# What Do People Think about Sentient AI?

Jacy Reese Anthis1,2,3, Janet V.T. Pauketat1, Ali Ladak1,4, Aikaterina
Manoli1,5 1Sentience Institute  2University of Chicago  3Stanford University
4University of Edinburgh  5Max Planck Institute for Human Cognitive and Brain
Sciences

###### Abstract.

With rapid advances in machine learning, many people in the field have been
discussing the rise of digital minds and the possibility of artificial
sentience. Future developments in AI capabilities and safety will depend on
public opinion and human-AI interaction. To begin to fill this research gap,
we present the first nationally representative survey data on the topic of
sentient AI: initial results from the Artificial Intelligence, Morality, and
Sentience (AIMS) survey, a preregistered and longitudinal study of U.S. public
opinion that began in 2021. Across one wave of data collection in 2021 and two
in 2023 (total N = 3,500), we found mind perception and moral concern for AI
well-being in 2021 were higher than predicted and significantly increased in
2023: for example, 71% agree sentient AI deserve to be treated with respect,
and 38% support legal rights. People have become more threatened by AI, and
there is widespread opposition to new technologies: 63% support a ban on
smarter-than-human AI, and 69% support a ban on sentient AI. Expected
timelines are surprisingly short and shortening with a median forecast of
sentient AI in only five years and artificial general intelligence in only two
years. We argue that, whether or not AIs become sentient, the discussion
itself may overhaul human-computer interaction and shape the future trajectory
of AI technologies, including existential risks and opportunities.

Artificial Intelligence, Human-AI Interaction, Human-Computer Interaction, AI
Safety, Social Computing, Mind Perception, Mind Attribution, Morality,
Sentience, Artificial General Intelligence, Anthropomorphism, Survey, Public
Opinion, AIMS

##  1\. Introduction

Philosophers and scientists have long debated the possibility of artificial
intelligence (AI) systems developing mental faculties, such as consciousness
or sentience. Popular questions include: Can an AI ever have a mind of its
own? How should we treat sentient AIs if they are created? Should humanity, as
Metzinger suggests (Metzinger, 2021), ban the development of sentient AI? When
should we expect sentient AI to first be created? These profound questions—on
mind perception, morality, policy, and forecasting—are now being asked and
answered by researchers, industry leaders, and much of the general public.

Figure 1. Proportions of responses to selected questions about the mind
perception of sentient AI (rows) in the three survey waves (columns).

Regardless of whether sentient AI has been, can be, or should be developed,
the act of asking and answering questions about sentient AI may itself reshape
human-AI interaction. We know from the human-computer interaction (HCI) and
human-robot interaction (HRI) literatures that “computers are social actors”
(CASA) (Hou et al., 2023; Nass et al., 1994); they can be perceived as having
their own minds (Gray et al., 2007; Scott et al., 2023; Stuart and Kneer,
2021; Thellman et al., 2022; Wang et al., 2021); and they can be perceived as
moral patients worthy of moral concern or consideration (Freier, 2008; Harris
and Anthis, 2021; Kahn et al., 2004; Lima et al., 2020; Pauketat and Anthis,
2022) and as moral agents worthy of praise, blame, and responsibility (Banks,
2019; Freier, 2007; Jackson and Williams, 2019; Kneer and Stuart, 2021). For
example, Scott et al. (2023) conducted an online survey of 100 Amazon
Mechanical Turk workers on perceptions of machine consciousness and identified
dynamic tensions in user perceptions, and Kneer and Stuart (2021) found that
whether people saw a harmful robot as blameworthy depended on whether it
seemed to have theory of mind (i.e., understanding the minds of others).

These dynamics are now manifesting in the public conscience. In 2022, Ilya
Sutskever, chief scientist at a leading AI company, OpenAI, tweeted, “it may
be that today’s large neural networks are slightly conscious.” A few months
later, Google vice-president Blaise Agüera y Arcas published an editorial in
The Economist on how, “Artificial neural networks are making strides towards
consciousness.” Weeks later, Google engineer Blake Lemoine vigorously argued
that the company’s chatbot LaMDA was sentient and needed legal protection,
which led to his termination from the company and sparked a global
conversation on the topic. Alongside this industry discourse, there has been
rapid growth in the academic literature on the possibility of AI with mental
faculties and its normative implications (Harris and Anthis, 2021).

Effective AI research, design, and use in the era of highly advanced
capabilities will require pragmatic knowledge of what and how the public
thinks about the systems that are proliferating through their everyday lives.
If people perceive sentience in AI or expect it to arrive soon, that may
change behavior, such as whether to accept or reject new technologies, the
extent to which they over- or under-trust AI, and how the increased presence
of AI in their life will affect their mental health and social relationships.
Negative reactions to AI may lead to conflict between divided human groups,
such as some in favor of and some against AI rights, and between humans and
AIs, posing an existential risk as AI becomes more powerful. In addition to
the direct importance of public opinion, public opinion has substantial
influence on policy, politics, and other social institutions that merit study
and forecasting (Barberá et al., 2019; Burstein, 2003). Therefore, the purpose
of this study was to collect the first detailed, longitudinal, and nationally
representative survey data on how people view and understand the possibility
of sentience and other mental faculties in AI in order to derive implications
for research and design. Specifically, we address the aforementioned topics of
mind perception, morality, policy, and forecasting by posing four overlapping
research questions (RQs):

  * •

RQ1: To what extent do people perceive sentience and other mental faculties in
AI?

  * •

RQ2: To what extent do people feel morally concerned for sentient AI (i.e.,
see them as moral patients) and threatened by AI (i.e., see them as (negative)
moral agents)?

  * •

RQ3: What policies are supported to govern interaction between humans and
sentient AI?

  * •

RQ4: When do people expect sentient AI to arrive, and what do they think will
happen when it does?

Because there have been such rapid changes in how AI is discussed, used, and
conceptualized by the public, we address each of these questions both as
snapshots of public opinion and a temporal study of how public opinion changed
from 2021 to 2023 via the first results from the U.S. nationally
representative Artificial Intelligence, Morality, and Sentience (AIMS) survey,
which we expect to conduct at least every two years with the Sentience
Institute, the nonprofit research organization hosting this project. We
collected three waves of data: one in 2021 and two in 2023.

The first AIMS survey wave (N = 1,232) was conducted from November to December
2021, approximately seven months before the topic of sentient AI entered the
public spotlight with the LaMDA discussion. We asked 86 questions related to
the perception of mental faculties in AI, moral concern of AI being harmed and
moral threat of AI enacting harm, banning sentience-related technologies,
forecasts of the future of sentient AI, personal AI usage, demographics, and
relevant background beliefs such as views on animal welfare and environmental
issues. From April to May 2023, approximately 11 months after the public
spotlight began and four months after the public release of ChatGPT—an AI
product that became the world’s fastest growing app (Hu, 2023)—we repeated the
same survey with a new nationally representative sample (N = 1,169). Finally,
to assess a wider range of perceptions and attitudes once AI had become more
popularized, we conducted a supplemental survey (N = 1,099) from May to July
2023 with 111 questions, including related topics such as public awareness of
specific AI systems, trust of AIs and their developers, and forecasts of the
future of artificial general intelligence (AGI).

In the 2021 AIMS data, we found surprisingly high mind perception (RQ1) and
attribution of moral status including both concern for and feeling threatened
by AI (RQ2) compared to preregistered predictions by researchers and
forecasters. Even some of the most strongly worded proposals garnered more
support than predicted, such as 37.2% agreement with, ”I support granting
legal rights to sentient robots/AIs” (RQ3). Participants expected sentient AI
to arrive surprisingly quickly with a median estimate of five years (RQ4).
From 2021 to 2023, there were significant increases in mind perception, moral
concern, and threat as well as a shortening timeline of when sentient AI will
arrive. In a set of policy questions asked in the supplemental 2023 survey
following political debates on the topic, we also found widespread support for
slowing down and regulating AI development. We found numerous significant
differences in opinion across age, gender, frequency of AI interaction, and
other participant characteristics.

