# Voluminous yet Vacuous?
Semantic Capital in an Age of Large Language Models

Luca Nannini
<EMAIL_ADDRESS>

###### Abstract

Large Language Models (LLMs) have emerged as transformative forces in the
realm of natural language processing, wielding the power to generate human-
like text. However, despite their potential for content creation, they carry
the risk of eroding our Semantic Capital (SC) - the collective knowledge
within our digital ecosystem - thereby posing diverse social epistemic
challenges. This paper explores the evolution, capabilities, and limitations
of these models, while highlighting ethical concerns they raise. The study
contribution is two-fold: first, it is acknowledged that, withstanding the
challenges of tracking and controlling LLM impacts, it is necessary to
reconsider our interaction with these AI technologies and the narratives that
form public perception of them. It is argued that before achieving this goal,
it is essential to confront a potential deontological tipping point in an
increasing AI-driven infosphere. This goes beyond just adhering to AI ethical
norms or regulations and requires understanding the spectrum of social
epistemic risks LLMs might bring to our collective SC. Secondly, building on
Luciano Floridi’s taxonomy for SC risks, those are mapped within the
functionality and constraints of LLMs. By this outlook, we aim to protect and
enrich our SC while fostering a collaborative environment between humans and
AI that augments human intelligence rather than replacing it.

##  1 Introduction

The fable of Funes the Memorious, conceived by Jorge Luis Borges, serves as a
powerful metaphor for the era we live in. Funes, the character blessed—or
rather, cursed—with perfect memory, found himself submerged in an ocean of
unfiltered details. He was a prisoner of his own capacity, drowning in his
universe of relentless particulars. The individual who once boasted the
greatest memory lost his ability to discern the important from the trivial,
transforming his mind into a "garbage heap" of excessive detail. As Borges
wrote, "To think is to forget differences, generalize, make abstractions. In
the teeming world of Funes, there were only details, almost immediate in their
presence" [1]. In an echoing resonance to Funes’ plight, our society now finds
itself amidst a surge of information generation and consumption with uncharted
challenges to our epistemic filters.

This era, marked by the infinite details of our rapidly expanding infosphere,
mirrors Funes’ predicament. In this context, the concept of Semantic Capital
(SC), coined by Luciano Floridi, gains paramount importance. SC encapsulates
the collective information resources—knowledge, skills, or competencies—that
individuals or entities possess. These resources can be harnessed to create
value within our interconnected global information ecosystem [2]. This
construct actively shapes the infosphere, catalyzing communication, fostering
innovation, and driving informed decision-making [3]. As the realms of human
cognition and artificial intelligence (AI) increasingly coalesce, their
intersection is redefining the landscapes of collaboration, decision-making,
and knowledge creation. In these emerging dynamics, the role of SC escalates
in importance and complexity. The collaborative environments entailing human
and AI integration go beyond mere task execution; they embody intricate
interactions that should be dependent on mutual beneficial augmentation, and
not replacement or mimicry [4, 5]. Nonetheless, this surge in information,
fueled in part by AI, has the potential to generate a cascade of cognitive and
sociotechnical risks e.g., cognitive overload, misinformation, social
polarization, and erosion of public trust. As we argue in this paper, we are
possibly approaching a deontological ’tipping point’—an inflection where our
moral obligation to promote open dissemination of AI information might
conflict with our duty to prevent harm. Indeed, the relentless acceleration
and proliferation of AI information might soon manifest their most detrimental
and repressive effects [6, 7, 8, 9].

This paper endeavors to delve into the role of SC within the sphere of human-
AI interaction. We depart by defining its value to foster societal knowledge
and trust within the context of the information ethics challenges that we
currently face. We pay particular attention to generative AI systems,
particularly Large Language Models (LLMs). By contextualizing the debate
happening over their capabilities and limitation, we move to address the
broader range of ethical and deontological implications of LLMs. Central to
this endeavor is a necessary reframing of AI narratives, with a mindful
consideration of who benefits from these narratives and how they shape public
perception. In an era where our infosphere is populated with increasingly
accessible AI-generated content, a critical reassessment of our relationship
with open-source practices is paramount. By reflecting on the value of open-
source models and regulations, we endorse governance practices to ensure they
align with our ethical obligations and societal values. But before achieving
that, we exhort to consider a deontological tipping point in our AI-driven
infosphere. This approach entails moving beyond calls to adhere to AI ethical
guidelines or regulations and conceiving a range of social epistemic risks
that LLMs pose to our collective SC. Following Floridi’s taxonomy for SC risk,
our main contribution lies in mapping them within the capabilities and
limitations of LLMs. By doing so, we aim to bring a novel outlook to the LLM
discussion while encouraging innovative strategies to reinforce our epistemic
defenses. Our discourse seeks to guide us toward an equitable and sustainable
infosphere, where innovation flourishes without compromising societal values
and individual well-being.

##  2 Appraising Semantic Capital

Our exploration of the crucial role of SC in human-AI collaboration begins
with Luciano Floridi’s philosophy of information. Floridi’s seminal work,
birthed from the metamorphosis of the information age, places information at
the core of our world understanding [10]. The infosphere, Floridi proposes, is
an immersive information environment housing all informational
entities—humans, artificial agents, and other organisms [10]. In this sphere,
constant streams and exchanges of information form a complex interaction
network, shaping our reality perception and directing our actions. Within this
infosphere nests SC—value derived from meaningful information. It transcends
mere data accumulation, presenting as well-formed, meaningful data that
bolsters one’s power to create meaning—semanticise. An individual’s, group’s,
or society’s SC stock, demonstrated in various forms like knowledge
repositories, skills, shared societal norms, cultural narratives, etc., is
employed and invested in information creation, understanding, and
dissemination. This process fuels essential life aspects like communication,
decision-making, learning, problem-solving, and others. SC’s value is
intrinsically linked to its ability to enrich our understanding, navigation,
and shaping of our realities. As such, managing and curating SC is vital in
our increasingly information-dense society. The risks associated with SC— (a)
loss, (b) unproductive, (c) underuse, or (d) misuse or or (e) depreciation due
to truth erosion — are defined by Floridi as "the potential of loss of part or
all of the value of some content that can no longer enhance someone’s power to
semanticise something". [10, 3].

