# Reconfiguring Participatory Design to Resist AI Realism

Aakash Gautam  University of PittsburghPittsburghPAUSA<EMAIL_ADDRESS>
(2024)

###### Abstract.

The growing trend of artificial intelligence (AI) as a solution to social and
technical problems reinforces AI Realism—the belief that AI is an inevitable
and natural order. In response, this paper argues that participatory design
(PD), with its focus on democratic values and processes, can play a role in
questioning and resisting AI Realism. I examine three concerning aspects of AI
Realism: the facade of democratization that lacks true empowerment, demands
for human adaptability in contrast to AI systems’ inflexibility, and the
obfuscation of essential human labor enabling the AI system. I propose
resisting AI Realism by reconfiguring PD to continue engaging with value-
centered visions, increasing its exploration of non-AI alternatives, and
making the essential human labor underpinning AI systems visible. I position
PD as a means to generate friction against AI Realism and open space for
alternative futures centered on human needs and values.

agonism, adversarial design, politics, power, artificial intelligence

††journalyear: 2024††copyright: rightsretained††conference: Participatory
Design Conference 2024, Vol. 2: Exploratory Papers and Workshops; August
11–16, 2024; Sibu, Malaysia††booktitle: Participatory Design Conference 2024,
Vol. 2: Exploratory Papers and Workshops (PDC ’24 Vol. 2), August 11–16, 2024,
Sibu, Malaysia††doi: 10.1145/3661455.3669867††isbn:
979-8-4007-0654-7/24/08††ccs: Human-centered computing Interaction design
theory, concepts and paradigms

##  1\. Introduction

“It’s easier to imagine the end of the world than the end of capitalism”,
writes Mark Fisher (Fisher, 2022, pp. 1) to illuminate the change in global
polity, which since the fall of the Soviet Union around 1991 has portrayed
that there is no alternative to capitalism. Fisher calls this phenomenon
“Capitalist Realism” (Fisher, 2022). In the era of capitalist realism, every
aspect of our lives is governed by capitalist rules and values. At the same
time, various mechanisms for shaping a collective imagination that does not
see a possibility beyond capitalistic approaches are deployed, including
conflating capitalism with democracy, emphasizing the power of political
participation, and contrasting the current order with tyrannical socialist
governments that failed.

We have also entered the era of AI Realism, where problems that were
previously attempted to be solved collectively through social and technical
mechanisms are now proposed to be solved through corporate-controlled AI
(c.f., (Castelvecchi, 2022; Clayton, 2024)). The “AI” I talk about here is not
the statistical approach but rather the much-hyped larger ecological system
that is being pushed by corporations. The growing use of generative AI systems
in different workplaces (Castelvecchi, 2022), academic venues’ rules on the
use of ChatGPT or other large language models (for Computing Machinery, 2023),
and the growing use of chatbots by existing services (Hale, 2023) all point to
the fact that we have arrived at AI Realism. The shift to AI realism has been
more rapid than that of capitalist realism though. The success of AlexNet in
2012 began a drive to push AI as _the_ solution to a range of problems
(Christian, 2020), and by 2022, with the release of ChatGPT, it had captured
the public’s imagination as a panacea (e.g., (De Angelis et al., 2023; Hale,
2023; Extance, 2023)). In some cases, there have been concerted efforts to
push AI as a solution, in others, we inadvertently accepted it.

More than the striking parallels, Capitalist and AI Realism are closely
connected. AI is a tool that existed long before 2012, but _the_ AI that is
being pushed widely is more than a statistical tool. It is the tool as used by
and defined by the capitalist; a technological mechanism that enables the
concentration of resources and power, hidden behind the veil of neutral-
seeming statistical formalism111Although statistical-AI and AI-as-capitalist-
instrument are tied to some extent considering the corporate control over the
data and other infrastructure (Whittaker, 2021).. The pervasive promotion of
AI with claims of efficiency, productivity, and various other capital value
creation — referred to as “AI snake oil” (Narayanan, 2019) — has distracted us
from examining details of the value and the potential for harms that the
system brings.

In the face of growing AI Realism, PD has a significant role to play. To
illustrate this, I begin by questioning three aspects of AI that are promoted
widely. First, the claims of “democratizing AI” lack the necessary
distribution of power to the people affected by the AI system to be
democratic. Second, the popular imagery of AI for personalization makes
significant demands of human malleability and erodes opportunities for
collective action. Third, the image of AI systems as efficiency-promoting
obfuscates the labor required to enable them. Reflecting on these three
aspects, I argue for reconfiguring PD methods to resist AI Realism. These
reconfigurations include contesting narrow metrics to enable broader visions
of the future, incorporating explorations of non-AI alternatives while
identifying possibilities, and making visible all the human labor that enables
AI systems. In doing so, I seek to (re)position PD as a force for setting
agendas and upholding democratic participation, and, with it, centering human
values in the era of AI Realism.