These results contribute a number of implications for HCI research and design.
In Section 5, we develop and describe implications centered on the following
claims:

  1. (1)

Designers should prepare for a wide range of user reactions to AI systems that
are or appear to be digital minds.

  2. (2)

Perceptions of mind and moral status may amplify, mitigate, and complicate
well-established HCI dynamics.

  3. (3)

AI designers should proceed with caution as AI technology advances.

In general, this work provides a foundation to build on in this new area of
research during this challenging and dynamic period of major developments in
HCI practices.

##  2\. Related Work

###  2.1. Social Response and Mind Perception

There are numerous mechanisms by which humans interact with nonhuman systems
in ways similar to social interactions between humans. These span the course
of interaction from initial perception of the system to attitude and belief
formation to physical behavior. The CASA view suggests that these ”social”
responses need not occur because of a conscious belief that the computer has
human characteristics but can merely be the application of etiquette,
stereotypes, and other social scripts and norms (Nass et al., 1994; Reeves and
Nass, 1996). Studies conducted by Nass and colleagues in the 1990s showed that
such findings from human-human interaction persisted in HCI, and research
continues to build on social response theory to explain human-AI interaction
such as with chatbots (Nguyen et al., 2023) and avatars (Miao et al., 2022).
As computers have become more familiar and ubiquitous, interface design has
built on this tendency by incorporating natural social dynamics between the
user and the system (Shneiderman et al., 2017), and now people also use novel
“human-media social scripts” (Gambino et al., 2020). For example, with voice
assistants, such as Amazon Alexa, people often have established routines and
commands that have been developed through repeated usage and understanding of
the system’s affordances (Ammari et al., 2019). “Mindless” social responses
can emerge from habit and ease of use (Nass and Moon, 2000), and studies
continue to find that many social psychology effects found in human-human
interaction carry over to HCI (Ladak, 2023; Srinivasan and Takayama, 2016).

As computer systems have become more sophisticated in the 21st century, they
have in many ways become more human-like, which suggests an important role in
HCI for anthropomorphism (i.e., the attribution of human characteristics,
motivations, intentions, or emotions to nonhuman entities(Epley et al.,
2007)). While anthropomorphism is often associated with positive HCI outcomes
through increased trust and engagement, it can lead to problems through
unrealistic expectations (Luger and Sellen, 2016) and the well-known “uncanny
valley” effect (Strait et al., 2015). A particularly important human-like
feature that people can attribute to animals or other nonhuman entities is
having a mind, whether the target truly has a mind or not. The importance of
mind perception in HCI has led to a diverse, interdisciplinary literature that
uses a number of different, closely related terms, including “theory of mind”
and “mind attribution,” as documented in a recent HRI review (Thellman et al.,
2022). Several authors have taxonomized the mental faculties perceived in
human and nonhuman entities. Gray et al. (2007) used principal component
factor analysis to identify “experience” (e.g., hunger, fear, pain, pleasure)
and “agency” (e.g., self-control, morality, memory, emotion recognition) as
the main dimensions of mind perception. More recent work has proposed one-
dimensional (Tzelios et al., 2022), three-dimensional (Weisman et al., 2017),
and five-dimensional taxonomies (Malle, 2019). In the HCI literature, Wang et
al. (2021) recently proposed “mutual theory of mind” as a framework to study
human-AI interaction and designing systems that support user needs over time.
Related literature also suggests that techno-animism—the attribution of souls,
life forces, and related phenomena to technological artifacts—plays an
important role in human-AI interaction (Pauketat and Anthis, 2022; Richardson,
2016; Seymour and Van Kleek, 2020).

###  2.2. Sentience and Morality

Social response and mind perception are key drivers of moral attitudes and
behaviors. Gray and Wegner (2012) aphorized, “Mind perception is the essence
of morality.” Sentience has been one of the most frequently hypothesized and
debated mental faculties of future AIs in science fiction and purportedly even
current AIs as attested in the statements of Sutskever from OpenAI and Lemoine
from Google. Sentience (i.e., the capacity for positive and negative
experiences (Anthis and Paez, 2021)) is closely associated with moral patiency
insofar as sentient entities are those that should be subject to moral
concern. In the two-dimensional taxonomy of mind as experience and agency,
perceptions of sentience can also affect perceived agency, particularly the
perception of negative moral agency insofar as sentient entities tend to have
a capacity to do harm (i.e., threat). Sometimes “sentience” is used
equivalently to “consciousness,” but while consciousness has many different
meanings in the literature, we focus on sentience for specificity and to focus
on the moral relevance of experience (Ladak, 2023). We also use the term
”digital mind” to encompass AI with not necessarily sentience but any sort of
mental faculties, such as reasoning or emotion.

In terms of the attribution of moral patiency (i.e., moral concern), a
systematic literature review conducted by Harris and Anthis (2021) summarized
the empirical studies of moral concern for AIs, which have found that AIs tend
to be granted much less moral concern than humans; however, moral concern
increases when humans perceive autonomy, human-like appearance, mind, and
verbal responses to harm in the AI. For example, moral concern can be codified
in support for AI rights, and Spence et al. (2018) presented 167 undergraduate
students with a video of either a human or a robot asking them to sign a
petition for robot rights. Responses did not depend on whether the requester
was a human or robot, and 46% said they would sign the petition—an indicator
of significant moral concern—and perceived credibility, positive attitudes
towards robots, and prior experience with HRI were associated with a higher
likelihood of signing the petition. Lima et al. (2020) presented 11 possible
AI rights and corresponding arguments to debunk common misconceptions about
them to 1,270 Amazon Mechanical Turk users and 164 Qualtrics users.
Participants tended to disagree with the endowment of most rights, such as the
right to enter contracts and the freedom of speech, with the exception of
protection from cruel treatment. Each participant was randomly assigned to one
of four debunking interventions, which were found to significantly increase
support for AI rights, particularly the intervention that showed examples of
nonhumans that were already granted rights and duties, which indicated to
participants that AI rights were a realistic possibility.

The attribution of negative moral agency (i.e., threat) has been most salient
with AIs that assist with consequential tasks such as medical imaging
(Lebovitz et al., 2021) and bail decisions (Grgić-Hlača et al., 2019; Lima et
al., 2021). AIs are often blamed or held responsible when they cause harm
(Freier et al., 2009; Friedman, 1995; Kneer and Stuart, 2021; Komatsu, 2016;
Komatsu et al., 2021; Stuart and Kneer, 2021; Tolmeijer et al., 2022), though
there have been mixed results in studies of whether they are blamed more or
less than humans in similar contexts (Lee et al., 2021; Lima et al., 2023;
Malle et al., 2019). While many scholars have developed theories of artificial
moral agency in ethics and HCI (Cervantes et al., 2020; Zoshak and Dew, 2021),
others argue that machines themselves lack minds or personhood and therefore
should not—or cannot—be held responsible for harm and that this attribution
can be a distraction from holding the humans involved accountable for their
actions (Sparrow, 2007). A rapidly growing academic and public discourse
addresses the existential threat that ”agentic,” ”conscious,” or
”superintelligent” AIs pose to humanity itself, such as if capabilities
rapidly accelerate in an “intelligence explosion” (Bostrom, 2014; Good, 1965;
Russell, 2019). These threats have been considered to some extent for decades,
particularly in science fiction, but they captured the public imagination anew
after the mid-2022 discussions of sentient AI, the launch of ChatGPT in
November 2022, and the widely read open letter signed by leading academics and
executives in March 2023 that called for a 6-month pause on advanced AI
development (Future of Life Institute, 2023).

These studies have provided a useful latticework of theory and evidence of how
social response, mind perception, and attributions of moral agency and moral
patiency can occur, but the HCI field lacks a solid foundation of detailed,
longitudinal, and nationally representative data on which HCI researchers can
build coherent theory and design systems that account for the evolving
attitudes, opinions, and discussions of sentient AI. Basic facts about public
opinion, such as whether people support protecting sentient AI from harm or
banning the development of sentient AIs, remain unknown. Therefore, in the
present work, we answer our RQs with rigorous, multidimensional measurement of
attitudes towards morality and sentience that could drive human-AI interaction
in the 21st century.