The digital technology era has brought forth new SC dimensions. Data abundance
and computational power have created pathways for enhancing and expanding our
SC. AI and other digital technologies facilitate SC management and curation,
aiding its effective and efficient usage and enrichment. If our world
understanding is based on relationships between informational entities, and
not just their intrinsic properties [11], then these technologies give rise to
new SC forms that significantly impact our semanticising processes and,
ultimately, shape our identities and realities111SC can be differentiated from
related concepts like ’intellectual capital’ and ’cultural capital’. While SC
focuses on knowledge, skills, and resources used for communication and
comprehension [3], intellectual capital pertains to an organization’s sum of
knowledge and skills that provide a competitive edge [12, 13]. Cultural
capital, however, refers to cultural resources like education and norms that
influence individual behavior and societal opportunities [14].. Why, then, is
it essential to highlight these concepts? Their role in shaping human-AI
collaboration is central. SC provides a crucial lens through which we
understand, navigate, and shape the evolving landscape of human-AI interaction
in the generative AI era.

##  3 Development of LLMs and the Debate Over Language "Understanding"

In the compelling narrative of natural language processing (NLP), we’ve borne
witness to a series of remarkable advancements over the past decade, with
Large Language Models (LLMs) and other AI generative systems claiming center
stage [15]. Commencing with the invention of Long Short-Term Memory (LSTM)
networks in 1997 [16], the journey has led us to the present-day marvels of
AI, such as GPT-4 [17]. These developments have profound implications for SC,
raising pressing questions about the deontology of knowledge and information
resources within the infosphere.

A brief historical overview of NLP highlights the rapid progress and
increasing complexity of these models. From LSTM to Word2Vec [18], from
Sequence-to-Sequence models [19] to the transformative attention mechanism
[20], and ultimately to the groundbreaking Transformer architecture [21] and
subsequent birth of BERT [22], each evolution has refined the capacity to
process and generate text, thereby influencing the constitution and use of SC.
The ’philosophical’ foundations of these NLP applications, especially for
Word2Vec, relied on concepts of Distributional Semantics [23, 24], paired with
the untapped benefits and dangers of "Big Data" to mirror a presumptive
realistic image of textual knowledge gathered from online repositories and
communities [25, 26, 27]. Against such shallow reflection, scholars addressed
concerns related to biases of this knowledge available online or within any
other databases with spurious, impartial, or unguarded data entries [28, 29,
30, 31]. This raised challenges for these models, such as primarily avoid to
display semantically incomplete or nonfactual information222the so-called
”hallucinations” [32] in Natural Language Generation (NLG).. The advent of
LLMs such as OpenAI’s GPTs, and their deployment in various applications,
represent the contemporary zenith of this technological trajectory [33].
Nevertheless, the rapid proliferation of these models has sparked a lively
debate among researchers and scholars concerning their true capabilities and
implications.

A crucial question raised in this debate is whether LLMs genuinely understand
the information they process, or if they are mere "stochastic parrots," as
posited by AI researchers Emily Bender, Timnit Gebru, Angelina McMillan, and
(under an alias) Margaret Mitchell [15]. The paper offered a continuation of a
critical inquiry toward their natural language understanding, as previously
expressed in 2020 by Bender [34]. They argued that these models, despite their
seemingly human-like text generation abilities, merely mimic patterns without
comprehending the underlying meaning, potentially leading to the dilution of
SC shuffling human knowledge in a convincing manner. Foremost, their concerns
were grounded around the biases embedded in the training data, the substantial
environmental footprint of training such language models, and the
concentration of power in a few tech giants controlling them. In echoing these
concerns, Melanie Mitchell highlighted in December 2022 the limitations of
LLMs in truly "understanding" the world and their reliance on superficial
patterns in the data [35].

Yet, it needs to be recognized how LLMs are powerful tools that generate
human-like narratives: their underlying architecture and scalability allow
them to manipulate and operate inferences over the external world
representations. But such abilities are generally hard to forecast, as well as
to handle and interpret, by their designers. The so-called "emergent
abilities," which become more evident as the scale of the models increases,
refer to the unforeseen and unplanned behavior that LLMs display, which often
defy easy understanding or control by the developers themselves [36]. Such
abilities can result in outputs that are surprisingly insightful or
disturbingly off-mark, underscoring the unpredictability and potential risks
of deploying LLMs in real-world contexts [37, 38, 39, 40]. This challenge
intensifies when we consider the increasing number of studies being released
for their application in practical scenarios to assist various human tasks
[41, 42].

This translates to the fact that despite the property to handle a certain
degree of semantic information [38] to produce coherent textual information,
LLMs cannot be universally trusted as epistemic agents capable to handle
pragmatic constraints of human communication. The reason for this lies in
their architectures per sé, but also within potential Eliza-effect [35], e.g.
how the user linguistically frames their prompts based on their intention and
competencies [43]. This entails that the presumptive factuality of these model
outputs needs then to be compared against their stochastic nature, heavily
influenced by the design [44, 39] and also the interaction [45] of the users
with the prompts fed. Despite growing efforts in providing additional
heuristic bases to downplay unpredictable behavior, such as with chain-of-
thought, constitutional AI or red-teaming [36, 46, 47], a crucial question
stands with the reliability and factuality of LLMs: can we equate the
performances of LLMs with human understanding and knowledge? Recognizing the
differences, the academic community is reevaluating how to benchmark these
models’ performance. This calls for more critical assessment measures that
better reflect the nature and capabilities of LLMs, especially in terms of
interpretability and predictability [48, 49, 44, 31].

##  4 Fear Sells Well? On Ethical and Deontological Implications of LLMs

The debate over LLMs’ abilities underscores the complex implications of AI
generative systems for SC. Indeed, it comes as no surprise how LLMs, by their
design and capabilities, can profoundly influence the infosphere landscape:
they operate as powerful amplifiers and conduits of information, capable of
synthesizing and generating vast amounts of text that are, in many instances,
indistinguishable from human-written content.

Reasoning about their potential benefits, LLMs can democratize access to
information by breaking down barriers to user understanding e.g.,
paraphrasing, summarizing, or translating text into different languages. By
making information more accessible and interpretable, these models can enhance
the inclusivity and utility of SC. Secondly, LLMs can also contribute to the
expansion of SC by facilitating the creation of new content. Authors can use
these tools to overcome writer’s block, generate creative ideas, or automate
routine writing tasks. In academic and professional settings, LLMs can help to
compile emails, draft reports, write code, or even create poetry and prose,
thereby enriching the diversity and volume of SC. Such positive scenarios must
be counterbalanced with a sober recognition of the potential costs and risks
that these models pose to SC. Among others, the risks associated with LLMs
extend beyond semantic information handling, touching upon socio-economic,
political, and ethical domains, encompassing bias propagation, labor market
disruptions, power centralization, misinformation campaigns, cyber threats,
intellectual property issues, and unforeseen harmful uses [50].

