Anthropomorphism in AI: hype and fallacy

Abstract

This essay focuses on anthropomorphism as both a form of hype and fallacy. As a form of hype, anthropomorphism is shown to exaggerate AI capabilities and performance by attributing human-like traits to systems that do not possess them. As a fallacy, anthropomorphism is shown to distort moral judgments about AI, such as those concerning its moral character and status, as well as judgments of responsibility and trust. By focusing on these two dimensions of anthropomorphism in AI, the essay highlights negative ethical consequences of the phenomenon in this field.

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Vol.:(0123456789)
AI and Ethics (2024) 4:691–698
https://doi.org/10.1007/s43681-024-00419-4
ORIGINAL RESEARCH
Anthropomorphism inAI: hype andfallacy
AdrianaPlacani1
Received: 27 October 2023 / Accepted: 4 January 2024 / Published online: 5 February 2024
© The Author(s) 2024
Abstract
This essay focuses on anthropomorphism as both a form of hype and fallacy. As a form of hype, anthropomorphism is shown
to exaggerate AI capabilities and performance by attributing human-like traits to systems that do not possess them. As a
fallacy, anthropomorphism is shown to distort moral judgments about AI, such as those concerning its moral character and
status, as well as judgments of responsibility and trust. By focusing on these two dimensions of anthropomorphism in AI,
the essay highlights negative ethical consequences of the phenomenon in this field.
Keywords AI· Anthropomorphism· Ethics· Moral judgment· Fallacy· Hype
1 Introduction
The roots of anthropomorphism run deep. In the eighteenth
century, David Hume wrote that there is a “universal ten-
dency among mankind to conceive all beings like them-
selves and to transfer to every object, those qualities... and
by a natural propensity, if not corrected by experience and
reflection, ascribe malice or good-will to every thing, that
hurts or pleases us” [1]. The long-standing phenomenon of
anthropomorphism is still present today and one if its new-
est incarnations is in the field of artificial intelligence (AI).
There are many examples of anthropomorphism in the
AI field, but perhaps the most famous instantiation of it is
the “ELIZA effect”. ELIZA, considered the first chat bot,
was a natural language processing program developed by
Joseph Weizenbaum at MIT in the 1970s. In spite of the
unusually constrained form of dialogue used by ELIZA [2],
which consisted of simply mirroring or rearranging what-
ever a user said in the style of a Rogerian psychotherapist,
people related to the program in anthropomorphic ways as
though it was a person [3]. As Weizenbaum wrote: "What I
had not realized is that extremely short exposure to a rela-
tively simple computer program could induce powerful delu-
sional thinking in quite normal people” [3]. Subsequently,
Weizenbaum spent much of his life warning about the dan-
gers of projecting human qualities onto AI.
In a similar vein, this essay offers an examination of
anthropomorphism in AI by focusing primarily on some of
its negative ethical consequences. An exhaustive analysis
of such consequences would be virtually impossible, but by
focusing on anthropomorphism construed as a form of hype
and as a fallacy this work shows that and how anthropo-
morphism overinflates the capabilities and performance of
AI systems, as well as distorts a host of moral judgments
about them.
This work is structured as follows. In the first section,
the paper explains what anthropomorphism entails, as well
as some of ways in which the phenomenon manifests itself
in the field of AI. Emphasis is placed here on showing
that anthropomorphism is a constitutive part of the hype
surrounding AI. Hype in this context is understood as the
misrepresentation and over-inflation of AI capabilities and
performance, while being a constitutive part of hype is
understood as being a part of the creation of hype. In the
second section, the essay shows that anthropomorphism
distorts moral judgments through its fallacious character. It
illustrates this by focusing on four central moral judgments
about AI: judgments concerning its moral character and sta-
tus, as well as judgments about responsibility and trust in
AI. The third section ends this work by providing a brief
summary and conclusion.