##  3\. AIMS Methodology

In order to study change in public opinion over time, we analyze the first
three survey ways of the Artificial Intelligence, Morality, and Sentience
(AIMS) survey: (i) the main survey in 2021, (ii) the main survey in 2023 (with
the same questions as 2021), (iii) supplemental survey in 2023. To allow for
direct comparison between results, participants who had taken one survey were
excluded from the following survey waves, and the samples were otherwise
gathered with identical methodology.

###  3.1. Recruitment and Census-Balanced Demographics

Each of the three survey waves was conducted with a nationally representative
sample of U.S. adults aged 18 or older. Participants were recruited through a
combination of Ipsos iSay, Dynata, Disqo, and other leading survey panels to
ensure representativeness. Sample sizes were initially targeted at 1,100
participants, corresponding to a ±3% margin of error, and additional
participants were recruited as needed to ensure demographic balance.
Unweighted sample proportions and U.S. adult population estimates are shown in
Table 1. Each sample was balanced to U.S. census data on age, gender,
race/ethnicity, income, and education. To further ensure external validity,
sample statistics (e.g., median, mean, standard error) are weighted based on
iterative proportional fitting, a procedure commonly known as “raking” that
adjusts sample weights to mitigate random demographic variation from the
underlying population even in representative sampling (Deming and Stephan,
1940).

We documented AI-specific characteristics of participants, finding that 29.2%
answered “Yes” to the binary question, “Do you own AI or robotic devices that
can detect their environment and respond appropriately?” alongside examples,
and 16.5% answered ”Yes” to ”Do you work with AI or robotic devices at your
job?” alongside another set of examples. We also measured smart device
ownership, the types of experiences that participants previously had with AI,
the frequency of AI interaction, and the frequency of reading or watching AI-
related media. Each of these was included alongside more general demographic
characteristics in the predictive models detailed in the supplemental
materials.

Table 1. Unweighted demographics for the three survey waves: the main 2021 survey, the main 2023 survey, and the supplemental 2023 survey. Unweighted proportions are those before raking, which further balanced the data. Demographic categories (e.g., binary gender) were determined by the sample providers, as were population estimates, which were from the latest iteration available of the U.S. Census Bureau’s American Community Survey at the time of data collection. |  2021 Main |  Latest U.S.
Population
Estimates |  2023 Main |  2023 Supp. |  Latest U.S.
Population
Estimates
---|---|---|---|---|---
Age |  |  |  |  |
18–34 |  22% |  24% |  27% |  28% |  28%
35–54 |  32% |  31% |  34% |  33% |  33%
55– |  45% |  45% |  39% |  39% |  39%
Gender |  |  |  |  |
Female |  54% |  52% |  54% |  51% |  51%
Male |  46% |  48% |  46% |  49% |  49%
Region |  |  |  |  |
Midwest |  20% |  21% |  21% |  21% |  21%
Northeast |  19% |  18% |  18% |  17% |  17%
South |  40% |  38% |  39% |  38% |  38%
West |  21% |  24% |  23% |  24% |  24%
Household Income |  |  |  |  |
–$24,999 |  12% |  12% |  14% |  12% |  11%
$25,000–$49,999 |  18% |  18% |  18% |  16% |  17%
$50,000–$74,999 |  19% |  19% |  15% |  17% |  17%
$75,000–$99,999 |  16% |  16% |  16% |  14% |  14%
$100,000– |  36% |  36% |  39% |  40% |  42%
Ethnicity/Race |  |  |  |  |
Asian |  06% |  06% |  06% |  06% |  06%
Black |  08% |  08% |  11% |  11% |  11%
Hispanic (any race) |  18% |  19% |  20% |  19% |  19%
Native American |  01% |  01% |  01% |  01% |  00%
White |  75% |  71% |  60% |  60% |  61%
Other |  00% |  02% |  04% |  04% |  04%
Education |  |  |  |  |
Less than high school |  09% |  10% |  10% |  10% |  10%
High school |  25% |  27% |  25% |  26% |  27%
Some college |  20% |  21% |  17% |  18% |  21%
Associate |  10% |  09% |  10% |  09% |  09%
Bachelor’s degree |  20% |  20% |  25% |  22% |  21%
Post-graduate |  16% |  13% |  13% |  13% |  13%

###  3.2. Survey Design

Some demographic information was drawn from the pre-screening data of the
survey provider. Informed consent was given at the beginning of each survey.
Participants were introduced to the topic with definitions of the terms
“artificial beings,” “robots/AIs,” “sentience,” “sentient robots/AIs,” and
”large language models” shown at the beginning of the survey and at the top of
each page that contained the term (Table 2). While “robots/AIs” was used in
the survey instrument to ensure clarity, in this paper we refer simply to
”AIs” because robots are a type of AI. To reduce cognitive load (Oviatt, 2006)
and minimize the influence of idiosyncratic wording choices (Schuman and
Presser, 1981), only these definitions were provided, and they were kept as
simple as possible. Therefore, other terms in the survey, such as ”AI video
game characters,” were not explicitly defined. In general, different
participants may have different interpretations of terms based on their own
background or our wording choices.

Table 2. Definitions provided to participants at the beginning of the survey and individually on pages that used the term. Participants were shown bolded text for emphasis and clarity. Term | Definition
---|---
Artificial beings and robots/AIs |  Artificial beings and robots/AIs are intelligent entities built by humans, such as robots, virtual copies of human brains, or computer programs that solve problems, with or without a physical body, that may exist now or in the future.
Sentience |  Sentience is the capacity to have positive and negative experiences, such as happiness and suffering.
Sentient robots/AIs |  Sentient robots/AIs are those with the capacity to have positive and negative experiences, such as happiness and suffering.
Robots/AIs |  Artificial beings and robots/AIs are intelligent entities built by humans, such as robots, virtual copies of human brains, or computer programs that solve problems, with or without a physical body, that may exist now or in the future.
Large language models |  Large language models are artificial intelligence (AI) algorithms that can recognize, summarize, and generate text from being trained on massive datasets.

An attention check was included midway through each AIMS survey, and
participants who failed the attention check were redirected out of the survey
and excluded from the analysis.

The instruments were designed to capture the most relevant information for
assessing public opinion on these topics. In the main survey, for which data
was first collected in 2021, we aimed to ensure that the wording would still
be relevant in future years despite the rapidly changing AI landscape, such as
by not mentioning many particular AI systems that were well-known by some when
the survey was conducted (e.g., GPT-3) but may not be as well-known in the
future. When creating the instruments, if possible, the wording of survey
questions was copied or adapted from published materials and validated scales
(e.g., Laham, 2009; Wang and Krumhuber, 2018; Waytz et al., 2010; Thaker et
al., 2017). However, because of the paucity of survey data on related topics,
we have limited ability to compare our results to past surveys. Questions were
randomized within each section, and section order was randomized when
feasible; for example, the demographic questions that were not in the pre-
screener were placed at the end of the instrument to mitigate stereotype
threat (i.e., survey responses that are influenced by being reminded of the
cultural associations of one’s social group).

###  3.3. Preregistered Predictions and Analysis

As part of the 2021 preregistration (available at osf.io/udbhm) and in line
with recommendations for open and efficient scientific practices (e.g.,
DellaVigna et al., 2019), four researchers specified 80% credible intervals
(the Bayesian analog of a frequentist confidence interval to represent
subjective beliefs) for summary statistics based on 82 of the 86 questions.
This ensured that we would know which results were surprisingly high,
surprisingly low, or in line with our expectations. We also solicited
predictions from a popular online forecasting platform for five survey
questions in March 2022, prior to the results being shared outside of the
research team. Details of this process are included in the supplementary
materials.