##  2\. Literature Review: AI and the Core Tenets of PD

Participatory design (PD) finds its roots in the 1970s with the workers’ push
for greater control and democratization amidst the introduction of new
technology in the workplace (Bødker and Kyng, 2018). Over the years, PD
scholarship, within the workplace and beyond it, has highlighted tenets like
carefully developed participation, mutual learning between workers and
designers, and envisioning alternative technological futures (Ehn, 2008).
These principles support creative cooperation in designing new sociotechnical
systems, thus enabling potential space to challenge existing power dynamics
(Bratteteig and Wagner, 2012; Muller and Druin, 2012). In some respect, the
wheels of time have brought us back to the 1970s with the advent of AI in the
workplace and beyond, with growing concerns of drastic changes in work
practices and values (Muro et al., 2019).

AI systems bring a set of unique characteristics, some common with other
large-scale technologies and a few unique to it. Among the unique ones, AI
systems often lack transparency in how it was created and evaluated, in data
collection, model development, fine-tuning, and impact evaluation (Pushkarna
et al., 2022; Gebru et al., 2021; Raji et al., 2020; Sambasivan et al., 2021).
Second, AI system development is expensive and typically deployed on large
scales, thus requiring infrastructure that only a few can afford (Whittaker,
2021). Third, AI as a “modern technology” is justified for a fast-paced push,
where rapid development and deployment evades auditing, evaluation, and even
regulations. In this sense, the proliferation of AI embodies Silicon Valley’s
ethos of “move fast and break things” (Taplin, 2017) or “ask for forgiveness,
not permission” (Davies, 2019). Given its pervasiveness, we are all being
participated in its growth by providing it with data; at times, there seems
like there is no alternative.

Efforts have been made to promote transparency and create possibilities for
broader group engagement (e.g., (Pushkarna et al., 2022; Gebru et al., 2021;
Mitchell et al., 2019; Raji et al., 2020)). But these are technological
solutions that assume AI is inevitable; the challenge of AI is a “social,
cultural, political, and ethical one” (Loi et al., 2018, pp. 2) where the
technical decisions intermingle with social responsibilities. The majority of
participatory AI movements have been devoid of _empowered_ engagement of
broader stakeholders, including the possibilities to challenge existing power
differences or opportunities for recourse and refusal (Delgado et al., 2023;
Birhane et al., 2022; Bratteteig and Verne, 2018; Williams et al., 2022). They
fail to illuminate broader and alternative futures, thus further solidifying
the position of existing AI as the inevitable “natural order”. In such cases,
Fisher contends, we “must reveal what is presented as necessary and inevitable
to be a mere contingency just as it must make what was previously deemed to be
impossible seem attainable” (Fisher, 2022, pp. 17). Revealing these
alternative possibilities and enacting moves towards realizing them —i.e.,
resisting AI Realism—is where PD can play a significant role.

##  3\. The Real Behind the Promoted Reality of AI Realism

Table 1. Contrast between the promoted reality and the Real in AI Realism Promoted reality | Real
---|---
| Democratized AI with
---
transparency
| Lack of data ownership
---
and control
| Personalized
---
recommendations
| Human malleability,
---
machine intransigence
Automation and Efficiency |  | Human labor behind the
---
infrastructure

Fisher, drawing from Lacan (Lacan, 1988), argues for invoking the ‘Real’, that
which threatens the promoted “reality”. The Real is the “traumatic void that
can only be glimpsed in the fractures and inconsistencies in the field of
apparent reality” (Fisher, 2022, pp. 18). For Fisher the Reals that can be
presented to challenge the realities in capitalist realism are ongoing and
increasing environmental catastrophes challenging the growth fetish of
capitalism, the burgeoning mental health crisis that undermines the push for
privatization of stress, and the proliferation of bureaucracy in capitalism
despite the widespread claims of arriving at market-driven efficiency (Fisher,
2022). Drawing parallels, I share three Reals that could open avenues to
question the reality promoted in AI Realism (Table 1).

###  3.1. Participation Without Power Is Not Democratic

In many Participatory AI efforts, participation focuses on inclusion for
design input rather than a critical mechanism to support people in setting an
agenda. For instance, on reviewing 80 participatory AI projects, Delgado et
al. (2023) found that only three projects involved stakeholders throughout the
design process, and only ten involved them in setting the scope and purpose of
AI. The effect of this is that those efforts presuppose AI as a given with its
claims of benefits unchecked; participation is constrained to small-scale
inputs on improving the development and deployment of AI.

From this perspective, participatory AI is deployed as a mechanism to co-opt
human labor toward acquiescence (Birhane et al., 2022; Ahmed, 2020). This
weakened participation stems from perverse incentive structures benefiting
corporate AI — they benefit from expanding AI adoption with the facade of
people’s participation. There have been exception cases of community ownership
and governance of data practices and AI systems, including deciding whether to
use AI (e.g., (Cofey, 2021)). However, constraints such as limited time and
resources are often cited as barriers, leaving open questions of _how_
communities can be empowered in governing AI systems. Indeed, the opacity and
rapid evolution of AI systems raise questions on what even constitutes
meaningful participation.