* Adriana Placani
adrianaplacani@fcsh.unl.pt
1 Institute ofPhilosophy (IFILNOVA), Nova University
ofLisbon, Lisbon, Portugal
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692 AI and Ethics (2024) 4:691–698
2 Anthropomorphism andhype aboutAI
This section briefly describes anthropomorphism in general
terms, only to focus in more detail on its manifestations in
the AI field. The aim here is to show that and how anthropo-
morphism can be construed as a form of hype in virtue of it
misrepresenting, distorting and exaggerating AI capabilities
and performance.
Anthropomorphism is the ascription of human qualities
(e.g., intentions, motivations, human feelings, behaviors)
onto non-human entities (e.g., objects, animals, natural
events) [4, 5]. This phenomenon is considered an evolution-
ary and cognitive adaptive trait [6], which does not neces-
sarily correlate to the features of that which is anthropo-
morphized [4]. Instead, it represents a distinctively human
process of inference or interpretation [7] that includes not
only perceiving an entity as human-like in terms of its physi-
cal features, but also imbuing it with mental capacities con-
sidered uniquely human, such as emotions (e.g. empathy,
revenge, shame, and guilt) and the capacity for conscious
awareness, metacognition and intention formation [8].
Anthropomorphism is a pervasive and widespread phe-
nomenon that garners new dimensions in the realm of AI.
One dimension that is seldom emphasized relates to the hype
surrounding AI systems. In virtue of the attribution of dis-
tinct human characteristics that misrepresent and exaggerate
AI capabilities and performance, anthropomorphism in AI
can be viewed as a constitutive part of hype. To see this,
consider first anthropomorphic language.
Anthropomorphic language is so prevalent in the disci-
pline that it seems inescapable. Perhaps part of the reason
is because anthropomorphism is built, analytically, into the
very concept of AI. The name of the field alone—artificial
intelligence—conjures expectations by attributing a human
characteristic—intelligence—to a non-living, non-human
entity, which thereby exposes underlying assumptions about
the capabilities of AI systems. Using such anthropomorphic
language also invites interpreting algorithmic behavior as
human-like so that it may be compared to human modes of
reasoning [9].
Going beyond the concept, there are many examples of
anthropomorphic language that exaggerate the capabilities of
AI, starting from the earliest days of the field. Alan Turing,
creator of the Turing test, among other things, described
his machines in anthropomorphic terms in spite of the fact
that they were simple abstract computational devices. For
example, he compared what he dubbed his ‘child-machine’
to Helen Keller and said that the machine could not ‘be sent
to school without the other children making excessive fun
of it’, but that it would get ‘homework’ [10].
Famed cyberneticist Valentino Braitenberg also used
anthropomorphisms to describe his very simple robot
vehicles, which were said to dream, sleepwalk, have free
will, ‘ponder over their decisions’, be ‘inquisitive’, ‘opti-
mistic’, and ‘friendly’ [10]. Other researchers, such as David
Hogg, Fred Martin, and Mitchel Resnick used anthropo-
morphic language for their robots even though these robots
were built from LEGO bricks containing electronic circuits.
Masaki Yamamoto described his vacuum cleaner robot,
Sozzy, as ‘friendly’ and as having ‘four emotions... joy,
desperation, fatigue, sadness’ [10].
More recently, Sophia, a robot with a human-like form
was granted citizenship in Saudi Arabia, was a guest on
various TV shows and news programs, and appeared beside
world leaders and policymakers. As Sven Nyholm [11–13].
Writing about Sophia, computer scientist Noel Sharkey high-
lighted that “it is vitally important that our governments and
policymakers are strongly grounded in the reality of AI at
this time and are not misled by hype, speculation, and fan-
tasy” [13].
The examples above show how anthropomorphisms
have been part and parcel of the hype surrounding AI in
robotics and, indeed, anthropomorphism is a well-known
and well-researched phenomenon in this area. After all,
human characteristics are used as guiding principles in robot
design, while perceiving robots as humanlike is important
to human–robot interactions [14, 15]. However, this should
not lead to the conclusion that anthropomorphism in the AI
field is isolated to robotics.