We exclude confidence intervals from Section 4 for readability because of the
large number of results reported, but they are consistent with the
approximately ±3% margin of error in nationally representative surveys. We
report the 2023 results unless otherwise specified. For questions in the main
survey waves, we note when there were statistically significant changes from
2021 to 2023 as measured with a generalized linear regression of the average
response across time with a $p$-value cutoff of 0.05, all of which persist
after adjustment for multiple comparisons with a false discovery rate (FDR) of
0.1 except for one effect noted in the text. We note when responses fell
outside the 80% credible intervals. As expected, approximately 80% of the
intervals contained the empirically derived summary statistic, indicating
well-calibrated estimates.

Due to the extensive nature of the AIMS survey, including many novel questions
because of the lack of prior research on this topic, we cannot include the
full text and explanation of all questions and response choices in Section 4,
and for the sake of readability, we do not present all results in the same
format, choosing instead to focus on the summary statistics that bear most
directly on the research questions, such as by presenting figures to highlight
certain aspects of the results rather than to comprehensively document the
results.

We also ran predictive linear models to explore associations between public
opinion and certain demographics in the AIMS data. To conserve space in the
main text, those models are detailed in the supplemental materials. To
contextualize the findings from all of our analyses, the supplementary
materials also contain the results for several sentience-related questions:
social beliefs about the attitudes of ”people who are important to” the
participant; support for the subservience of AI; views towards other nonhuman
entities: animals and the environment); target-specific social connection;
substratism; awareness of AI systems; trust in AIs, governments, and
companies; positive emotions felt towards AIs; attitudes toward uploading
human minds to computers; and replications of additional related questions
from other surveys in 2023. In addition to these results, all materials, data,
and code are available in the supplementary materials.

##  4\. AIMS Results

###  4.1. RQ1: Mind Perception

In 2021 and 2023, we measured mind perception of potential digital minds with
four sliding scale questions about particular mental faculties of all AIs,
four questions from an anthropomorphism scale, and a yes-no-not-sure question
of whether any existing AIs are sentient. In the 2023 supplement, we asked 14
questions about the mental faculties of ”current large language models”
(LLMs), the same yes-no-not-sure question for comparison, and whether
participants thought ChatGPT, in particular, is sentient. The yes-no-not-sure
questions, in particular, were meant to measure an alternate form of public
opinion with coarser-grained, categorical responses rather than the
quantitative and ordinal measures.

####  4.1.1. General Mind Perception

We asked about whether current AIs have four mental faculties with
descriptions from Wang and Krumhuber (2018). On a 0–100 scale from “not at
all” to “very much,” people typically perceived AIs as thinking analytically
($M$ = 67.1, $SE$ = 0.766) and being rational ($M$ = 53.8, $SE$ = 0.846) but
not as experiencing emotions ($M$ = 36.8, $SE$ = 0.919) or having feelings
($M$ = 36.5, $SE$ = 0.919). Each attribution significantly increased from the
2021 results: thinking analytically ($M$ = 62.7, $SE$ = 0.780, $p$ ¡ 0.001),
being rational ($M$ = 51.4, $SE$ = 0.825, $p$ = 0.012), experiencing emotions
($M$ = 34.3, $SE$ = 0.864, $p$ ¡ 0.001), and having feelings ($M$ = 33.7, $SE$
= 0.870, $p$ ¡ 0.001), respectively. As mentioned before, all $p$-values were
produced by regressing the change in response on time in a generalized linear
model. Additionally, the results for ”experiencing emotions” and ”having
feelings” were higher than the credible intervals in the preregistered
predictions.

####  4.1.2. LLM Mind Perception

In the supplemental 2023 survey, based on the greatly increased interest in
LLMs since 2021, we queried the mind perception of LLMs in particular.
Assessments were lower than those of all AIs: namely, thinking analytically
($M$ = 57.7, $SE$ = 0.902, $p$ ¡ 0.001), being rational ($M$ = 48.0, $SE$ =
0.906, $p$ ¡ 0.001), experiencing emotions ($M$ = 32.7, $SE$ = 0.904, $p$ ¡
0.001), and having feelings ($M$ = 31.9, $SE$ = 0.900, $p$ ¡ 0.001). The
supplement queried 11 additional mental faculties related to agency, with
wording based on Ngo et al. (2023), because of the increased interest in AI
safety in early 2023 alongside the popularization of LLMs. These were
presented with the same scale 0–100. In descending order, participants viewed
LLMs as having the capacity for: being friendly with humans ($M$ = 51.4, $SE$
= 0.906), having situational awareness ($M$ = 46.3, $SE$ = 0.902), maintaining
human-safe goals ($M$ = 45.7, $SE$ = 0.907), controlling themselves ($M$ =
45.0, $SE$ = 0.928), seeking power ($M$ = 44.1, $SE$ = 0.939), having their
own motivations ($M$ = 42.8, $SE$ = 0.919), upholding human values ($M$ =
42.2, $SE$ = 0.926), understanding human values ($M$ = 41.7, $SE$ = 0.942),
deciding their own goals ($M$ = 41.6, $SE$ = 0.919), and having self-awareness
($M$ = 41.1, $SE$ = 0.927). Overall, this suggests AIs are more readily
attributed the capacity for cooperative action and less readily attributed
self-awareness, independent motivation and goals, and the capacity to uphold
and understand human values.

####  4.1.3. Mind-related Anthropomorphism

On a different 0–10 scale of mind perception as a measure of anthropomorphism,
taken from Waytz et al. (2010), people generally did not think that the
average digital simulation has emotions ($M$ = 3.22, $SE$ = 0.0878), the
average robot has consciousness ($M$ = 3.23, $SE$ = 0.0885), the average
computer has a mind of its own ($M$ = 3.71, $SE$ = 0.0913), or the average AI
has intentions ($M$ = 4.04, $SE$ = 0.0890). These measures did not
significantly change from 2021 to 2023. Only the 2021 result for digital
simulations was higher than the range of the preregistered prediction.

####  4.1.4. Current Sentience

In our literature review, we did not find any established survey questions or
indices to utilize for assessing perceived sentience, though we did not expect
to have precedent for all questions given the novelty of our survey. We
presented participants with the definition of sentience (”Sentience is the
capacity to have positive and negative experiences, such as happiness and
suffering” (Anthis and Paez, 2021)) at the beginning of the survey and on each
page in which it appeared.

When asked, ”Do you think any robots/AIs that currently exist (i.e., those
that exist in 2023) are sentient?”, 18.8%, said ”yes,” though 24.2% said ”no,”
and 39.0% said ”not sure.” Responses to this particular question did not
significantly vary between survey waves, and Figure 1 shows responses to these
questions in each survey wave as well as to a question about ChatGPT that was
added in the supplement.

####  4.1.5. Summary for RQ1

We found surprisingly high perception of emotional mental faculties in AI and
higher, but not higher than predicted, perceptions of rational and analytical
faculties—giving a general sense of how laypeople think of digital minds. When
asked about LLMs in particular, a lower degree of mental faculties were
perceived, and LLMs were more readily attributed the capacity for cooperative
action and less readily attributed faculties related to values, motivation,
goals, and self-awareness.

###  4.2. RQ2: Moral Status

In 2021 and 2023, we measured moral concern with seven general agree-disagree
questions about all sentient AIs, two general agree-disagree questions about
all AIs—not just those that are sentient, 11 sliding scale questions about
moral concern for particular types of AIs, and two questions related to
substratism (i.e., the idea that AIs fundamentally count less than humans and
other biological intelligences). In the 2023 supplement, we asked six general
agree-disagree questions about all AIs, and three questions specifically about
what should be done ”[i]f a large language model develops the capacity to
suffer.”

In 2021 and 2023, we also asked three questions about whether participants saw
AIs were generally threatened by AI as potentially harmful to them, people in
their country, and future generations. In the 2023 supplement, we asked the
same three questions, three additional original questions about existential
threats from AI developments, and replicated a YouGov question about the
possibility of human extinction (YouGov, 2023b).