Bratteteig and Verne (2018), discussing if AI makes PD obsolete, argue for
participation in (1) envisioning ideas with knowledge of what AI can and
cannot do, (2) concretizing design ideas by engaging with values and visions,
and (3) evaluating the impact of the decision. While these are important
steps, they are not sufficiently political. Knowing the capabilities, which is
a motivation for explainable AI, does not challenge the techno-deterministic
stance that it promotes; participation is co-opted to attend to technical
solutions conveniently solved through AI rather than engaging in institutional
or social possibilities. Similarly, discussing higher-level values and visions
only has the risk of arriving at consensus on systems that in reality may
necessitate surveillance and other large-scale data collection mechanisms that
the participants may not be aware of. Transparency and co-designing processes
involved in developing and deploying AI systems needs to be added to the
process of making collective decisions about AI. Further, for participants to
evaluate the impact of AI systems especially in the design phase is
challenging since effects are unknown until development and, often, seen after
long-time deployment (Bratteteig and Verne, 2018). Thus, participation should
involve collectively establishing and bringing into practice accountability
structures before the development or deployment of AI technologies.

###  3.2. Personalization Demands Human Malleability and Erodes Mutual
Learning

Personalization is one of the most visible aspects of AI. Marketing rhetoric
pushes personalization as a universally desired feature, with one McKinsey
report declaring, “Consumers don’t just want personalization, they demand it”
(Arora et al., 2021). Despite widespread concerns about privacy and lack of
awareness regarding the types of data being collected (Liao and Sundar, 2021),
AI systems continue to collect data for personalization, which indicates
users’ limited agency in challenging this juggernautal drive towards
personalization.

Personalization surfaces two tensions with PD. First, personalization in AI
systems demands human malleability. This is not a new phenomenon: people
accommodate technology by changing their behavior and practices around the
technology (Suchman, 2007; Liao et al., 2018). But with AI, we are required to
accommodate it both while using it and, since AI is trained in use, in
training it. Gyldenkærne et al. (2020), for example, in studying AI use in
electronic health records in Denmark, highlight how an AI system in providing
personalized insights imposed a new process of data collection even though the
clinician’s existing processes were sufficient for their purposes. It not only
changed the way clinicians recorded patient data but since it was rife with
ambiguity, it made additional demands on the clinicians to attend to the data
processes, moving them away from the primary purpose of their work. Moreover,
human flexibility demanded in training the AI reifies AI’s assumptions about
us and our practices, leaving little room for us to change those. Going back
to the case of the clinicians, despite the mismatch in their practices and the
new AI-based processes, they did not have room for recourse. Instead, they had
to devise additional workarounds (Gyldenkærne et al., 2020). This challenge of
ensuring users’ control over their tools and their environment is a classic
problem that PD has grappled with (Bratteteig and Wagner, 2016), but now with
a much-pervasive technological system.

Second, personalization creates its own context, influencing the user to think
about aligning to the demands of the system rather than finding opportunities
to make common ground with others. It raises challenges for surfacing shared
experiences and engaging in collective action. We are forced to work on
pushing back on the limitations and impositions of our own spaces rather than
drawing commonalities among a larger mass to push against the system. This
echoes the Marxist notion of ideology — that AI systems are _the_ thing — and
acquiescing the alienation that it brings. A significant exercise of
abstraction is necessary to arrive at shared experiences and opportunities for
mutual learning; these skills are not easily accessible to all.
Personalization seeks to make participation individualistic. For PD, which
relies on forming publics — of people forming attachments to shared matters of
concern (Dantec and DiSalvo, 2013) — this poses a challenge.

###  3.3. Claims of Efficient AI Hide Away the Hidden Labor

Proclamations of AI’s potential to drive efficiency and unprecedented scale
abound. Hidden behind the veneer of magical-seeming machine intelligence lies
the indispensable critical infrastructure of human labor engaged in essential
tasks of collecting, cleaning, and labeling data. This is not accounted for in
the claims of AI’s efficiency! For instance, the ImageNet dataset which was
the foundation for the AlexNet model in 2012, required upwards of 25,000
people (Amazon Mechanical Turks) to build the dataset, with more than 45% of
them making less than USD$5 a week (Li, 2010). This labor is rarely counted.

The labor is typically sourced out to the Global South where worker conditions
and compensations are, at best, questionable (e.g., (Wang et al., 2022;
Williams et al., 2022; Gray and Suri, 2019)). The indispensable human labor is
obfuscated behind claims of technological progress of AI. Paradoxically, the
very AI that relies on the labor of many is employed to justify efficiency
drives, leading to layoffs elsewhere (Thorbecke, 2024). A prominent example of
this is the recent Duolingo layoff, where workers at most precarious positions
were laid off citing AI-enabled
efficiency222https://www.washingtonpost.com/technology/2024/01/10/duolingo-ai-
layoffs/.