Anthropomorphism has also been displayed around deep
neural networks (DNNs). In 2022, Ilya Sutskever, co-founder
and chief scientist at OpenAI, hyped up DNNs by declaring:
“it may be that today’s large neural networks are slightly
conscious” [16]. It is true that DNNs are one of the most
advanced and promising fields within AI research, with
DNN architecture applied in AlphaZero’s famous win over
the human Go world champion and a part of many AI-related
applications, such as Google translation services, Face-
book facial recognition software, and virtual assistants like
Apple’s Siri [9, 17]. However, in spite of the many accom-
plishments achieved using deep neural networks, parallels
to the human brain should be resisted.
Shimon Ullman [18] argues that almost everything we
know about neurons (e.g., structure, types, interconnec-
tivity) has not been incorporated in these networks [17,
18]. DNNs use a limited set of highly simplified homo-
geneous artificial “neurons”, whereas biological neuronal
architecture displays a heterogeneity of morphologies and
functional connections [17, 18]. Thus, describing network
units in anthropomorphic terms as, for example, biological
neurons is an enormous simplification given the highly
sophisticated nature and diversity of neurons in the brain
[17, 19]. Conversely, it is also an over-inflation of DNNs’
capabilities.
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693AI and Ethics (2024) 4:691–698
The New York Times’ 2018 article on AlphaZero’s victo-
ries is a good example of anthropomorphic tendencies that
seem to do just that—overinflate capabilities:
Most unnerving was that AlphaZero seemed to express
insight. It played like no computer ever has, intuitively
and beautifully, with a romantic, attacking style. It
played gambits and took risks. In some games it para-
lyzed Stockfish [the reigning computer world cham-
pion of chess] and toyed with it . . . AlphaZero had
the finesse of a virtuoso and the power of a machine.
It was humankind’s first glimpse of an awesome new
kind of intelligence. . . AlphaZero won by thinking
smarter, not faster . . . It was wiser, knowing what to
think about and what to ignore [20].
Part of the problem with anthropomorphic language as
exhibited above is that it asserts an out-of-place human-cen-
tric perspective that conceals the reality of how these net-
works work, as well as their limitations. David Watson [9],
for example, has argued that DNNs’ similarities to human
cognition have been seriously overstated and narrowly con-
strued, especially in light of DNNs’ considerable shortcom-
ings (e.g., brittleness, inefficiency, and myopia).
A final example of anthropomorphism that exaggerates
AI capabilities and performance comes from large language
models (LLMs). LLMs, such as ChatGPT, Bing Chat (Syd-
ney) and LaMDA have garnered a lot of attention recently.
These AI-powered chat bots belong to the class of AI called
generative AI, are trained on vast amounts of data, use artifi-
cial neural networks and can generate human-like responses
to any question users can think of. Given the latter, it almost
seems like hype through anthropomorphism was bound to
happen.
For example, in a recent cross-sectional study of 195 ran-
domly drawn patient questions, a team of licensed health
care professionals compared physicians and ChatGPT’s
responses to patients’ questions [21]. The chat bot responses
were preferred over physician responses and rated signifi-
cantly higher for both quality and empathy [21]. Impor-
tantly, the proportion of responses rated empathetic or very
empathetic was significantly higher for the chat bot than for
physicians, amounting to a 9.8 times higher prevalence of
empathetic or very empathetic responses for the chat bot
[21]. This means that almost half of responses from Chat-
GPT were considered to be empathetic (45%) compared to
less than 5% of those from physicians.
This example is noteworthy because attributing empathy
to a chat bot anthropomorphizes the latter since empathizing
is a complex emotional and cognitive process that involves
the ability to recognize, comprehend and share the feelings
of others.
Other notorious examples of anthropomorphizing chat
bots include the infamous exchange between Sydney,
Microsoft’s chatbot, and the New York Times’ technology
columnist Kevin Roose [22] and the declaration by Blake
Lemoine, a Google engineer, that the company’s chat bot,
LaMDA, was conscious and capable of feelings [23].
Given that AI is far from being sentient now, anthropo-
morphisms such as these fan the flames of hype by mis-
representing the current state of AI systems and potentially
leading to mistaken beliefs, as well as overblown fears and
hopes. AI already exhibits a great deal of influence in our
world, and this is only going to continue to grow. Exag-
gerating the capabilities of these systems conceals the real-
ity of AI achievements and impedes their understanding.
