceive the world. Being present in an
environment with a camera is one instance of this. Some users claim that the
inspiration for being in a location provides its own value beyond that of the
quality of the image. One user accounts how this is important, should it be
possible,

> ”[I]f I were to be doing a photoshoot and wanted it to be in the rainforest
> and could afford it, I’d want to go to the freaking rainforest. Not just for
> how it looks, but the feeling and inspiration that comes with shooting on
> location.”

Simultaneously, GF also has the potential to make obsolete old modes of work,
which would lead to a _loss of pride in craft_. Practitioners with Generative
Fill claim that they enjoy the work (a user says ”I enjoy retouching” and
another says that the new technique ”sounds really boring”). Loss of pride in
craft is observed as the notion that people took pride in their method of work
and enjoyed the method. In comparison, one user languishes over the new method
of work,

> ”Quite frankly I don’t feel the same sense or accomplishment upon completion
> either because it’s just so easy.”

###  3.5. Respect for dignity and privacy and freedom of expression

Finally, we found that there is a value tension that exists around what GF is
capable of, and how it is regulated. Particularly, the question is how the
ethical decision should be made if the creation of content is allowed or
prohibited. Adobe addressed this issue directly in the technology with the
creation of ”guidelines” that would block the use of GF if it determined the
user was attempting to make illicit content.

The two values clearly conflict with each other concerning explicit content.
The respectful use of technology, not creating unsettling content or directly
manipulating someone’s person without their consent, maintains individuals’
_sense of dignity and privacy_. And simultaneously there is the belief that an
individual should be _free to create content_ without interference if they are
using it for legitimate means.

Adobe’s guideline restrictions prevent unethical use of the technology _by_
preventing the creation of the type of images that could be considered
explicit. As an example specifically of how this led to values coming into
tension, we look at two users who took the issue from two different
perspectives. The first spoke to the risk of the feature being used for fake
pornographic images of people, which should be censored,

> ”I think it would be a very bad idea to have a feature in Photoshop that
> allows you to easily make high-quality fake nude images of people.”

While this is a valid instance for censorship, sometimes censorship is used
when the use case is legitimate. For example, how do you choose to censor
nudity in the case of classical art which often works with nude models? One
user comments on their frustration with getting censored repeatedly as they
are trying to go about their work,

> ”I know a lot of us work with nudity and it’s extremely annoying that we are
> getting an insane amount of guidelines violation messages when working on
> those pictures.”

We found that a large number of users were bothered by guideline violations
(the current solution to the problem of illicit content), so it would seem
reasonable to expect that a more nuanced solution is still necessary.

##  4\. Future Work

The value of creative arts to society is fundamental (Milbrandt, 2010; Stuckey
and Nobel, 2010). Creativity is a uniquely human quality (Balter, 2009).
Today, however, generative AI applications can produce new content in the form
of text, images, audio, and video, or a combination of those. While some
creators are experimenting with these tools to augment their creative work,
some are alarmed at how they can impact their lives and livelihoods, e.g., by
creating unfair competition (Walton, 2024). Some have even speculated a future
“techlash” against algorithmically generated content, where people begin to
value authentic creativity more and be willing to pay a premium for “human-
made”(De Cremer et al., 2023). This new value, the ability to create,
recognize, and appreciate art created by a human, without any AI assistance,
is then shaped not due to how the technology affects the creators or
consumers, per se, but rather the position the technology places them in.
Thus, it is important to understand how the work practices of creative
professionals are evolving with the evolution of generative AI tools and which
of their values are being helped, hindered, or are in tension.

In this paper, we reported results from a preliminary study on how users’
values are embodied in ways that they find Photoshop’s Generative Fill (GF)
meaningful to their workflow. As a future step, it is important to understand
how individual differences in creative professionals influence their value
perceptions when using generative models in their work practices. For example,
how age, experience, and expertise would influence how people use generative
tools in creative work? And, as a result, how would workforce training, skill-
building, hiring, and mentoring practices change? Would prompt engineering be
a required skill in graphic design in the future? How would the lived
experiences of people play a role in how they use generative models in
creative work practices? On the other hand, it will also be interesting to
study how these tools can help people who are not in the creative profession
express their creativity better. And when doing so, how can these tools uphold
their values, like becoming an expert with colors or mastering portrait
photography?

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