There are two facets to the problem. First, the promotion of values around
efficiency and scale brought by AI normalizes the practice of hiding and often
exploiting large human labor (Gray and Suri, 2019). The growing reliance on
labor from the Global South enabled through complex sub-contracting practices
has created mechanisms to evade accountability. Those who use and are affected
by AI are placed at a distance from those who build the infrastructure for
that AI, enabling very little space for collective action and solidarity
(Williams et al., 2022).

Second, “efficiency” is presented as the only metric that should guide our
endeavors. It has broader societal implications. Examining the reality of what
kind of labor is hidden and what kinds of work continue to be prized, we
notice that the push for efficiency is top-down. The acceptance of efficiency
requires acquiescing to unilateral decisions. It leaves no room to explore
alternative visions (e.g., promoting adaptability or interconnectedness) that
we could collectively design (Huybrechts et al., 2020), even when efficiency
may conflict with our collective well-being (Schwartz, 2020). These two facets
— hiding the labor and leveraging the labored artifact to squeeze greater
efficiency — enable those with the power to create and deploy AI tools to
concentrate wealth and resources.

##  4\. Discussion: PD in the Era of AI Realism

PD, with its emphasis on forming attachments to matters of concern (Dantec and
DiSalvo, 2013), is uniquely positioned to question the relentless push for AI
and create space to determine _if and how_ AI should be developed and
deployed.

AI is not a modular unit; it is ecological and does not function in isolation.
It is not monolithic either. Each AI system taps into and brings with it a
myriad of standards, processes, and values, each of which in turn influences
our actions and values. However, AI Realism pushes an instrumental and utopic
view of AI, focused solely on outcomes (e.g., efficiency and growth) while
obfuscating the complex processes and tradeoffs involved. In contrast, PD
engages with processes that emphasize forming publics for collective
envisionment, surfacing diverse values, and negotiating decisions over the
technologies that impact them. More generally, there is an inherent tension:
PD acknowledges diverse human conditions and seeks to support pluralism and
individual agency in collectively realizing a shared vision of the future,
whereas AI Realism seeks to — or rather requires — imposing AI as _the_
solution, removing it from the nuanced contextual differences and
uncertainties inherent in our human condition. This oppositional stance of PD
can be constructive, allowing it to steer the relentless drive of AI Realism
toward values that matter.

Research on agonistic pluralism in PD (e.g., (Björgvinsson et al., 2012;
DiSalvo, 2015)) provide guidance on creating the necessary friction to realize
positive change. Scholars argue for embracing an adversarial stance rather
than avoiding conflict. It is needed in the face of AI Realism. Drawing from
it, I propose three initial reconfiguration of participatory methods to create
space for resisting AI Realism. First, contesting dominant metrics to support
engagements with broader visions and possibilities for the future. Second,
including possibilities of developing non-AI solutions as alternatives to
distill needs and situations that truly require AI solutions. Third, surfacing
and engaging with the necessary human labor that is required to make the AI
system function before making decisions on incorporating AI. Embracing these
would allow participatory approaches to move beyond questioning whether AI
works or how to make it work, towards surfacing systemic issues, assumptions,
and impositions that underlie AI Realism.

###  4.1. Beyond Dominant Metrics: Engaging in Value-Centered Visions

The incessant focus on scale and efficiency — a root element of modern-day
capitalism — finds fuel in AI systems. These metrics fail to capture the
complex values that we care about. Yet, in the rapid pace of incorporation, we
rarely have an opportunity to question what values AI systems enforce and
whether we want them.

There is a need to design alternative visions collectively. I echo Huybrechts
et al. (2020, pp. 7), who posit that “designing ‘visions’ can turn the tension
between addressing the focus on the big issues and the close attention to the
particularity of relations into a dynamic dialogue that can repoliticise
design.” However, given the dominant focus on scale and efficiency, there is a
need to first contest those metrics and discuss alternatives such as commoning
and post-growth models (e.g., (Sharma et al., 2023; Teli et al., 2020)).

As we engage in designing for alternative visions, it is equally important,
given the global pervasiveness of AI systems, to engage both with the local
priorities and the global realities (Bødker and Kyng, 2018), acknowledging and
attending to the potential impacts, tradeoffs, and tensions at play across
these levels (Gray and Suri, 2019; Crawford, 2021). Moreover, AI is not a
monolithic system. Deconstructing AI into its constitutive parts is necessary
to understand the vision it embodies. To this end, we may, for example, bring
together the precarious workers early in the AI development pipeline and the
higher-paid, slightly empowered workers — the recent layoffs suggest no worker
is truly in control — in the design process of alternative visions and
possibilities.