This leads to a generalized lack of knowledge about how
these systems work, which can feed extreme beliefs and
sentiments through misinformation. On the other hand, the
phenomenon is also reductive because it asserts an out-of-
place, bio-centric perspective that can overlook the unique
potential of artificial systems.
Projecting human capabilities onto artificial systems is a
relatively new manifestation of a long-standing and natural
phenomenon, but in the realm of AI, this may lead to seri-
ous ramifications. The above offers some telling examples
of anthropomorphism in AI but does not and indeed cannot
provide an exhaustive account of this phenomenon or its
connection to hype. Nevertheless, it seems fair to conclude
that anthropomorphism is part of the hype surrounding AI
systems because of its role in exaggerating and misrepre-
senting AI capabilities and performance. Furthermore, such
over-inflation and misrepresentation is nothing mysterious.
It is simply due to projecting human characteristics onto
systems that do not possess them.
3 Anthropomorphism andmoral judgments
aboutAI
The previous section showed the prevalence of anthropo-
morphism as exhibited across the field by researchers, devel-
opers, science communicators, and the public. It also showed
that the pervasiveness of this phenomenon is nothing new.
However, in spite of the fact that anthropomorphism is a
well-known occurrence, its ethical consequences are less
understood.
Anthropomorphism is also a kind of fallacy, and this is
often overlooked. The fallacy occurs when one assumes or
makes the unwarranted inference that a non-human entity has
a human quality. This can involve projecting human charac-
teristics onto non-humans, such as: “My car is angry at me”
or making an unwarranted inference about non-humans, such
as “The robot is friendly because it waved at me”. In this
way, anthropomorphism can be regarded as either a factual
error—when it involves the attribution of a human character-
istic to some entity that does not possess that characteristic,
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694 AI and Ethics (2024) 4:691–698
or as an inferential error—when it involves an inference that
something is or is not the case when there is insufficient
evidence to draw such a conclusion [24].
As a kind of fallacy, then, anthropomorphism involves
a factually erroneous or unwarranted attribution of human
characteristics to non-humans.1 Given this, when anthropo-
morphism becomes part of reasoning it leads to unsupported
conclusions. The following will discuss some of these con-
clusions and how they occur within moral judgment. In this
way, some of the negative ethical implications of anthropo-
morphizing AI will be exposed.
There is a necessary connection between attributing
human traits to AI and a distorting effect on various moral
judgments about AI. This distorting effect occurs because
attributing human characteristics to AI is currently falla-
cious, affecting beliefs and attitudes about AI, which in
turn play a role in moral judgment.2 The activity of moral
judgment is that of reasoning, deliberating, thinking about
whether something has a moral attribute [25]. The thing
assessed might be an action, person, institution or state of
affairs, and the attribute might either be general (such as
rightness or badness) or specific (such as loyalty or injus-
tice) [25].
For example, consider how anthropomorphic language
(e.g., AI systems “learn”, decision algorithms “think”, clas-
sification algorithms “recognize”, Siri and Alexa are “listen-
ing”) can influence deliberation, be it moral or otherwise.
Such language shapes how we think about AI because it
provides us with the conceptual framework, tools, and ter-
minology for forming, expressing and organizing our beliefs,
expectations and general understanding of these systems. In
other words, it is how we conceptualize AI.
Furthermore, anthropomorphic language stands to influ-
ence both conscious and unconscious thinking about AI.
Although it might be thought that only conscious reflec-
tion plays a central role in moral judgments, Haidt [26], for
example, has argued that quick, automatic processes drive
moral judgment while reflective processes play a more ad
hoc role. Consequently, even if, on reflection, one might
actively avoid anthropomorphic language when engaging
in moral reasoning, it is possible that moral judgments are
still distorted by it.