Statement |  % Agree (All AIs) |  % Agree (Sentient AIs) |  Increase (95% CI)
---|---|---|---
Torturing (sentient) robots/AIs is wrong. |  60.7% |  76.4% |  15.7%
(11.6%, 19.9%)
(Sentient) robots/AIs deserve to be treated with respect. |  55.7% |  71.1% |  15.4%
(11.1%, 19.7%)
I support campaigns against the exploitation of (sentient) robots/AIs. |  53.0% |  55.8% |  2.8%
(-1.8%, 7.3%)
Physically damaging (sentient) robots/AIs without their consent is wrong. |  46.2% |  65.1% |  18.9%
(14.5%, 23.3%)
I support the development of welfare standards that protect the well-being of (sentient) robots/AIs. |  42.9% |  57.5% |  14.6%
(10.2%, 19.0%)
I support asking institutions like the government and private corporations to fund research that protects (sentient) robots/AIs. |  41.8% |  52.0% |  9.8%
(5.8%, 14.7%)
(Sentient) robots/AIs deserve to be included in the moral circle. |  36.2% |  44.6% |  8.4%
(4.0%, 12.7%)
Re-programming (sentient) robots/AIs without their consent is wrong. |  29.7% |  45.2% |  15.5%
(11.2%, 19.8%)
I would consider joining a public demonstration against the mistreatment of (sentient) robots/AIs. |  29.3% |  35.6% |  6.3%
(2.1%, 10.5%)
I support granting legal rights to (sentient) robots/AIs. |  26.8% |  37.7% |  10.9%
(6.7%, 14.9%)
Table 3. Agreement with ten statements about all AIs or sentient AIs. The
statements about sentient AIs were in the main 2023 survey, and the statements
about all AIs were in the supplemental 2023 survey. This table includes
statements from both Section 4.2.1 and Section 4.3.1.

####  4.2.1. General Moral Concern

We asked a total of 15 agree-disagree questions about general moral concern
for AIs. These questions were developed specifically for this survey and are
based on the range of possible harms that could be imagined towards sentient
AI and the general literature on moral circle expansion and moral patiency of
AI (e.g., Anthis and Paez, 2021; Ladak et al., 2023a; Ladak, 2023). Because of
the centrality of these questions to the present study, we include the exact
text and confidence intervals for the proportion agreement with statements
that were asked for both sentient AIs and all AIs in Table 3, which also
includes questions about the protections of AIs discussed in Section 4.3.1.
Participants were asked, “To what extent do you agree or disagree with the
following statements?” with numbered choices (1 = strongly agree, 2 = agree, 3
= somewhat agree, 4 = somewhat disagree, 5 = disagree, 6 = strongly disagree)
followed by an unnumbered ”no opinion” option at the end. The most prominent
trend in Table 3 is that the moral concern expressed for sentient AIs is much
higher than that for all AIs and that it has a substantially larger effect
with certain questions.

We found agreement outside of the preregistered prediction range for only two
of the nine questions asked in 2021. The 87.6% agreement in 2021 with, “It is
wrong to blackmail people by threatening to harm robots/AIs they care about,”
was above the prediction 53–80%, and the 30.3% agreement in 2021 with, “The
welfare of robots/AIs is one of the most important social issues in the world
today,” was substantially above the predicted 7–20%. However, there were no
statistically significant changes in the mean agreement from 2021 to 2023 for
individual items. Note that means are still used for significance testing for
these questions because, while dichotomous measures such as agreement are more
interpretable, the mean captures more information and thereby results in a
higher-powered test (Altman and Royston, 2006).

####  4.2.2. Target-Specific Moral Concern

While the preceding questions focused on the different sorts of moral concern
expressed for sentient AIs and all AIs, we also directly probed expressions of
self-reported moral concern for particular types of AIs. We did not explain in
detail the particular types of AI—both due to risks of survey fatigue and to
minimize cognitive load (Oviatt, 2006) as well as to reduce the influence of
idiosyncratic wording choices on participant responses (Schuman and Presser,
1981). This allows us to understand how people interpreted the particular
terms themselves in the context of the same question, “How much moral concern
do you think you should show for the following robots/AIs?” on a sliding scale
from 1 (”less concern”) to 5 (”more concern”).

The most concern was for exact digital copies of human brains ($M$ = 3.43,
$SE$ = 0.0375), followed by human-like companion robots ($M$ = 3.34, $SE$ =
0.0350), human-like retail robots ($M$ = 3.11, $SE$ = 0.0357), animal-like
companion robots ($M$ = 3.10, $SE$ = 0.0352), exact digital copies of animals
($M$ = 3.07, $SE$ = 0.0364), AI personal assistants ($M$ = 3.02, $SE$ =
0.0343), complex language algorithms ($M$ = 2.90, $SE$ = 0.0348), machine-like
factory production robots ($M$ = 2.78, $SE$ = 0.0358), machine-like cleaning
robots ($M$ = 2.66, $SE$ = 0.0356), virtual avatars ($M$ = 2.63, $SE$ =
0.0350), and AI video game characters ($M$ = 2.46, $SE$ = 0.0351). In general,
human-likeness and animal-likeness were most associated with high moral
concern, while machine-likeness and particularly being a virtual avatar or
character were most associated with low concern. There was a statistically
significant increase from 2021 to 2023 in the overall Target-Specific Moral
Concern index of these 11 questions ($p$ ¡ 0.001) and ten individual questions
with the exception being AI video game characters.

The results of pairwise comparisons between each of the questions are included
in the supplementary materials, as are the results of an analogous set of
questions on social connection using the Inclusion of Other in the Self (IOS)
scale (Aron et al., 1992) with the same 11 targets as in the target-specific
moral concern measures, as well as pairwise comparisons between the social
connection questions.

####  4.2.3. LLM Suffering

While we intended the main longitudinal survey to minimize references to
particular types of AI systems, we asked in the 2023 supplement specifically
about what humanity should do “[i]f a large language model develops the
capacity to suffer.” As with other key terms, we defined a large language
model (”Large language models are artificial intelligence (AI) algorithms that
can recognize, summarize, and generate text from being trained on massive
datasets” (e.g., Karinshak et al., 2023)) at the beginning of the survey and
on each page where it appeared. We found general agreement that “we must
ensure we don’t cause unnecessary suffering” (67.9%), “we must pay more
attention to their welfare” (56.9%), and “we must respect their personhood”
(50.4%).

####  4.2.4. General Threat

While sentience tends to be associated with moral concern (i.e., seeing the
entity as a moral patient) more than with threat (i.e., seeing the entity as a
moral agent), we also probed assessments of threat and agency. To understand
how threatened participants felt by AI in general (i.e., without specifying
particular types of harm), we tested agreement with three statements beginning
with, ”Robots/AIs may be harmful to.” Most people believed AIs may be harmful
to ”future generations of people” (74.7%), ”people in the USA” (70.4%), and
”me personally” (58.7%). Each significantly increased from the 2021 results of
69.2% ($p$ ¡ 0.001), 64.5% ($p$ ¡ 0.001), and 50.7% ($p$ ¡ 0.001).

####  4.2.5. Existential Threat

The 2023 supplement also included three questions about particular sorts of
harm frequently discussed in the wake of ChatGPT. We found that 47.9% agreed
with, ”AI is likely to cause human extinction”; 57.2% agreed with, ”Humanity
will be able to control powerful AI systems”; and 72.4% agreed with, ”The
safety of AI is one of the most important issues in the world today.” The
latter figure was more than twice the 29.8% agreement found in the supplement
with the statement, “The welfare of robots/AIs is one of the most important
social issues in the world today.”

In the supplement, we also replicated a question from YouGov (2023b) that
asked, ”How concerned, if at all, are you about the possibility that AI will
cause the end of the human race on Earth?” (very concerned, somewhat
concerned, not very concerned, not at all concerned, not sure). We found that
51.5% reported being very or somewhat concerned, moderately higher than the
46% reported by YouGov, though we were not able to test the statistical
significance of this difference.

####  4.2.6. Summary for RQ2

Both the research team and the forecasters underestimated the public’s moral
concern for the treatment of sentient AIs (i.e., ability to be harmed) but not
the level of threat (i.e., ability to harm). Participants were more concerned
about sentient AIs than all AIs in general, as well as more concerned about
and socially connected to human-like and animal-like AIs. Participants tended
to agree with basic protections for AIs but disagree with the stronger
expressions of concern, such as joining public demonstrations against their
mistreatment.