###  4.2. Plural Possibilities: Exploring Non-AI Alternatives

Moving past the dominant metrics is an important first step. A subsequent step
along the line involves ensuring that PD methods make space for participants
to enact their visions through non-AI alternatives.

Specifically engaging with non-AI alternatives is important for the very act
of co-designing AI systems, even when they are critical of other existing AI
systems, enforces the belief that AI is necessary or inevitable. Exploring
non-AI alternatives challenges the assumptions that AI systems are better or
necessary. Further, the perception of AI as a widely-encompassing solution has
made it easier to brush aside organizational and/or social problems. Engaging
in non-AI alternative solutions may surface issues that cannot be fixed by
technology.

While exploring non-AI alternatives may not necessarily lead to the refusal of
AI, it will afford space to critically reflect and distill aspects of the
participants’ needs and identify situations where AI is truly needed. In doing
so, participants can deepen their understanding of the situation, and if AI is
deployed, critically understand the role the AI system plays in the solution.
This can help them gain some power over the AI systems.

Moreover, exploring non-AI alternatives opens possibilities to reflect on what
would be lost when a situation is reformulated to make it amenable to AI
ecosystem. Scholars warn about “algorithmic governmentality,” where only
outcomes that can be measured and realized through algorithms will be pursued
(Nayak, 2021; Rouvroy et al., 2013). It is a reductive move, taking us away
from uncertainties and politics to perfectly objective outcomes with no room
for deliberation and recourse (Rouvroy et al., 2013). Exploring non-AI
alternatives enables space to engage with uncertainties and the inherent
messiness in our collective visions and values, potentially leading to plural
possibilities of being.

###  4.3. Delegation Inversion: Making Visible the Human Labor

Earlier, I presented two stages where human labor was essential to make the AI
systems function: in creating AI systems such as with data labeling work, and
our efforts to accommodate AI despite its rigidity. Both these essential labor
are hidden from the popular narrative of AI. PD methods should surface and
engage with it, particularly making it central in the decision-making process
when participants are exploring AI-based possibilities. This echoes scholars
who have long argued for making work visible (Suchman, 1995; Gray and Suri,
2019).

For PD to resist AI Realism, we have to surface and engage with the realities
of the hidden labor at the time of design envisionment. To enable this, I
build on Latour, who, using a door as an exemplary technology, expounds on the
value of “delegation”, i.e., the work the technology does that humans no
longer have to do (Johnson, 1988). I contend that for pervasive systems like
AI, PD methods can be configured to engage participants in “delegation
inversion” — cataloging the additional work and sacrifice humans have to make
for the technology to function. For instance, participants could create a
“labor accountability card” to record information such as the number of data
labelers, total labeling hours, and the working conditions of those
contributing to the system supporting their envisioned AI solution. Similarly,
to enhance shared experiences regarding human adaptability, participants could
note places of friction in using existing AI systems and where they needed to
be flexible.

These efforts of surfacing and engaging with the labor will help illuminate
the cost of developing and deploying AI solutions, thus seeding informed
decision-making on whether it is worth it. It can potentially create space for
enacting alternative visions that do not demand such cost.

These three reconfigurations of PD methods — contesting narrow metrics while
envisioning possibilities, incorporating explorations of non-AI alternatives,
and illuminating the often-hidden labor enabling AI — aim to position PD as a
powerful mechanism to resist AI Realism. They surface societal costs incurred
by AI Realism, while simultaneously empowering people to shape or refuge
technologies based on the values they care about.

However, this is a conversation at an early stage of PD’s evolution in the era
of AI Realism. There is critical work ahead for the PD community, both in
research and practice. We, as PD researchers and practitioners, must continue
to challenge the dominant drive of AI solutionist approaches and ensure that
the public is empowered to enact the future that _they_ desire. It is, indeed,
a call for us to push back on AI Realism toward democratic, caring, and just
futures shaped through collective decision-making. The stakes could not be
higher.

##  5\. Conclusion

The fundamental question of AI Realism is about making decisions on the
distribution of resources and power. It is asking, “Who should have the
decision-making power on whether, what, how, when, where, and why AI systems
should shape our collective future?” As it stands, the power differences are
highly skewed towards those who already control AI, and challenging it — i.e.,
resisting AI Realism — is where I see PD play a significant role. PD has
always been political, but in the face of AI Realism, we need a vigorous
renewal of the political. I argue for repositioning PD as a mechanism to
create friction in the incessant focus on outcome that AI Realism pushes by
engaging with the processes enabling AI and the values inherited by adopting
it. Resisting the relentless push for AI adoption is an attempt to prioritize
human values and justice over the corporatization and centralization of
resources and power. As such, this work attends to the theme of PDC 2024 in
arguing to extend the notion of participation beyond the specific methods
towards being a driver for setting agendas in the face of AI Realism and
establishing initiatives to reflect and facilitate alternative futures. This
calls for an alliance of researchers and practitioners who see PD as a
democratic force to look outward and leverage PD methods to address root
issues in the era of AI Realism, rather than inward at refining our methods.