Furthermore, consider perhaps the biggest problem with
anthropomorphizing AI, which is that viewing AI as human-
like involves viewing it as having human-like agency. To be
clear, at this point at least, conceiving of AI as having this
kind of agency is a mistake because human agency involves
having the capacity to act intentionally, where intentional
actions are explained in terms of mental states (e.g., beliefs,
desires, attitudes) that are the causal antecedents of an
agent’s behavior [27]. No such mental states could be attrib-
uted, plausibly, to current AI, which means that attribut-
ing this kind of agency to AI systems is a mistake and not
an isolated one.3 This error can have serious consequences
because it can distort moral judgments about AI. When an
error such as this becomes part of moral reasoning, then
arguments based (or partly based) on it become fallacious
and any subsequent conclusion unfounded.
To appreciate the distorting effects of anthropomorphism,
the following will consider four moral judgments about AI
systems: judgments of moral character, judgments of moral
status, responsibility judgments and judgments of trust. To
be clear, these moral judgments are distorted not necessarily
in their verdict, but in the process of arriving at their verdict
when this process is (partly) based on anthropomorphism.
Furthermore, these moral judgments are to be addressed
in turn even though there are many points of convergence
between them. Finally, it should be noted that a full treat-
ment of such extensive moral issues is not possible given
their breadth, but that, nevertheless, the following seeks to
illustrate how anthropomorphism affects them in virtue of
the attribution of human-level qualities onto entities that do
not possess them.
3.1 Judgments ofmoral character
Dating back to Aristotle, moral character is, primarily, a
function of having or lacking various virtues and vices. The
virtues and vices that comprise one’s moral character are
typically understood as dispositions to behave in certain
ways in certain sorts of circumstances [28]. Thus, a moral
character judgment can be defined as an evaluation of anoth-
er’s moral qualities, i.e., their virtues and vices.
Making moral character judgments about other people is
a common practice. The way in which such judgments are
made differs, but it typically involves, at least, three sources
of information: about another’s behavior, their perceived
mind and their identity [29]. Thus, character judgments
hinge on what others do, what they seem to think and on
1 This does not deny that it is possible for non-humans to possess
human characteristics. However, as a fallacy, anthropomorphism
necessarily involves a kind of an error. Indeed, charges of anthropo-
morphism usually imply some kind of mistaken attribution of human
traits [24].
2 Whether AI could develop human qualities (e.g., awareness) is an
open question.
3 There are scholars [44–46] who suggest that the concepts of agency
and moral agency should be broadened and intentions not taken into
account. They argue in favor of artificial or virtual agency and even
artificial or virtual moral agency, but they do not claim that these
kinds of agencies are human-like. For example, Floridi [47] claims
that to be called ‘agents’ systems have to be interactive, autonomous,
and adaptive and that all ‘agents’ whose actions have morally qualifi-
able consequences are ‘moral agents’ [46]. For a useful criticism of
this view, see Fritz, etal. [48].
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695AI and Ethics (2024) 4:691–698
who these others are (e.g., in terms of appearance, group
membership) [29].
Normally, the second criteria for making character judg-
ments—the perceived mind of others—disqualifies non-
human entities from being the subject of moral character
judgments [29]. This is because when making character
judgments about others, one must make inferences about
their minds, which includes making inferences about their
intentions and moral capacities [29]. In the case of AI sys-
tems, the absence of mental states, their inability to under-
stand moral issues or reason about morality should disqual-
ify them from being the subjects of such judgments.
However, anthropomorphism can change all that. In fact,
the previous section offered some examples of moral char-
acter judgments of AI, such as the ‘empathetic’ ChatGPT,
the ‘friendly’ Sozzy robot, and the ‘wise’ AlphaZero. This
means not only that anthropomorphism can distort moral
judgments, but also that it can distort them to such an extent
that a previously inappropriate evaluation becomes appropri-
ate. By projecting a mind onto AI systems, AI becomes the
subject of moral character evaluations.