At the core, LLMs can potentially spawn a proliferation of information a-like
content, increasingly blurring the line with factual information. This
proliferation risks diluting the quality of SC, contributing to an infosphere
that is voluminous yet vacuous333Not only related to textual abilities, but so
far public opinion was surprised by the dissemination of hyperrealist
portraits of public personas made through generative AI tools, e.g. Pope
Francis [TheNYT2023a] wearing fashion coats or Donald Trump getting arrested
[Forbes2023] getting arrested. Afterward, part of the public was enraged to
see how a professional photographer, Boris Eldagsen, could even win an
international award with an AI-produced image [TheGuardian2023a], or, even
worse, famous painters such as Edward Hopper being displayed in Google’s
search engine alongside AI-generated imitations of their works
[Futurism2023].. Alongside these concerns, the susceptibility of LLMs to the
propagation of false information, as explored by Bian et al., adds another
layer of complexity to the debate [51]. Their study claimed how false
information tends to spread and contaminate related memories in LLMs via a
semantic diffusion process. Also, these models might be subject to authority
bias, often accepting false information presented in a more trustworthy style
such as news or research papers. On this line, if LLMs are easily perturbable
given prompt and information sources provided, they might be deployed at scale
to scuffle or crowd out minor or dissenting public voices444On this note, a
debate should be held on how appropriate is to deploy generative AI to
represent social distress and identities, such as public manifestations
[TheGuardian2023b, Twitter2023], or companies recoursing to generative AI
tools claiming to promote ”diversity” through fake fashion models advertising
[TheIndependent2023], while in reality displaying a subtle operation of
ethics-washing - failing to hire and remunerate underrepresented individuals
while still leveraging their image at no costs..

Within these considerations, we should now approach the paper from Bender et
al. [15] as a starting point of a wider debate, encompassing not only
capabilities of LLMs, but rather the governance implication and social
communities impacted, ultimately pertaining to the value of our shared SC
[52]. Put in simple terms, their discourse shall be not considered merely a
matter of academic disquisition over semantic handling of human language, but
rather a pointed attempt to scrutinize how these generative tools are
associated to a narrative about AI that serves those who possess the means and
resources to develop and capitalize economic value and competitive advantage -
not only directly from such models, but also around them [12]. Through this
lens, it can be discerned two key interpretive perspectives in this debate.
The first, immediate perspective mesmerizes the public by proclaiming these
LLMs as "sparks of Artificial General Intelligence (AGI)," [53], implying that
these models display initial prototypes of human-a-like cognitive
intelligence555Yet, the research from Bubeck et al. is released [as for May
2023] without peer-review by a team of Microsoft and OpenAI’s researchers,
using foremost controversial definitions of human intelligence as a comparison
[TheNYT2023b]. Related to the AGI narrative, Giada Pistilli, main ethicist of
HuggingFace and contributor of the LLM BLOOM [54], claimed in May 2022 to not
engage herself to speak any more of AGI in a fortunate Twitter thread
[Twitter2022].This is because the framing of that public debate was proved
only detrimental to the real harms of LLMs, cautioning an in-depth analysis of
the issue in a research study published the same month [55]. This position
resonates with an increasing number of scholars being cautious to adopt or
even engage in using these terms in the public discourse; similarly - as also
for the current paper - concerns over unnecessary anthropomorphisms [56]
within LLMs are now being raised while deploying terms pertaining to human
cognition, such as ”hallucination”, to address nonfactual information provided
by LLMs [TheGuardian2023c]. In this perspective, some work is finally moving
towards making explicit design choices to prevent anthropomorphism for
conversational systems [57].. Such a view captivates public imagination and
fuels, at best, a techno-optimistic narrative, while at worst, technological
determinism, having public opinion feel humanity as doomed by the advent of
some unavoidable and imminent superior AI [52]. The second perspective,
however, is way more sobering and less sensationalist, unpacking a far more
structural and intricate argument concerning the ecology of AI development,
commercialization, and the possession of SC in the form of know-how for
gathering and maintaining increasingly sophisticated data and AI models [7].
When the conversation revolves solely around the inherent risks in the models,
it inadvertently diminishes the role of their developers. As Bender et al.
resonated, their research served as a warning bell, cautioning against a
development trajectory of AI solutions promising extraordinary capabilities
without due scrutiny [15, 58].

The core issue resides in the polarization of a debate where, on one hand, one
faction predominantly comprises stakeholders - such as proprietaries of AI
solutions - might derive benefits from gauging public attention over these
models. Their strategic maneuvers, despite genuine fears over downsides of
their products, might also be geared towards maintaining the undivided
attention of the global audience, intending to foster an environment conducive
to the promotion and consumption of their AI-based creations. Concurrently,
another group emerges posing stark opposition by unearthing the contentious
aspects of such models. This group, yet widely heterogeneous, contends that
these AI solutions are not inherently superior or advantageous, and instead,
might cause more harm than good due to their pronounced socio-technical
ramifications and the plausible monopoly [9] they can create in the AI
innovation landscape 666 Interestingly, the fervor and dynamism of this debate
have garnered widespread attention. With the current momentum, an increasing
number of scholars and civil rights associations are echoing the apprehensions
about the potential LLMs can inflict, taking actions such as open letters to
regulate LLMs. Of this group, a segment of the public is lending credence to
the ”longtermism” outlook—holding onto the belief that AI might be a blessing
for all humanity in the future, only if it is perceived as an existential
threat today [Forbes2022],[Bloomberg2023]. This viewpoint, however, does not
advocate for immediate and tangible action against present structural issues,
such as the exploitation of underrepresented communities involved in
annotating and moderating LLMs. In response to these systemic issues, the
affected communities have begun showcasing innovative grassroots initiatives.
Karen Hao’s investigation into AI colonialism [MIT, TechRev2022] and the
protests staged by African AI workers to unionize in Nairobi illuminate these
ongoing efforts [Time2023]. Meanwhile, it is noteworthy that AI pioneers, like
Geoffrey Hinton, have been vocal about the necessity for increased regulations
but have not explicitly extended support to these communities or other
concerned academics, such as Bender, Gebru, and Mitchell [TheGuardian2022].
Similarly, owners of AI technologies, like OpenAI’s CEO Samuel Altman, have
sought regulatory measures before the US Senate [TheNYT2023], while other
industry leaders, such as Microsoft Chief Economist Michael Schwarz
[ArsTechnica2023] and former Google CEO Eric Schmidt [Fortune2023], have
either invited caution over the perceived risks of generative AI until
incidents of ”meaningful harm” occur or advocated for self-regulation in the
industry while criticizing governments for their alleged lack of expertise to
regulate technology effectively. The narrative spun by these AI proprietors
oscillates between demanding no regulation and advocating for a different
regulation. Such a seemingly contradictory stance might be interpreted as a
strategic maneuver to hold investor attention captive while cleverly
deflecting competitive threats in the AI arena [Insider2023]. . Indeed, the
year 2023 witnessed an unprecedented surge in the release of LLMs applications
to the public by large corporations. These developments were characterized by
increasingly shortened time-to-market durations, intensifying the potential
risks and implications of these systems777The rush to launch these
applications often eclipsed necessary precautions, resulting in technology
releases without sufficient safeguards. This haste raises concerns about
corporate decision-making and leaves the public exposed to unanticipated AI-
related risks, such as LLMs chat-bots harassing or recommending users to self-
harm or indulge minors into socially irresponsible behaviors [Insider2023b,
WashinghtonPost2023, Time2023]. . This speed, while demonstrating their
technological capabilities, also exposed gaps in their ethical governance.
Despite their "demo" status, instances of these LLMs causing harm or
harassment to users highlighted the need for careful deployment strategies and
comprehensive product testing and feedback, as well as structural inquiry over
the influence exerted over the AI development agenda by proprietary solutions.