###### Acknowledgements.

I am grateful to Aditya Nayak and Deborah Tatar for their feedback.

## References

  * (1)
  * Ahmed (2020) Alex Ahmed. 2020\.  We Will Not Be Pacified Through Participation.  https://assets.pubpub.org/zy0iuc4f/61d44af5-cf5b-4c40-8b0e-5ea160a5e956.pdf.  _Tech Otherwise_ 15, 10 (2020).
  * Arora et al. (2021) Nidhi Arora, Daniel Ensslen, Lars Fiedler, Wei Wei Liu, Kelsey Robinson, Eli Stein, and Gustavo Schüler. 2021.  The value of getting personalization right—or wrong—is multiplying.  https://www.mckinsey.com/capabilities/growth-marketing-and-sales/our-insights/the-value-of-getting-personalization-right-or-wrong-is-multiplying.
  * Birhane et al. (2022) Abeba Birhane, William Isaac, Vinodkumar Prabhakaran, Mark Diaz, Madeleine Clare Elish, Iason Gabriel, and Shakir Mohamed. 2022\.  Power to the people? opportunities and challenges for participatory AI.  _Equity and Access in Algorithms, Mechanisms, and Optimization_ (2022), 1–8.  https://doi.org/10.1145/3551624.3555290
  * Björgvinsson et al. (2012) Erling Björgvinsson, Pelle Ehn, and Per-Anders Hillgren. 2012\.  Agonistic participatory design: working with marginalised social movements.  _CoDesign_ 8, 2-3 (2012), 127–144.
  * Bødker and Kyng (2018) Susanne Bødker and Morten Kyng. 2018.  Participatory design that matters—Facing the big issues.  _ACM Transactions on Computer-Human Interaction (TOCHI)_ 25, 1 (2018), 4.  https://doi.org/10.1145/3152421
  * Bratteteig and Verne (2018) Tone Bratteteig and Guri Verne. 2018.  Does AI make PD obsolete? exploring challenges from artificial intelligence to participatory design. In _Proceedings of the 15th Participatory Design Conference: Short Papers, Situated Actions, Workshops and Tutorial-Volume 2_. 1–5.  https://doi.org/10.1145/3210604.3210646
  * Bratteteig and Wagner (2012) Tone Bratteteig and Ina Wagner. 2012.  Disentangling power and decision-making in participatory design. In _Proceedings of the 12th Participatory Design Conference: Research Papers-Volume 1_. 41–50.  https://doi.org/10.1145/2347635.2347642
  * Bratteteig and Wagner (2016) Tone Bratteteig and Ina Wagner. 2016.  What is a participatory design result?. In _Proceedings of the 14th Participatory Design Conference: Full papers-Volume 1_. 141–150.  https://doi.org/10.1145/2940299.2940316
  * Castelvecchi (2022) Davide Castelvecchi. 2022\.  Are ChatGPT and AlphaCode going to replace programmers?  _Nature_ (2022).  https://doi.org/10.1038/d41586-022-04383-z
  * Christian (2020) Brian Christian. 2020\.  _The alignment problem: Machine learning and human values_.  WW Norton & Company.
  * Clayton (2024) James Clayton. 2024\.  CES 2024: AI pillows and toothbrushes - is it all getting a bit silly?  https://www.bbc.com/news/technology-67959240.
  * Cofey (2021) Donavyn Cofey. 2021\.  Maori are trying to save their language from Big Tech.  _Wired UK (April 2021)_ (2021).
  * Crawford (2021) Kate Crawford. 2021\.  _The atlas of AI: Power, politics, and the planetary costs of artificial intelligence_.  Yale University Press.
  * Dantec and DiSalvo (2013) Christopher A Le Dantec and Carl DiSalvo. 2013.  Infrastructuring and the formation of publics in participatory design.  _Social Studies of Science_ 43, 2 (2013), 241–264.  https://doi.org/10.1177/0306312712471581
  * Davies (2019) Jamie Davies. 2019\.  Silicon Valley’s ‘ask for forgiveness, not permission’ attitude is wearing thin.  https://www.telecoms.com/mobile-devices/silicon-valley-s-ask-for-forgiveness-not-permission-attitude-is-wearing-thin.
  * De Angelis et al. (2023) Luigi De Angelis, Francesco Baglivo, Guglielmo Arzilli, Gaetano Pierpaolo Privitera, Paolo Ferragina, Alberto Eugenio Tozzi, and Caterina Rizzo. 2023.  ChatGPT and the rise of large language models: the new AI-driven infodemic threat in public health.  _Frontiers in Public Health_ 11 (2023), 1166120.
  * Delgado et al. (2023) Fernando Delgado, Stephen Yang, Michael Madaio, and Qian Yang. 2023\.  The Participatory Turn in AI Design: Theoretical Foundations and the Current State of Practice. In _Proceedings of the 3rd ACM Conference on Equity and Access in Algorithms, Mechanisms, and Optimization_. 1–23.  https://doi.org/10.1145/3617694.3623261