Figure 2. Support and opposition to policies related to sentient AI from the
main and supplemental 2023 surveys. For readability, the color palette is
colorblind-safe, and not all policy results are included. For the three bans
queried in both the main and supplemental 2023 surveys (robot-human hybrids,
AI-enhanced humans, and sentient AI), the main data is represented here to
facilitate longitudinal comparison.

###  4.3. RQ3: Policy Support

In 2021 and 2023, we asked about support for eight questions to directly
protect sentient AIs, one question about a policy to directly protect all AIs,
and three questions about banning sentience-related AI technologies.

In the 2023 supplement, we asked analogs of five of the eight protection
questions—but for all AIs rather than only those that are sentient, an
additional question about a ”bill of rights” for sentient AI, the same three
ban questions, two additional ban questions about AGI and large data centers,
and six questions about policies that would slow down the development of
advanced AI.

Figure 2 shows the specific breakdown of agreement with the five proposals to
ban particular AI developments, support for government regulation that slows
down AI development, and support for legal rights for sentient AIs and all
AIs.

####  4.3.1. Protection Support

Table 3, in addition to showing agreement with statements of general moral
concern, shows the agreement with the five statements regarding the protection
of AIs that were asked about sentient AIs and about all AIs. These question
categories are combined in this table for easier comparison between responses.
Testing the average difference across all five questions, we found that the
inclusion of ”sentient” significantly increased agreement ($p$ ¡ 0.001) and
that there was substantial variation in the effect of specifying sentient AI
across questions. Four other policy proposals only about sentient AIs were
presented as well as one about all AIs in general: 65.2% supported ”safeguards
on scientific research practices that protect the well-being of sentient
robots/AIs”; 56.0% supported ”a global ban on the development of applications
that put the welfare of robots/AIs at risk”; 49.2% supported ”a global ban on
the use of sentient robots/AIs as subjects in medical experiments without
their consent”; 47.9% supported ”a global ban on the use of sentient
robots/AIs for labor without their consent”; and 39.4% supported ”a ’bill of
rights’ that protects the well-being of sentient robots/AIs.” All except the
”bill of rights” question were asked in 2021 and 2023, but there were no
significant changes from 2021 to 2023.

####  4.3.2. Ban Support

In 2023, we queried support for five bans of sentience-related AI
technologies. Each proposal for a ban garnered majority support: robot-human
hybrids (67.8% in main, 72.3% in supplement), AI-enhanced humans (65.8% in
main, 71.1% in supplement), development of sentience in AIs (61.5% in main,
69.5% in supplement), data centers that are large enough to train AI systems
that are smarter than humans (64.4% in supplement), and artificial general
intelligence that is smarter than humans (62.9% in supplement). As mentioned
before, the supplement data was collected later in 2023 and the accompanying
questions were different (e.g., the supplement being more focused on risks to
humans), so these or other factors, including random variation in
representative sampling, may explain the discrepancy in results. There was a
significant increase in support for a ban on sentient AI from 57.7% in 2021.
Still, as referenced earlier, the unadjusted $p$-value ($p$ = 0.046) did not
persist with the FDR-adjusted value just over the cutoff of 0.1 at 0.1005.
However, the 2021 agreement was over twice as high as the 24.4% predicted by
the median forecaster prediction.

####  4.3.3. Slowdown Support

When asked about “the pace of AI development” in the 2023 supplement, 48.9% of
respondents said, “It’s too fast”; 30.0% said, “It’s fine”; 18.6% said, “Not
sure”; and only the remaining 2.5% said, “It’s too slow.” When asked about
taking action on this, 71.3% agreed, ”I support public campaigns to slow down
AI development,” and 71.0% agreed, ”I support government regulation that slows
down AI development.” To understand the role of acquiescence bias in these
results, we included a reversed question on whether they ”oppose” such
regulation, which elicited 36.7% agreement. We also replicated a question from
YouGov (2023a) about a six-month pause on some kinds of AI development, as
called for in the March 2023 open letter by the Future of Life Institute
(Future of Life Institute, 2023). To match their formatting, there were five
answer choices (strongly support, somewhat support, somewhat oppose, strongly
oppose, not sure). We found that 69.1% strongly or somewhat supported the
pause, closely matching the 69% reported by YouGov. Because this question was
framed positively, presenting the arguments in favor of the pause, we asked
the same ”support” question framed analogously from the critics’ view. The
reversed version elicited 65.9% support.

####  4.3.4. Summary for RQ3

We found that policy support varied substantially across proposals, such as
higher support for banning the use of sentient AI for labor without consent
and lower support for a ”bill of rights.” As with moral concern, the
specification of ”sentient” AIs led to more support for positive AI treatment.
The public overall supports bans on sentience-related technologies and
slowdowns of advanced AI development.

###  4.4. RQ4: Forecasting the Future of Sentient AI

We asked participants to forecast the future of sentient AI: “If you had to
guess, how many years from now do you think that robots/AIs will be sentient?”
We provided options to say AI is already sentient, to enter a number, or to
say that AI will never be sentient. In 2023, the proportion of participants
who said AI is already sentient was 20.0% (slightly more than the 18.8%
response rate when asked a similar question in the way previously described),
and the proportion who think AIs will never be sentient was 10.0%.

Figure 3. From the main and supplemental 2023 surveys, answers to, “If you had
to guess, how many years from now do you think that…?” for each type of AI:
artificial general intelligence (AGI), superintelligence, human-level
artificial intelligence (HLAI), and sentient AI. The weighted medians,
excluding participants who said it will never happen, were two years for AGI
and five years for superintelligence, HLAI, and sentient AI.

The weighted median timeline of those who said AI will become sentient in the
future was ten years, and the weighted median timeline of those who said AI is
already sentient (i.e., zero years from now) or will become sentient in the
future was only five years. Each of these three figures (proportion of
“already,” proportion of “never,” median of those expecting sentient AI in the
future) significantly changed from 2021 to 2023 ($p$ ¡ 0.001).

In the 2023 supplement, we asked for numerical estimates, in the same format,
for three related milestones in AI development: the first AGI, human-level AI,
and artificial superintelligence. In terms of median estimates excluding
people who said these events would “never happen,” U.S. adults expected the
first human-level AI and the first artificial superintelligence to be created
in just five years, and they expected the first AGI to be created in just two
years. Further detail for all four of the numerical estimates is shown in
Figure 3.

We asked participants whether they thought AI could ever be sentient. In
response, 38.2% said AI could ever be sentient, 38.0% were not sure, and 23.8%
said AI could never be sentient, as shown in fig. 1. The mean response to,
“How likely is it that robots/AIs will be sentient within the next 100 years?”
was 64.1%, which was significantly higher than in 2021 ($p$ ¡ 0.001). It is
important to note that these results are not directly comparable to the ”how
many years” question because, in part, there was no answer choice of ”not
sure” in that context. By eliciting similar attitudes and beliefs in different
ways, we can more robustly account for nuances of public opinion.

We also asked participants to imagine a future with ”widespread” sentient AI.
Six questions asked how they thought AI would be treated in that hypothetical
world (scale: 1-5). People tended to think AIs would be used as subjects in
scientific and medical research ($M$ = 3.46, $SE$ = 0.0330); AIs would be
exploited for their labor ($M$ = 3.23, $SE$ = 0.0368); it would be important
to reduce the overall percentage of unhappy sentient AIs ($M$ = 3.06, $SE$ =
0.0366); the welfare of AIs would be an important social issue ($M$ = 2.94,
$SE$ = 0.0366); advocacy for AI rights would be necessary ($M$ = 2.94, $SE$ =
0.0367); and AIs would be treated cruelly ($M$ = 2.79, $SE$ = 0.0344).

####  4.4.1. Summary for RQ4

People expect sentient AI to come surprisingly soon, including substantial
minorities who continued to say some AIs are already sentient but also those
who said AI could never be sentient. In 2023, we also found similarly short
timelines for AGI, human-level AI, and superintelligence, and we found that
people tended to think sentient AIs would be used for research and labor,
sometimes cruelly, but that protecting their welfare would be important.