Such unforeseen detrimental consequences serve as stark reminders of the need
to couple AI development with comprehensive evaluation processes that
prioritize societal well-being over speed and profit. Navigating this debate,
one must remain cognizant of the intricate dynamics at play and question who
ultimately benefits from these narratives. This to ensure that the discourse
around AI and its impact on our collective SC remains grounded in empirical
realities and is sensitive to the broader socio-economic implications.

##  5 Open Source and Regulation for LLMs

Let’s momentarily pause and look beyond the current maelstrom of the ongoing
debate on LLMs. Taking a step back, we find ourselves in the birth of the
internet era, deeply influenced by the late 20th century’s internet
narratives. This was a time ripe with the promise of an information revolution
[59]. The birth of the open-source paradigm during this time served as a
catalyst to this revolution. By providing a universal platform accessible to
anyone with an internet connection, it was an embodiment of the democratic
ethos of these emerging digital utopias. The open-source movement, anchored in
collaboration, transparency, and accessibility, has spurred an incredible
acceleration in technological evolution [60]. This movement’s transformative
impact is especially palpable in the AI field, cultivating a fertile ecosystem
ripe for progress and innovation. Emerging in this backdrop, LLMs owe much of
their rapid development to open-source AI frameworks like TensorFlow and
PyTorch as well as the Transformer architecture [61, 62, 21]. Such open-source
tools have made it feasible for researchers, developers, and organizations
across the globe to access, modify, and contribute to a shared body of
knowledge and codebase. This democratization of AI technologies, however, is a
double-edged sword; while it empowers innovation and progress, it
simultaneously amplifies challenges related to misuse, ethical implications,
and regulatory requirements. The diffusion of generative AI technologies, such
as LLMs, via open-source platforms, accentuates the dual-use risk. LLMs can be
applied for both beneficial and harmful purposes. Still cognizant of their
risks, once an AI model is made openly available, specularly becomes harder to
track, contain, or retract, given the scale, speed, and accessibility
facilitated by open-source platforms. If instead an LLM is proprietary, such
as GPT-4 [17], being undisclosed to the public, then risks might arise in not
being able to reprove its design phase and data provenance, as well as
oversight its deployment.

From this, it comes as no surprise that regulating generative AI technologies
is a formidable challenge. The pace at which AI evolves is often unmatched by
the rate at which traditional regulatory frameworks adapt888A lively example
of this challenge can be found in the EU commissions efforts back in April
2023 to make amendments targeting generative AI, ahead of final parliamentary
votation on May 11th with the EU AI Act draft [EuroparlPress2023,
Euractiv2023].. Crafting effective regulations requires a delicate balancing
act: on one side, for disclosed models, it entails to manage the risks of
misuse while preserving the democratic ethos of open-source, without stifling
innovation; on the other, for proprietary models, it entails preserving
marketing advantages while still allowing impart auditing measures to reprove
model compliance and benevolence within regulatory standards as well as
societal values. One potential pathway forward involves revisiting our
relationship with open-source practices in the context of LLMs. This
rethinking requires a comprehensive, integrative approach that respects the
principles of open-source while recognizing and addressing the risks posed by
AI technologies. Strategies could include more accountable deployers’
practices, having them bear a greater responsibility for their creations, and
revised legal frameworks that adapt to the specific challenges of LLMs. In
terms of soft-power, this could be complemented by industry-wide
certifications and licensing999Within license, a leading example is RAILS. The
BigScience project, an open collaborative initiative, introduces a Responsible
AI License (RAIL) for the usage of their LLMs to balance accessibility and
risk mitigation. It reflects a community-led approach to restrict potential
LLM harms, such also concerns about their societal and environmental impacts
[BigScience-RAILS2023]. to enhance accountability over the design and
development of those AI systems. In terms of hard-power, instead, AI
governance measures should attain from clear legislative guardrails, such as
regulatory sandboxes, risk assessments, and auditing practicing encompassing
the development and deployment of LLMs. Within this scope, the current major
regulatory effort in the global landscape is now being lead by the European
Union (EU), yet not being exempted from potential legislative weaknesses that
might not always efficiently mitigate LLMs risks101010In particular, the
current amendment draft of the EU AI Act voted on May 2023 introduced
definition and provisions targeting LLMs, intended as foundation models [63].
At the current stage of draft, Art.28b(4), although partially beneficial with
its transparency obligations, is criticized for its lack of duties imposed on
online AI content generators, necessary for curbing misinformation. Yet, the
Act is not yet enforced, and will likely have to interplay, within the EU
regulatory ecosystem, with other regulations being discussed or already
enacted. For an in-depth overview of these legislative implications, also
outstanding the EU ground, refer to the working paper of [64]..