If AIs are perceived as having mental states, then they
can be characterized in moral terms as good, evil, friendly,
empathetic, wise, loyal, courageous, bad, trustworthy, etc. In
other words, the whole plethora of virtues and vices, which
are said to make up moral character, becomes available. In
the absence of moral agency, this is problematic. For exam-
ple, on Aristotle’s view, a virtuous agent is not one that just
performs virtuous actions, but also one that understands
those actions, whose actions result from a fixed character,
and who chooses the action in question “for its own sake”
(e.g., the agent chooses to be honest because they believe
there is something intrinsically good about being honest)
[30, 31]. These criteria are far from the capabilities of cur-
rent AI systems, which means that attributing virtues to them
is troublesome.
Moreover, trouble compounds because character is usu-
ally perceived as a partial driver of future moral behavior.
For example, a person judged to be ‘evil’ will probably be
perceived as more likely to do evil things, while a person
judged to be ‘good’ will probably be perceived as more
likely to do good things [29]. This means that AIs perceived
in moral terms will also be perceived as more or less likely
to behave in accordance with their so-called virtues and
vices. This, in turn, can affect human interactions with AI
systems, as well as human dispositions, expectations and
attitudes towards AI (e.g., of trust, hope, suspicion). Need-
less to say, these would be as supported as the attribution of
virtues and vices on the basis of anthropomorphism. That
is, not at all.
3.2 Judgments ofmoral status
An entity with moral status is one that matters (to some
degree) morally in and of itself [32]. More precisely, if an
entity has moral status, then there are certain moral rea-
sons or requirements concerning how it is to be treated for
its own sake [32]. Thus, to have a moral status is to be an
entity towards which moral agents have, or can have, moral
obligations [33].
Arguably, the moral status of an entity should be based on
the intrinsic properties of that entity [34].4 In the 2020 book,
Ethics of Artificial Intelligence, Matthew Liao [34] provides
the following list of empirical, non-speciesist, intrinsic
properties that could afford moral status to an entity: being
alive; being conscious; being able to feel pain; being able to
desire; being capable of rational agency (e.g., being able to
know something about causality, such as if one does x, then
y would happen, and being able to bring about something
intentionally); being capable of moral agency, such as being
able to understand and act in light of moral reasons. If some
or all of these characteristics are present in an entity, then
moral status could be afforded to it.
It is clear that anthropomorphizing AIs can involve
the projection of some of these qualities onto AI systems.
Anthropomorphism is the attribution of intrinsic human
qualities onto non-humans. The problem is that these quali-
ties can then become a part of moral judgment. For example,
viewing AI as human-like could project consciousness onto
AI systems. We have seen that this actually happens in the
previous section. However, viewing AI as conscious is not
contained to just this, but can become a reason in favor of
conferring moral status onto it.
Anthropomorphism raises problems for the kinds of evi-
dence we need to make inferences about the moral status of
entities like AI. John Danaher [35], a proponent of ethical
behaviorism, claims that a sufficient ground for believing
that an entity has moral status is that it is roughly behavio-
rally equivalent to another entity of whose moral status we
are already convinced (e.g., humans).5 However, judgments
of behavioral equivalency can easily be undermined by the
tendency to project human qualities onto AI.6
The projection of human traits onto AI systems can
bestow AI with moral status when it is not deserved through
the attribution of intrinsic qualities that are not present in
4 Cf. Mark Coeckelbergh [49] who advances a relational approach
to moral status, which affords the latter based on social relations
between different entities (e.g., human beings and robots).
5 Cf. Shevlin [50] who argues in favor of cognitive equivalence,
which is the view that we ought to regard AI as a psychological moral
patient if it is cognitively equivalent to beings we already regard as
psychological moral patients.
6 Thank you to an anonymous reviewer for suggesting this implica-
tion.
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696 AI and Ethics (2024) 4:691–698
AI, but that are criteria for moral status (e.g., capacity for
rational agency, capacity for feeling). Granting moral status
to AIs would then make them into a site of moral concern,
as well as make them into potential right-holders to whom
duties are owed. Of course, if AIs come to possess some or
all of the intrinsic qualities mentioned, then it is plausible to
afford them the same kind of moral status as other entities
that have such properties [34]. Until then, however, such
judgments are flawed.