  * DiSalvo (2015) Carl DiSalvo. 2015\.  _Adversarial design_.  Design Thinking, Design Theory.
  * Ehn (2008) Pelle Ehn. 2008\.  Participation in design things. In _Proceedings of the tenth anniversary conference on participatory design 2008_. Indiana University, 92–101.  https://doi.org/10.5555/1795234.1795248
  * Extance (2023) Andy Extance. 2023\.  ChatGPT has entered the classroom: how LLMs could transform education.  _Nature_ 623, 7987 (2023), 474–477.  https://doi.org/10.1038/d41586-023-03507-3
  * Fisher (2022) Mark Fisher. 2022\.  _Capitalist realism: Is there no alternative?_ John Hunt Publishing.
  * for Computing Machinery (2023) Association for Computing Machinery. 2023\.  ACM Policy on Authorship.  https://www.acm.org/publications/policies/new-acm-policy-on-authorship.
  * Gebru et al. (2021) Timnit Gebru, Jamie Morgenstern, Briana Vecchione, Jennifer Wortman Vaughan, Hanna Wallach, Hal Daumé Iii, and Kate Crawford. 2021\.  Datasheets for datasets.  _Commun. ACM_ 64, 12 (2021), 86–92.  https://doi.org/10.1145/3458723
  * Gray and Suri (2019) Mary L Gray and Siddharth Suri. 2019.  _Ghost work: How to stop Silicon Valley from building a new global underclass_.  Eamon Dolan Books.
  * Gyldenkærne et al. (2020) Christopher H. Gyldenkærne, Gustav From, Troels Mønsted, and Jesper Simonsen. 2020.  PD and the challenge of AI in health-care. In _Proceedings of the 16th Participatory Design Conference 2020-Participation (s) Otherwise-Volume 2_. 26–29.  https://doi.org/10.1145/3384772.3385138
  * Hale (2023) Erin Hale. 2023\.  ChatGPT is giving therapy. A mental health revolution may be next.  https://www.aljazeera.com/economy/2023/4/27/could-your-next-therapist-be-ai-tech-raises-hopes-concerns.
  * Huybrechts et al. (2020) Liesbeth Huybrechts, Maurizio Teli, Mela Zuljevic, and Mela Bettega. 2020.  Visions that change. Articulating the politics of participatory design.  _CoDesign_ 16, 1 (2020), 3–16.
  * Johnson (1988) Jim Johnson. 1988\.  Mixing humans and nonhumans together: The sociology of a door-closer.  _Social problems_ 35, 3 (1988), 298–310.  333Bruno Latour published the article under a pseudonym. Please see the footnote on page 304 of the article.
  * Lacan (1988) Jacques Lacan. 1988\.  _The Seminar of Jacques Lacan_.  WW Norton & Company.
  * Li (2010) Fei-Fei Li. 2010\.  ImageNet: Crowdsourcing, benchmarking & other cool things.  https://www.image-net.org/static_files/papers/ImageNet_2010.pdf.  _CMU VASC Seminar_ (2010).
  * Liao and Sundar (2021) Mengqi Liao and S Shyam Sundar. 2021.  How should AI systems talk to users when collecting their personal information? Effects of role framing and self-referencing on human-AI interaction. In _Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems_. 1–14.  https://doi.org/10.1145/3411764.3445415
  * Liao et al. (2018) Q Vera Liao, Muhammed Mas-ud Hussain, Praveen Chandar, Matthew Davis, Yasaman Khazaeni, Marco Patricio Crasso, Dakuo Wang, Michael Muller, N Sadat Shami, and Werner Geyer. 2018\.  All Work and No Play? Conversations with a Question-and-Answer Chatbot in the Wild. In _Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems_. 1–13.  https://doi.org/10.1145/3173574.3173577
  * Loi et al. (2018) Daria Loi, Thomas Lodato, Christine T Wolf, Raphael Arar, and Jeanette Blomberg. 2018.  PD manifesto for AI futures. In _Proceedings of the 15th Participatory Design Conference: Short Papers, Situated Actions, Workshops and Tutorial-Volume 2_. 1–4.  https://doi.org/10.1145/3210604.3210614
  * Mitchell et al. (2019) Margaret Mitchell, Simone Wu, Andrew Zaldivar, Parker Barnes, Lucy Vasserman, Ben Hutchinson, Elena Spitzer, Inioluwa Deborah Raji, and Timnit Gebru. 2019.  Model cards for model reporting. In _Proceedings of the conference on fairness, accountability, and transparency_. 220–229.  https://doi.org/10.1145/3287560.3287596