##  6 The Deontological Tipping Point: Navigating the Information Surge

Yet, calling for ethical virtuosism and regulations might not be enough to
shelter us our epistemic filters in this unprecedented storm of AI-generated
information. While this surge of information has democratized access to
knowledge and fueled progress in myriad fields, it also has the potential to
create a state of social epistemic bewilderment. It is against this backdrop
that it can be argued that we have reached a deontological tipping point—an
inflection where the relentless acceleration and proliferation of information
culminate in the epistemic condition to scale up its detrimental effects. The
concept of a deontological tipping point suggested is constituted by a
juncture where our moral obligation to assist to the open dissemination of
certain AI narratives and solutions may come into conflict with our duty to
prevent harm. Within the context of AI, and particularly in relation to LLMs,
this tipping point is precipitated by the realization that unfettered access
to information and open-source practices, while fostering innovation, can also
amplify risks given how scalable and accessible these models are,
independently of liability of major AI proprietors or individual developers
and deployers.

This democratization and explosion of information blur the lines between
reality and artificial constructs, in echoing Baudrillard’s notion of
"hyperreality". The hyperreality conceived by Baudrillard—an environment where
simulacra blur the boundaries between real and artificial, and virtual
identities deontologically supersede their real references —becomes an eerily
accurate premonition of a possible AI-saturated infosphere [65]. As AI-
generated content swells, we confront the dual challenge of strenghtening our
cognitive ecology to preserve our SC, whilst upholding the open-source
principles that have traditionally sparked innovation [66]. Despite being
awash with information, we are precariously perched on the edge of what James
Bridle refers to as a "New Dark Age," a paradox where information in our
current technological ecosystem obscures knowledge instead of revealing it
[8]. We must navigate this deontological tipping point, resisting unchallenged
acceptance of an AI-driven information ecosystem.

The challenge lies in recognizing and navigating this deontological tipping
point: this aligns with Floridi’s information ethics framework, which
underscores the moral implications of creating, managing, and utilizing
information. As remarked before, Floridi stresses how that the quality of our
infosphere, or the environment in which information is created, shared, and
consumed, profoundly impacts our lives and our moral decisions [2, 11]. To
navigate this new complex infosphere, we must engage with a multi-faceted
strategy. First, it necessitates moving forward from merely calling AI systems
to adhere to ethical guidelines or exhorting to establish a culture of
accountability, transparency, and shared responsibility when AI proprietors
are able to influence AI agenda and public opinion [9]. This shift in approach
should involve a critical reexamination of why, within our current
informational ecology, certain narratives are dominant and universally
accepted, and who benefits from this status quo. Such societal introspection
might prompt a critical reconsideration of the merits of confining the AI
debate and our notion of innovation to a single range of solutions.
Furthermore, we argue that, while public online information sources have
proven to be fertile ground for the proliferation of AI technologies, today
the wealth of SC at stake might be threatened by a range of epistemic risks
that we outline using Floridi’s taxonomy [3]:

  * •

Loss of SC: This occurs when there is an oversimplification of complex
semantic ideas or when an LLM relies on biased or erroneous explanatory models
based on incomplete or distorted input data, resulting in flawed argumentation
[15, 51]. In this case, the value of the semantic content is reduced due to
the propagation of inaccurate or misleading information, akin to the spread of
propaganda, fake news, or "alternative facts" [50]. Protection against this
type of risk necessitates rigorous data curation (such as data provenance, and
lineage) and model validation protocols to ensure LLMs generate accurate and
reliable information.

  * •

Unproductiveness and Underuse: When LLMs are used to replicate semantic
content without adding value or facilitating a deeper understanding, it can
lead to the stagnation of SC. This can happen when users rely too heavily on
LLMs for information generation and consumption while neglecting to actively
participate in knowledge sharing and debate. Also, at the core, this underuse
of SC might stems from the LLMs’ architecture, being able to fetch only data
that might be available in accessible online repositories, without yet
considering the ’long-tail’ of secondary, related contributions, as well as
different perspectives, on a given topic. To guard against this risk, it’s
essential first to inquire over the role of LLMs as epistemic agents, as well
as to foster a culture of critical thinking and active engagement in the
discourse, preventing the ’mummification’ of SC [66].

  * •

Misuse: LLMs, if not properly calibrated or deployed by malicious actors, can
generate content that disrespects, misunderstands, or illegitimately
appropriates information [36, 51]. This misuse, or information expropriation,
can lead to the loss of SC. Mitigating this risk requires careful design and
tracking over their deployment, with due respect for cultural nuances and
contexts. In terms of data, this might be possible also leveraging
underrepresented communities to not just moderate, but actively participate in
data annotation policies, to mitigate potential biases [31]. In terms of
models, intellectual property, trademarks, and measure to ensure
accountability shall be established to track responsibles within the
development and deployment of generative solutions, also enforced by hard
laws, such as the forthcoming EU AI Act or the Liability Directive [67, 63].

  * •

Depreciation: The value of SC can depreciate over time, particularly when new
LLM-generated information floods the infosphere and obscures or distorts
earlier knowledge. Future LLMs models, being trained or fine-tuned in such a
stagnating environment, might see an increase in diminished returns over their
performance. This could happen by being fed data that are either synthetically
produced or, even worse, being produced by a shrunken online community of
users that lacks incentives to share and engage in knowledge creation and
maintenance given the information accessibility of LLMs. Also connected to
underuse, the concept of Model Dementia has been recently coined [68] to
signal how future LLMs training datasets might lead to diminished returns in
terms of content richness, intended as forgetting underlying data
distributions.

Building on this assumption, our collective reliance on language models as
repositories of information might entail a shift in our ethical
responsibilities, as we transfer the locus of our communal knowledge from the
outward sphere of human discourse to the inward representations within these
models. This shift of direction needs also to be put in context, two
additional factors play a key role, being inversely proportionals among them:
availability of information and attention. With the sheer amount of data being
produced by LLMs, we might approach new states of information magnitude. This
overabundance of information is overshadowing and possibly distorting pre-
existing knowledge, causing the depreciation of SC. It’s becoming
progressively more demanding to discern useful information or valuable
knowledge in the face of this onslaught, which in turn undermines the value
derived from it. In this new era defined by the Attention Economy [69], where
human attention is a scarce and coveted resource, the pressure on LLMs to be
deployed within work or educational tasks, outreach various audiences, and
produce engaging content can inadvertently contribute to this range of risks.
As these models strive to produce information that appears coherent and well-
expounded - such as also sensationalist AI-generated images or news of public
personas, sociopolitical facts etc - the focus might shift from providing
comprehensive and nuanced insights to offering quick, often shallow pieces of
information. This shift could potentially "flatten" the richness of discourse,
leading to apparently more engaging, yet less insightful information being
circulated.