3.3 Responsibility judgments
Another moral judgment that can be affected by anthropo-
morphism concerns attributions of moral responsibility. If
AIs are attributed certain human capacities, then their pos-
session could qualify them as morally responsible agents.
For example, if AI is perceived as having a mind of its own,
then this means that it can be viewed as capable of inten-
tional action, and therefore, held responsible for its actions
[36]. For an entity to be morally responsible for its actions, it
has to be a moral agent. At this point, it is important to note
that having moral status, which was the judgment considered
before, and being a moral agent are distinct. An entity is a
moral agent when it is morally responsible for what it does.
For example, a baby is not a moral agent because it lacks
moral competency, but it does have a moral status as it is
considered a moral patient that can be wronged.
If AI is perceived as a moral agent, then it can be held
responsible and blamed or praised for its actions, as well
as for the consequences of its actions. However, blaming
or praising an AI would be futile given the absence of any
kind of understanding of such moral responses on the part
of AI systems. The error is in regarding the AI itself as a
site of moral responsibility in the absence of moral agency.
Moral agency requires that one can meet the demands of
morality. This requirement is interpreted in different ways
on different accounts: as being able to obey moral laws, act
for the sake of the moral law, have an enduring self with free
will and an inner life, understand relevant facts as well as
have moral understanding, have a capacity for remorse and
concern for others [25]. Arguably, until AI can meet such
requirements, they should not be considered moral agents.7
Through the process of anthropomorphization, however,
AIs can be attributed qualities that render a verdict of moral
agency plausible when it is not.
One of the dangers of anthropomorphizing AI in this
way is that judgments about responsibility might then focus
solely on AI as a locus of responsibility. This is a problem
in and of itself because of the error in agential attribution on
anthropomorphic bases, but also because it has the potential
to absolve others of responsibilities that they ought to bear
by shifting focus onto AIs themselves. For example, hold-
ing an AI to be responsible for some action or outcome can
obfuscate the responsibilities of potentially rightful bear-
ers of responsibility, such as those of owners, developers or
governments who each have distinct roles to play in, respec-
tively, responsible ownership, development and regulation.8
If AIs are viewed as having a mind of their own, then
this can lead not only to a distorted judgment of respon-
sibility that sees the AIs as responsible, but also to others
being wrongfully absolved of responsibility, as well as to a
generalized sense of a loss or outright lack of control. The
latter effect is because if AIs are moral agents, then they are
also autonomous decision-makers who are able to choose
their own goals and act freely in light of moral reasons. This
would mean that their decisions are outside of human con-
trol, as well as (usually) opaque because of the black-box
nature of AI algorithms, but that they ought to be given the
same moral weight and respect as the decisions of any other
moral agent’s. However, until AIs become such agents (if
ever), such moral judgments are flawed.
3.4 Judgments oftrust
Trust is an attitude towards those (or that) which we hope
is trustworthy, where trustworthiness is a property not an
attitude [37]. Trust and trustworthiness are distinct, but, ide-
ally, what is trusted is trustworthy, and what is trustworthy
is trusted [37]. However, it is clear that trust can be mis-
placed. At a very basic level, trust is about a trustor that
trusts (judges the trustworthiness of) a trustee with regard
to some object of trust [38]. Trustworthy trustees are those
worthy of being trusted. To be worthy of trust, they must be
capable of being trusted, which means that they must have
the competence to fulfil the trust that is placed in them [39].
One of the dangers of anthropomorphizing AI is that
judgments about whether to trust AI can become judgments
concerning the trustworthiness of the AI itself. However,
according to two of the most prevalent conceptions of
trust, AIs are not capable of being trusted [39]. On affec-
tive accounts of trust, ‘trust is composed of two elements:
an affective attitude of confidence about the goodwill and
7 Cf. Floridi [47] who argues that all ‘agents’ whose actions have
morally qualifiable consequences are ‘moral agents’, while to be
called ‘agents’ systems have to be interactive, autonomous, and adap-
tive. According to Floridi, such ‘moral agents’ without intentions are
not morally responsible, but they are accountable (e.g., they can be
modified, deleted, disconnected) [46, 48]. Fritz, et al. [48] criticize
this view of ‘moral agency’ without moral responsibility as an empty
concept.