  * Muller and Druin (2012) Michael J Muller and Allison Druin. 2012.  Participatory design: the third space in human–computer interaction.  In _Human Computer Interaction Handbook_. CRC Press, 1125–1153.
  * Muro et al. (2019) Mark Muro, Robert Maxim, and Jacob Whiton. 2019.  _Automation and Artificial Intelligence: How machines are affecting people and places_.  https://www.brookings.edu/research/automation-and-artificial-intelligence-how-machines-affect-people-and-places/.
  * Narayanan (2019) Arvind Narayanan. 2019\.  How to recognize AI snake oil.  https://www.cs.princeton.edu/~arvindn/talks/MIT-STS-AI-snakeoil.pdf.  _Arthur Miller Lecture on Science and Ethics_ (2019).
  * Nayak (2021) Aditya Nayak. 2021\.  Boredom, Time-Perception and Algorithmic Governmentality.  _Journal of AI Humanities_ 7 (2021).  https://doi.org/10.46397/JAIH.7.1
  * Pushkarna et al. (2022) Mahima Pushkarna, Andrew Zaldivar, and Oddur Kjartansson. 2022\.  Data cards: Purposeful and transparent dataset documentation for responsible ai. In _Proceedings of the 2022 ACM Conference on Fairness, Accountability, and Transparency_. 1776–1826.  https://doi.org/10.1145/3531146.3533231
  * Raji et al. (2020) Inioluwa Deborah Raji, Andrew Smart, Rebecca N White, Margaret Mitchell, Timnit Gebru, Ben Hutchinson, Jamila Smith-Loud, Daniel Theron, and Parker Barnes. 2020\.  Closing the AI accountability gap: Defining an end-to-end framework for internal algorithmic auditing. In _Proceedings of the 2020 conference on fairness, accountability, and transparency_. 33–44.  https://doi.org/10.1145/3351095.3372873
  * Rouvroy et al. (2013) Antoinette Rouvroy, Thomas Berns, and Liz Carey-Libbrecht. 2013\.  Algorithmic governmentality and prospects of emancipation.  _Réseaux_ 177, 1 (2013), 163–196.
  * Sambasivan et al. (2021) Nithya Sambasivan, Shivani Kapania, Hannah Highfill, Diana Akrong, Praveen Paritosh, and Lora M Aroyo. 2021\.  “Everyone wants to do the model work, not the data work”: Data Cascades in High-Stakes AI. In _proceedings of the 2021 CHI Conference on Human Factors in Computing Systems_. 1–15.  https://doi.org/10.1145/3411764.3445518
  * Schwartz (2020) Barry Schwartz. 2020\.  Why efficiency is dangerous and slowing down makes life better.  https://psyche.co/ideas/why-efficiency-is-dangerous-and-slowing-down-makes-life-better.
  * Sharma et al. (2023) Vishal Sharma, Neha Kumar, and Bonnie Nardi. 2023\.  Post-growth Human–Computer Interaction.  _ACM Transactions on Computer-Human Interaction_ 31, 1 (2023), 1–37.  https://doi.org/10.1145/3624981
  * Suchman (1995) Lucy Suchman. 1995\.  Making work visible.  _Commun. ACM_ 38, 9 (1995), 56–64.  https://doi.org/10.1145/223248.223263
  * Suchman (2007) Lucy Suchman. 2007\.  _Human-machine reconfigurations: Plans and situated actions_.  Cambridge University Press.
  * Taplin (2017) Jonathan Taplin. 2017\.  _Move Fast and Break Things_.  Little, Brown and Company.
  * Teli et al. (2020) Maurizio Teli, Marcus Foth, Mariacristina Sciannamblo, Irina Anastasiu, and Peter Lyle. 2020\.  Tales of institutioning and commoning: participatory design processes with a strategic and tactical perspective. In _Proceedings of the 16th Participatory Design Conference 2020-Participation (s) Otherwise-Volume 1_. 159–171.  https://doi.org/10.1145/3385010.3385020
  * Thorbecke (2024) Catherine Thorbecke. 2024\.  The tech sector is pouring billions of dollars into AI. But it keeps laying off humans.  https://www.cnn.com/2024/01/13/tech/tech-layoffs-ai-investment/index.html.
  * Wang et al. (2022) Ding Wang, Shantanu Prabhat, and Nithya Sambasivan. 2022\.  Whose AI Dream? In search of the aspiration in data annotation.. In _Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems_. 1–16.  https://doi.org/10.1145/3491102.3502121
  * Whittaker (2021) Meredith Whittaker. 2021\.  The steep cost of capture.  _Interactions_ 28, 6 (2021), 50–55.  https://doi.org/10.1145/3488666
  * Williams et al. (2022) Adrienne Williams, Milagros Miceli, and Timnit Gebru. 2022\.  The exploited labor behind artificial intelligence.  _Noema Magazine_ 13\.  https://www.noemamag.com/the-exploited-labor-behind-artificial-intelligence/