At the core of this acceleration, the parameter of epistemic filters becomes
paramount. These are mechanisms that people use to sort and interpret the
information they encounter. They help us decide what counts as evidence for
forming a belief or what challenges it enough to lead to belief revision.
There are different kinds of filters, among which the two most important ones
are filters for omission and discredit [70]. Filters for Omission allow
individuals or groups to ignore or reject information that does not align with
their current beliefs or values. Filters for Discredit, instead, lead
individuals or groups to dismiss or discredit opposing viewpoints or evidence.
This can involve casting doubt on the source of the information, its
credibility, or its relevance. Discredit filters are particularly active in
polarized debates where individuals or groups have strong beliefs that they
feel are being threatened. Social media platforms, through their algorithmic
selection of content, might inadvertently strengthen that. Thus, from there,
it can be stated that when encountered with new information, we can actively
engage in accepting or rejecting that, yet, a role in such a selection is
performed by social aspects of communication. In this regard, the concept of
Epistemic Fitness refers to the effectiveness of an individual’s or group’s
ability to process evidence and revise their beliefs accordingly. It involves
the ability to gather, evaluate, and use information to form accurate beliefs
about the world. Maximizing epistemic fitness consists in enhancing one’s
epistemic filters to improve the quality of information intake and the
efficiency of belief formation and revision. Through this lens, we can now
reevaluate how the AI narratives [71] over capabilities of generative AI are
spread by communities with certain interests and beliefs: while some might
foresee economic revenues from instilling certain narratives, others might
adopt a stance geared toward protecting human rights, remarking the nuances
and challenges of human language among others. From there, we yet have to
tackle how to deal with future conversations where LLMs could be deployed to
reinforce existing viewpoints, possibly underpinning the deployment of these
filters if online users will be led to believe that information spread by LLMs
is actually factual and representative of an allegedly major group of people
than it is in reality.

The call to action is thus twofold. On one hand, consumers of AI-generated
content need to refine their individual epistemic filters to navigate this new
information landscape effectively. This might entail questioning why certain
narratives are spread and validated, and for which purposes. On the other
hand, developers and proprietors of LLM solutions have an ethical
responsibility to design systems that support, rather than undermine, the
collective epistemic fitness of society. Deployers, similarly, shall use these
tools cognizant of the value of public SC, being also subjected to watermarks,
licensing, and any other enforcement to reprove their own accountability.

Thus, to conclude, a cornerstone in our collective response to these risks is
the amplification of AI literacy initiatives. Creating an informed citizenry
that understands AI technologies, including their potential advantages and
associated risks, enables individuals to engage in meaningful discussions and
decision-making processes concerning their epistemic validity. Central to this
endeavor is the proactive integration of ethical considerations. Ethical
responsibility should not be a reactionary measure or an isolated response to
negative outcomes (e.g. regulate only when meaningful harm occurs). Instead,
it needs to be woven into the fabric of the AI design and deployment process.
Such proactive ethical responsibility can serve as a safeguard, aligning the
development and utilization of AI technologies, and disincentivizing
diminishing time-to-market agendas. However, this inquiry does not suggest a
departure from open-source practices. Rather, it signals the need for a
matured, conscientious version of open-source, devoid of narratives and
utopias of technological emancipation or determination. One that is sober,
cognizant of the social epistemic risks, and dedicated to enhancing public
comprehension of AI technologies.

##  7 Conclusion

This work attempts to evaluate the complex interplay between LLMs’ potential
for knowledge democratization and the sociotechnical challenges they present.
Amid the accelerating proliferation of LLMs in 2023, the widespread narrative
that frames them as precursors to AGI risks overshadowing important socio-
economic implications, potentially facilitating an AI monopoly. It is vital,
therefore, to question who benefits from these narratives and whether these
beneficiaries align with societal interests broadly. Despite acknowledging the
lively nature of this debate, we attempt to explore the delicate balance
between the democratization of knowledge and the emergence of a deontological
tipping point in our infosphere. This tipping point symbolizes a critical
juncture where our commitment to open information dissemination may intersect,
and potentially conflict, with our obligation to prevent harm. This dynamic
has been exacerbated by the cognitive deluge driven by AI technologies,
especially LLMs, leading to uncharted social epistemic challenges that stem
from their sociotechnical risks. We have highlighted that the unchecked
expansion and proliferation of AI-generated content such as textual
information from LLMs, while holding considerable promise, also pose
significant risks. Aside from the engaging debate over their properties to
handle semantic information (i.e., "understanding"), we shall not fail to
commit to a broader inquiry over the ecosystem that fuels attention towards
them, being cognizant of a different array of risks that ultimately affect the
value of our SC.