8 Cf. Bryson, Diamantis, and Grant [51] who argue in a similar vein,
but about the dangers of granting AI legal personhood, that natural
persons could use artificial persons to shield themselves from the
consequences of their conduct and Rubel, Castro, and Pham [52] who
argue that enlisting technological systems into agents’ decision-mak-
ing processes can obscure moral responsibility for the results.
Content courtesy of Springer Nature, terms of use apply. Rights reserved.

697AI and Ethics (2024) 4:691–698
competence of another... and, further, an expectation that
the one trusted will be directly and favorably moved by the
thought that you are counting on them’ [40]. However, AI
lacks the capacity to be moved by trust or a sense of good-
will since it lacks any emotive states [39]. On normative
accounts, trustees need to be appropriate subjects of blame
in those situations when trust is breached [39]. This means
that trustees need to be able to understand and act on what
is entrusted to them, as well as be held responsible for those
actions [39]. However, AI is not a moral agent in any such
standard sense, so it cannot he held morally responsible for
its actions. Thus, AI lacks the capacity for being trusted on
both of these accounts.
The problem then is that anthropomorphizing AIs can
lead to viewing such systems as trustworthy. Certain quali-
ties can be projected onto AI, such as goodwill, empathy, or
moral agency, and on their bases, the wrong conclusion can
be drawn. Conceiving of AIs themselves as trustworthy is,
by itself, erroneous when based on such projected qualities,
but it can also have additional effects. For example, a verdict
that AI systems are trustworthy can obfuscate the degree
of trustworthiness of other parties. This is because trusting
AIs because AIs themselves are trustworthy can leave out
of considerations factors that ought to include when mak-
ing such verdicts, such as the trustworthiness of owners, of
developers, of organizations behind the deployment of AIs
or the trustworthiness of governments whose responsibility
it is to regulate the industry. This means that regarding AIs
as trustworthy is not only a problematic moral judgment, but
also an obfuscation of important considerations that should
factor in judgments of trust.
It should be noted that, in the literature, the idea that
anthropomorphism in AI affects trust is present by way of
empirical findings that support the view that anthropomor-
phism increases trust in AI. For example, in the context of
both autonomous vehicles and virtual agents, people showed
more trust in AIs with human characteristics than without
[41–43]. In general, the claim is that the more human-like an
AI agent is, the more likely humans are to trust and accept it
[41]. If this is accurate, then this carries with it serious ethi-
cal implications as well because of the possibility of exploit-
ing this human bias for manipulative or deceptive purposes.
4 Conclusion
This work has focused on anthropomorphism as a form of
hype and as a fallacy. The first section showed how anthro-
pomorphism tends to exaggerate and misrepresent AI capa-
bilities by attributing human-like attributes onto systems
that do not possess them. The second section showed that,
via the same mechanism, anthropomorphism distorts moral
judgments about AI, such as those concerning AI’s moral
character and status, as well as judgments of responsibility
and trust in AI. In these ways, this work has shown some of
the more acute negative consequences of anthropomorphism.
Funding Open access funding provided by FCT|FCCN (b-on). Adriana
Placani’s work is financed by national funds through FCT - Fundação
para a Ciência e a Tecnologia, I.P., under the Scientific Employment
Stimulus - Individual Call -CEECIND/02135/2021.
Declarations
Conflict of interest The author has no competing interests to declare
that are relevant to the content of this article.
Open Access This article is licensed under a Creative Commons Attri-
bution 4.0 International License, which permits use, sharing, adapta-
tion, distribution and reproduction in any medium or format, as long
as you give appropriate credit to the original author(s) and the source,
provide a link to the Creative Commons licence, and indicate if changes
were made. The images or other third party material in this article are
included in the article’s Creative Commons licence, unless indicated
otherwise in a credit line to the material. If material is not included in
the article’s Creative Commons licence and your intended use is not
permitted by statutory regulation or exceeds the permitted use, you will
need to obtain permission directly from the copyright holder. To view a
copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
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