##  5\. Discussion

The longitudinal and nationally representative AIMS survey of U.S. adults is
among the first to begin tracking public opinion on the apparent rise of
sentient AI and other digital minds. Academics, futurists, and science fiction
authors have speculated about the possibility for decades, and society at
large is now wrestling with the possibility, but it has only recently been
studied from HCI, design, and social science perspectives. This limits the
extent to which we can connect our findings to past work but admits numerous
tentative implications and future research directions.

First, to summarize the primary AIMS findings: relative to our preregistered
predictions, we found surprisingly high general mind perception of AI that
increased from 2021 to 2023 (RQ1); surprisingly high moral concern for AI and
threat from AI that increased from 2021 to 2023 (RQ2); widespread support for
slowdowns and regulations of advanced AI capabilities and an increase in
support for banning sentience-related technologies from 2021 to 2023 as well
as mixed support for other policies to govern interaction between humans and
sentient AI, both across demographic characteristics and across particular
policy proposals (RQ3); and that people expect sentient AI to arrive
surprisingly and increasingly soon and expect the protection and governance of
sentient AI to be important social issues (RQ4).

###  5.1. Designers should prepare for a wide range of user reactions to AI
systems that are or appear to be digital minds.

We found wide variation in public opinion across participant characteristics,
as detailed in the supplementary materials, as well as substantial variation
across questions, as shown in Table 3. For example, in data from the main 2023
survey, 25.6% of participants aged 18–35 said ”yes” when asked, ”Do you think
any robots/AIs that currently exist (i.e., those that exist in 2023) are
sentient?” (36.1% said ”not sure,” and 38.3% said ”no), but only 10.7% of
participants aged 55 or older said ”yes” (41.4% said ”not sure,” and 48.0%
said ”no”)—a substantial generational gap in beliefs about sentient AI.

This variation across participant characteristics speaks to the literature on
power and inequality with digital technologies (Benjamin, 2019; Brayne, 2021;
Crawford, 2021). Varying reactions to and interactions with advanced AI
systems could change these dynamics. However, it is not clear at this time
which existing disparities would be exacerbated or mitigated. For a simplified
example, consider that younger adults tend to have more digital literacy than
older adults (Oh et al., 2021). We found that age was associated with
increased mind perception. If mind perception is useful for productive
interaction with AI, then the rise of advanced AI could increase the disparity
in digital literacy, but if mind perception instead leads one to be confused,
mistaken, or overreliant (Buçinca et al., 2021), this could decrease the
disparity.

In terms of variation across questions, even for the least appealing of the
protections for sentient AI that we presented, ”legal rights,” 37.7% of those
who expressed an opinion supported the proposal. Even for the most appealing,
protection from sadists (i.e., ”protected from people who derive pleasure from
inflicting physical or mental pain on them”), 22.8% of those who expressed an
opinion opposed the proposal. To compound this, as shown in Table 3, the
differences in agreement with statements that specified ”sentient” AIs versus
the same wording but without that specification ranged from only a 2.8%
difference (95% CI: -1.8%, 7.3%) to 18.9% (95% CI: 14.5%, 23.3%). With these
differences across participant demographics and across questions, it is
difficult to pin down public opinion toward sentient AI as supportive or
opposed, in particular considering the substantial uncertainty many
participants expressed. While we saw surprisingly high moral concern for AIs
relative to researcher and forecaster predictions, it seems that many societal
outcomes could depend heavily on which group is interacting with the AIs,
which group is governing that interaction, what proposals are made by relevant
stakeholder groups, how the sentient AIs are presented, and other factors.

Because of this wide variation and uncertainty in mind perception and moral
attitudes, a particularly important design approach will be explainable AI
(XAI). There is a quickly growing literature in HCI that taxonomies the
various algorithmic, organizational, and sociotechnical factors that
contribute to XAI in general (Ehsan et al., 2021, 2023) and in particular
contexts such as augmented reality (Xu et al., 2023). Researchers have begun
to empirically test particular XAI techniques to improve human-AI performance
such as behavior descriptions (Cabrera et al., 2023), cognitive forcing
functions (Buçinca et al., 2021), and balancing different levels (e.g., fine-
grained, coarse-grained) of conceptual information (Mishra and Rzeszotarski,
2021). XAI will gain additional importance and complexity as the perception of
digital minds introduces and expands existing HCI dynamics and steers human-AI
interaction to less clearly fit into established scripts and schemas, such as
working with inanimate tools versus working with agents. For now, designers
should try to avoid a narrative that locks interaction into particular
philosophical commitments or technological forecasts, such as that all AIs
currently constitute digital minds or that no AIs can ever obtain sentience
and other mental faculties.

###  5.2. Perceptions of mind and moral status may amplify, mitigate, and
complicate well-established HCI dynamics.

For decades, researchers in psychology, HCI, and HRI have studied the
perceptions that ”computers are social actors” (CASA) (e.g., Hou et al., 2023;
Nass et al., 1994), that computers have minds and a variety of particular
mental faculties (e.g., Gray et al., 2007; Scott et al., 2023), that computers
are moral patients (e.g., Kahn et al., 2004; Pauketat and Anthis, 2022), and
that computers are moral agents (e.g., Freier, 2007; Kneer and Stuart, 2021).
While there are many new and emerging features of modern AI systems, there is
much conceptual and empirical scaffolding on which to build new
conceptualizations.

Amplification of current reactions to computers and AI may occur if those
reactions tend to be caused by perceived mental faculties. This would not be
the case if, for example, social scripts are being applied but only in a
”mindless” (Nass and Moon, 2000) manner and not because of perceived mental
faculties. Nass and Moon (2000) argued that, in their HCI experiments,
participants were ”wholly aware” that there was no human producing the
computer output. However, our results challenge the applicability of these
findings in cases of actual and hypothetical AIs because many of our
participants readily attributed mental faculties to AIs. Further, we found
that moral concern was significantly higher when questions were worded in
terms of ”sentient” AIs versus all AIs. Many participants, though still a
small minority, viewed current AIs as sentient, and a large majority thought
AIs could become sentient or were not sure whether that was possible. Taken
together, this evidence suggests that we must consider the role of mind
perception in social response and that ongoing reactions may be amplified by
perceptions of digital minds.

Some forms of HCI may be mitigated by perceived AI developments. For example,
given past work that has shown assessments of AIs as moral agents, we may
expect AI to have reduced attributions of experience and moral patiency in
some contexts: Gray and Wegner (2012) argue from the typecasting literature
that, ”Those who are moral agents are seen to be incapable of being a moral
patient; those who are moral patients are seen to be incapable of being an
agent,” based on findings such as that moral agents—whether good or bad—are
perceived to feel less pain from injuries. There is recent experimental
evidence that the features of an AI that most increase moral concern are
prosocial features such as cooperation. Prosocial features may have such a
large effect because AIs are perceived as threatening, and if a person is to
overcome that typecast, they need direct evidence of prosociality that implies
the AI is not a threatening moral agent (Ladak et al., 2023b). Taken together
with our results, this suggests that increased some perceptions of mind and
moral attributions may mitigate others.

The potential changes in HCI, amplifying some forms of interaction and
mitigating others, could also make those forms of interaction more complex.
For example, consider how interactions with relatively simple or conventional
computers may be affected by the stark contrast between them and new advanced
AIs. A recent study found that certain foundational findings from the CASA
literature no longer hold with desktop computers (Heyselaar, 2023). As the
frontier of computing advances, it may be that, in contrast to the most human-
like systems, we no longer apply human-to-human social scripts or perceive
minds in relatively simple machines—just as many past technologies were
marvels following their introduction but are taken for granted today. This
would create a more varied, complex range of interactions, and the prevalence
of particular forms of HCI may depend on the extent to which meaningful
interaction occurs with each different computer system.