## References

  * [1] Jorge Luis Borges.  Funes, the Memorious, pages 306–312.  Duke University Press, New York, USA, 2002.
  * [2] Luciano Floridi.  Information: A Very Short Introduction.  New York: Oxford University Press, 2010.
  * [3] Luciano Floridi.  Semantic capital: its nature, value, and curation.  Springer Philosophy & Technology, 31:481–497, 2018.
  * [4] Erik Brynjolfsson.  The turing trap: The promise & peril of human-like artificial intelligence.  CoRR, abs/2201.04200, 2022.
  * [5] Shannon Vallor.  The AI mirror: Reclaiming our humanity in an age of machine thinking.  In Vincent Conitzer, John Tasioulas, Matthias Scheutz, Ryan Calo, Martina Mara, and Annette Zimmermann, editors, AIES ’22: AAAI/ACM Conference on AI, Ethics, and Society, Oxford, United Kingdom, May 19 - 21, 2021, page 6. ACM, 2022.
  * [6] Byung-Chul Han.  In the swarm: digital prospects, volume 3.  MIT press, 2017.
  * [7] Kate Crawford.  The atlas of AI: Power, politics, and the planetary costs of artificial intelligence.  Yale University Press, 2021.
  * [8] James Bridle.  New dark age: Technology and the end of the future.  Verso Books, 2018.
  * [9] Dan McQuillan.  Resisting AI: an anti-fascist approach to artificial intelligence.  Bristol University Press, 2022.
  * [10] Luciano Floridi.  The Philosophy of Information.  New York: Oxford University Press, 2011.
  * [11] Luciano Floridi.  The Routledge handbook of philosophy of information.  Routledge London, 2016.
  * [12] Thomas Stewart and Nicholas Brealey.  Intellectual capital: The new wealth of organizations.  Long Range Planning, 30(6):953, 1997.
  * [13] Steven Firer and S Mitchell Williams.  Intellectual capital and traditional measures of corporate performance.  Journal of intellectual capital, 4(3):348–360, 2003.
  * [14] Pierre Bourdieu.  The forms of capital.  In The sociology of economic life, pages 78–92. Routledge, 2018\.
  * [15] Emily M. Bender, Timnit Gebru, Angelina McMillan-Major, and Shmargaret Shmitchell.  On the dangers of stochastic parrots: Can language models be too big?  In Madeleine Clare Elish, William Isaac, and Richard S. Zemel, editors, FAccT ’21: 2021 ACM Conference on Fairness, Accountability, and Transparency, Virtual Event / Toronto, Canada, March 3-10, 2021, pages 610–623. ACM, 2021.
  * [16] Sepp Hochreiter and Jürgen Schmidhuber.  Long short-term memory.  Neural Comput., 9(8):1735–1780, 1997.
  * [17] OpenAI.  GPT-4 technical report.  CoRR, abs/2303.08774, 2023.
  * [18] Tomás Mikolov, Kai Chen, Greg Corrado, and Jeffrey Dean.  Efficient estimation of word representations in vector space.  In Yoshua Bengio and Yann LeCun, editors, 1st International Conference on Learning Representations, ICLR 2013, Scottsdale, Arizona, USA, May 2-4, 2013, Workshop Track Proceedings, 2013.
  * [19] Ilya Sutskever, Oriol Vinyals, and Quoc V. Le.  Sequence to sequence learning with neural networks.  In Zoubin Ghahramani, Max Welling, Corinna Cortes, Neil D. Lawrence, and Kilian Q. Weinberger, editors, Advances in Neural Information Processing Systems 27: Annual Conference on Neural Information Processing Systems 2014, December 8-13 2014, Montreal, Quebec, Canada, pages 3104–3112, 2014.
  * [20] Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio.  Neural machine translation by jointly learning to align and translate.  In Yoshua Bengio and Yann LeCun, editors, 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings, 2015.
  * [21] Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, and Illia Polosukhin.  Attention is all you need.  In Isabelle Guyon, Ulrike von Luxburg, Samy Bengio, Hanna M. Wallach, Rob Fergus, S. V. N. Vishwanathan, and Roman Garnett, editors, Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, December 4-9, 2017, Long Beach, CA, USA, pages 5998–6008, 2017.
  * [22] Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova.  BERT: pre-training of deep bidirectional transformers for language understanding.  CoRR, abs/1810.04805, 2018.
  * [23] John Firth.  A synopsis of linguistic theory, 1930-1955.  Studies in linguistic analysis, pages 10–32, 1957.
  * [24] Mikael Brunila and Jack LaViolette.  What company do words keep? revisiting the distributional semantics of J.R. firth & zellig harris.  In Marine Carpuat, Marie-Catherine de Marneffe, and Iván Vladimir Meza Ruíz, editors, Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL 2022, Seattle, WA, United States, July 10-15, 2022, pages 4403–4417. Association for Computational Linguistics, 2022.
  * [25] Viktor Mayer-Schönberger and Kenneth Cukier.  Big data: A revolution that will transform how we live, work, and think.  Houghton Mifflin Harcourt, 2013.
  * [26] Amir Gandomi and Murtaza Haider.  Beyond the hype: Big data concepts, methods, and analytics.  Int. J. Inf. Manag., 35(2):137–144, 2015.
  * [27] Matthew Zook, Solon Barocas, danah boyd, Kate Crawford, Emily Keller, Seeta Peña Gangadharan, Alyssa Goodman, Rachelle Hollander, Barbara König, Jacob Metcalf, Arvind Narayanan, Alondra Nelson, and Frank Pasquale.  Ten simple rules for responsible big data research.  PLoS Comput. Biol., 13(3), 2017.
  * [28] Kate boyd, danah Crawford.  Critical questions for big data.  Information, Communication & Society, 15(5):662–679, 2012.
  * [29] Cristian S Calude and Giuseppe Longo.  The deluge of spurious correlations in big data.  Foundations of science, 22:595–612, 2017.
  * [30] Brent Daniel Mittelstadt and Luciano Floridi.  The ethics of big data: Current and foreseeable issues in biomedical contexts.  The Ethics of Biomedical Big Data, pages 445–480, 2016.
  * [31] Roberto Navigli, Simone Conia, and Björn Ross.  Biases in large language models: Origins, inventory and discussion.  J. Data and Information Quality, may 2023.  Just Accepted.
  * [32] Ziwei Ji, Nayeon Lee, Rita Frieske, Tiezheng Yu, Dan Su, Yan Xu, Etsuko Ishii, Ye Jin Bang, Andrea Madotto, and Pascale Fung.  Survey of hallucination in natural language generation.  ACM Comput. Surv., 55(12), mar 2023.
  * [33] Alec Radford, Karthik Narasimhan, Tim Salimans, Ilya Sutskever, et al.  Improving language understanding by generative pre-training.  2018\.
  * [34] Emily M. Bender and Alexander Koller.  Climbing towards NLU: on meaning, form, and understanding in the age of data.  In Dan Jurafsky, Joyce Chai, Natalie Schluter, and Joel R. Tetreault, editors, Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, ACL 2020, Online, July 5-10, 2020, pages 5185–5198. Association for Computational Linguistics, 2020.
  * [35] Melanie Mitchell and David C. Krakauer.  The debate over understanding in ai’s large language models.  CoRR, abs/2210.13966, 2022.
  * [36] Samuel R. Bowman.  Eight things to know about large language models.  CoRR, abs/2304.00612, 2023.
  * [37] Tom B. Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, […] Sam McCandlish, Alec Radford, Ilya Sutskever, and Dario Amodei.  Language models are few-shot learners.  CoRR, abs/2005.14165, 2020.
  * [38] Belinda Z. Li, Maxwell Nye, and Jacob Andreas.  Implicit representations of meaning in neural language models.  In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pages 1813–1827, Online, August 2021. Association for Computational Linguistics.
  * [39] Rylan Schaeffer, Brando Miranda, and Sanmi Koyejo.  Are emergent abilities of large language models a mirage?, 2023.
  * [40] Antonio Valerio Miceli-Barone, Fazl Barez, Ioannis Konstas, and Shay B. Cohen.  The larger they are, the harder they fail: Language models do not recognize identifier swaps in python, 2023.
  * [41] Zihao Wang, Shaofei Cai, Anji Liu, Xiaojian Ma, and Yitao Liang.  Describe, explain, plan and select: Interactive planning with large language models enables open-world multi-task agents.  CoRR, abs/2302.01560, 2023.
  * [42] Yaqi Xie, Chen Yu, Tongyao Zhu, Jinbin Bai, Ze Gong, and Harold Soh.  Translating natural language to planning goals with large-language models.  CoRR, abs/2302.05128, 2023.
  * [43] Ethan Perez, Sam Ringer, Kamile Lukosiute, … Evan Hubinger, Nicholas Schiefer, and Jared Kaplan.  Discovering language model behaviors with model-written evaluations.  CoRR, abs/2212.09251, 2022.
  * [44] Miles Turpin, Julian Michael, Ethan Perez, and Samuel R. Bowman.  Language models don’t always say what they think: Unfaithful explanations in chain-of-thought prompting, 2023.
  * [45] Muru Zhang, Ofir Press, William Merrill, Alisa Liu, and Noah A. Smith.  How language model hallucinations can snowball, 2023.
  * [46] Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Brian Ichter, Fei Xia, Ed Chi, Quoc Le, and Denny Zhou.  Chain-of-thought prompting elicits reasoning in large language models, 2023.
  * [47] Boshi Wang, Sewon Min, Xiang Deng, Jiaming Shen, You Wu, Luke Zettlemoyer, and Huan Sun.  Towards understanding chain-of-thought prompting: An empirical study of what matters.  CoRR, abs/2212.10001, 2022.
  * [48] Samuel Bowman.  The dangers of underclaiming: Reasons for caution when reporting how NLP systems fail.  In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 7484–7499, Dublin, Ireland, May 2022. Association for Computational Linguistics.
  * [49] Simone Tedeschi, Johan Bos, Thierry Declerck, Jan Hajic, Daniel Hershcovich, Eduard H. Hovy, Alexander Koller, Simon Krek, Steven Schockaert, Rico Sennrich, Ekaterina Shutova, and Roberto Navigli.  What’s the meaning of superhuman performance in today’s nlu?, 2023.
  * [50] Laura Weidinger, Jonathan Uesato, Maribeth Rauh, Sean Griffin, Conor […] Legassick, Geoffrey Irving, and Iason Gabriel.  Taxonomy of risks posed by language models.  In 2022 ACM Conference on Fairness, Accountability, and Transparency, FAccT ’22, page 214–229, New York, NY, USA, 2022. Association for Computing Machinery.
  * [51] Ning Bian, Peilin Liu, Xianpei Han, Hongyu Lin, Yaojie Lu, Ben He, and Le Sun.  A drop of ink may make a million think: The spread of false information in large language models, 2023.
  * [52] Ross Boyd and Robert J. Holton.  Technology, innovation, employment and power: Does robotics and artificial intelligence really mean social transformation?  Journal of Sociology, 54(3):331–345, 2018.
  * [53] Sébastien Bubeck, Varun Chandrasekaran, Ronen Eldan, Johannes Gehrke, Eric Horvitz, Ece Kamar, Peter Lee, Yin Tat Lee, Yuanzhi Li, Scott Lundberg, Harsha Nori, Hamid Palangi, Marco Tulio Ribeiro, and Yi Zhang.  Sparks of artificial general intelligence: Early experiments with gpt-4, 2023.
  * [54] BigScience, Teven Le Scao, Angela Fan, Christopher Akiki, Ellie Pavlick, Suzana Ilić, […], Younes Belkada, and Thomas Wolf.  Bloom: A 176b-parameter open-access multilingual language model, 2023\.
  * [55] Giada Pistilli.  What lies behind agi: Ethical concerns related to llms.  Éthique Et Numérique, 1(1):59–68, 2022.
  * [56] Murray Shanahan.  Talking about large language models, 2023.
  * [57] Gavin Abercrombie, Amanda Cercas Curry, Tanvi Dinkar, and Zeerak Talat.  Mirages: On anthropomorphism in dialogue systems, 2023.
  * [58] Evgeny Morozov.  To save everything, click here: The folly of technological solutionism.  Public Affairs, 2013.
  * [59] Robert A. Cropf.  Benkler, y. (2006). the wealth of networks: How social production transforms markets and freedom. new haven and london: Yale university press. 528 pp.  Social Science Computer Review, 26(2):259–261, 2008.
  * [60] Chris DiBona and Sam Ockman.  Open sources: Voices from the open source revolution.  " O’Reilly Media, Inc.", 1999.
  * [61] Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, et al.  Pytorch: An imperative style, high-performance deep learning library.  In Advances in Neural Information Processing Systems 32, pages 8024–8035. Curran Associates, Inc., 2019.
  * [62] Martín Abadi, Paul Barham, Jianmin Chen, Zhifeng Chen, Andy Davis, Jeffrey Dean, et al.  Tensorflow: A system for large-scale machine learning.  In 12th $\\{$USENIX$\\}$ Symposium on Operating Systems Design and Implementation ($\\{$OSDI$\\}$ 16), pages 265–283, 2016.
  * [63] European Parliament and Council.  Draft compromise amendments on the draft report: Proposal for a regulation of the european parliament and of the council laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts - general approach, 2023-05-16.
  * [64] Philipp Hacker, Andreas Engel, and Marco Mauer.  Regulating chatgpt and other large generative AI models.  CoRR, abs/2302.02337, 2023.
  * [65] Jean Baudrillard.  Simulacra and simulation.  University of Michigan press, 1994.
  * [66] Edwin Hutchins.  Cognitive ecology.  Topics in cognitive science, 2(4):705–715, 2010.
  * [67] European Parliament and Council.  Proposal for a directive of the european parliament and of the council on adapting non-contractual civil liability rules to artificial intelligence (ai liability directive), 2022-09-28.
  * [68] Ilia Shumailov, Zakhar Shumaylov, , Yiren Zhao, Yarin Gal, Nicolas Papernot, and Ross Anderson.  Model dementia: Generated data makes models forget, 2023.
  * [69] Thomas H. Davenport and John C. Beck.  The attention economy.  Ubiquity, 2001(May):6, 2001.
  * [70] Filippo Ferrari and Sebastiano Moruzzi.  Verità e post-verità: dall’indagine alla post-indagine.  1088press, 2020.
  * [71] George Lakoff.  Don’t think of an elephant!: Know your values and frame the debate.  Chelsea Green Publishing, 2014.