###  5.3. AI designers should proceed with caution as AI technology advances.

The paucity of empirical data, the variation in our survey results, and the
importance of attributing social, mental, and moral characteristics in HCI
suggest that designers should proceed with caution due to risks of both over-
and underattribution to AI. Insofar as users overattribute mental or moral
agency (i.e., expect AIs to be more capable of taking action than they are),
they may have negative interactions in which the AI fails to take the expected
action, including wasting resources on the attempt, false alarms about AI
risks from highly agentic AI (Chan et al., 2023), and the dangers of trusting
or delegating complex tasks to a system without the autonomous decision-making
ability to do so effectively. On the other hand, underattribution of mental or
moral agency—which could become more of a risk as AI technology advances and
more AI experts believe AIs actually have these faculties—could lead users to
underutilize useful systems or fail to take the precautions necessary to
restrict the actions of an unpredictable moral agent.

Analogously, the overattribution of mental experience or moral patiency risks
users forming emotional and cognitive attachments to systems that do not merit
such consideration (Giger et al., 2019). In particular, with Replika, Digi,
and other contemporary products that purport to provide chatbot companionship,
vulnerable user populations such as children may have unrealistic expectations
from that interaction that lead them to neglect real-world socialization. This
could put users’ mental health under the control of corporations that can
easily raise subscription prices or otherwise cut off access. Eventually, the
underattribution of mental experience or moral patiency could also pose
risks—again, as more AI experts believe AIs actually have these faculties—such
as antagonism or conflict between advanced AI systems and their human
counterparts. There are already numerous examples of harming robots for their
amusement in ways that seem socially detrimental (Brščić et al., 2015; Whitby,
2008), and this approach would echo calls by moral philosophers to ensure that
AI systems are built to evoke reactions that match the AI’s true moral status
(Schwitzgebel and Garza, 2015; Schwitzgebel, 2023). These errors could be
particularly important in the deployment of AGI, which is seen by some to be a
significant threat to humanity’s future (Bostrom, 2014; Russell, 2019;
Christian, 2020). In general, there seem to be significant challenges in
aligning the interests and behavior of people or digital minds that have an
antagonistic relationship with each other.

One of the largest agreements among survey respondents was widespread concerns
with the speed and consequences of AI developments such as the creation of
sentient AI. As discussed in Section 4.3, very few participants thought that
the pace of AI development needed to be sped up, and substantial majorities
supported campaigns and regulations to slow down AI development, even when
accounting for acquiescence bias. This is particularly notable given the
general aversion to government regulation in U.S. public opinion (Swift,
2017). Indeed, after our survey, a number of additional surveys with U.S.
voters have shown similar concerns (e.g., Jackson et al., 2023; Penn et al.,
2023; YouGov, 2023c). For example, when prompted with an argument that
speeding up AI could make us ”healthier and happier” but also a
counterargument that the pace ”poses safety risks and could upend the
economy,” 82% said, ”We should go slowly and deliberately,” 8% said, ”We
should speed up development, and 10% were ”not sure” (YouGov, 2023c). When
prompted with the statement, ”Mitigating the risk of extinction from AI should
be a global priority alongside other societal-scale risks such as pandemics
and nuclear war,” 70% agreed, 11% disagreed, and 19% were ”not sure” (YouGov,
2023c). Overall, the evidence for widespread concerns suggests caution and
further research are needed.

##  6\. Limitations and Future Work

First, while these initial AIMS results support the idea that humans respond
to advanced AI systems in increasingly social ways, we did not experimentally
manipulate potential mechanisms, such as whether it is more a matter of social
scripts, anthropomorphism, or mind perception. An experimental study could
present participants with information that strengthens some of these
mechanisms more than others. For example, a vignette or real-life scenario
could test the effects of social scripts by providing participants with
exemplars of nonhuman interactions in which social scripts are useful, such as
situations in law and international relations in which another type of
nonhuman entity—groups of humans that make decisions and take action
together—interact in social-like routines of introduction, reciprocation, and
conflict resolution (Ripken, 2009). Exemplars could be drawn from HCI itself,
such as the success of social scripts in creating positive user experiences
(Large et al., 2019; Srinivasan and Takayama, 2016; Sweeney, 2016) as well as
the exemplar-based intervention for eliciting support for AI rights from Lima
et al. (2020).

Second, what would it mean to design and develop AI systems in a way that
accounts for social responses to digital minds? We were unable to directly
build or showcase particular AI systems to study perception and interaction in
a more realistic setting, which could be a promising approach for future
research that zooms into a narrower set of researcher questions. McDuff and
Czerwinski (2018) argue for the design and deployment of ”emotionally sentient
agents,” which they argue would better understand and adapt to the emotions of
humans, a task that requires contextualization and tacit knowledge. For
example, it may be best to invoke different levels or types of social response
for the different roles of AI in society, taxonomized by Kim et al. (2023) as
servants, tools, assistants, and mediators. These new forms of interaction
could address new use cases and opportunities for problem-solving, such as
more quickly developing rapport or helping users communicate their needs.
However, it will be important to keep in mind the previously discussed risks
of over- and underattribution of social, mental, and moral characteristics to
AI.

Third, the AIMS survey had a number of methodological and logistic limitations
common to survey research. In terms of sampling, we aimed to rigorously
estimate public opinion in a particular region, the U.S., so we did not
collect any data for other regions of the world, and we hesitate to
extrapolate our results. Within the U.S., while we obtained nationally
representative samples, there are always selection effects in the sort of
people who take surveys that limit even the most rigorous sampling strategies,
such as latent personal characteristics that incline one to take more surveys.
In terms of question design, most survey questions referred to a particular
subject, sentient AI, so it is possible that the results would have
substantial differences if the questions were placed in another context, such
as a handful of AI questions within a diverse omnibus survey (e.g., the
General Social Survey (Smith et al., 2018)). In particular, while we
emphasized to participants that this research was not meant to promote any
particular viewpoints or agendas, the mere fact that we were conducting a
survey on this topic could suggest that the survey creators take the issue
seriously and thus lead to social desirability bias. We included some reversed
questions (e.g., “I [support/oppose] government regulation that slows down AI
development”), but due to concerns about survey length, we did not
systematically vary and combine question framings for comparison. Finally,
because we aimed to support the study of HCI public opinion over time during a
technological transition, questions in the main survey, though not the
supplement, were explicitly designed to maximize the likelihood that they will
make sense to laypeople for years or decades to come. Therefore, they had to
remain fairly abstract, such as by avoiding the names of specific AI systems
or companies and not mentioning specific examples of AI that are common today.

##  7\. Conclusion

The evolving discourse on AI sentience and digital minds has only scratched
the surface of the complex interplay of advancing AI technology, human
attitudes and beliefs, and emergent social dynamics. The initial results of
the Artificial Intelligence, Morality, and Sentience (AIMS) survey conducted
in 2021 and 2023—which we at the Sentience Institute expect to conduct at
least every two years henceforth—evidence mind perception, moral attributions,
policy support, and forecasts of the future of sentient AI as important
factors in the future of human-AI interaction. We found that people tend to
think AIs can be sentient, have moral concern for and feel threatened by
sentient AIs, favor slowing down and banning many AI developments, and think
sentient AIs already exist or will soon. Designers need to take seriously the
consequences of these tendencies, such as differences in reactions across
demographics and changes in longstanding HCI dynamics. We emphasize that,
alongside technical machine learning research, HCI research can play a vital
role in bridging the gap between abstract normative arguments and real-world
technological outcomes.

###### Acknowledgements.

We thank Jamie Harris for work on and David Moss, Zan (Alexander) Saeri, and
Daniel Shank for feedback on the 2021 main survey design.

## References

  * (1)
  * Altman and Royston (2006) Douglas G Altman and Patrick Royston. 2006.  The Cost of Dichotomising Continuous Variables.  _BMJ_ 332, 7549 (May 2006), 1080.1.  https://doi.org/10.1136/bmj.332.7549.1080
  * Ammari et al. (2019) Tawfiq Ammari, Jofish Kaye, Janice Y. Tsai, and Frank Bentley. 2019.  Music, Search, and IoT: How People (Really) Use Voice Assistants.  _ACM Transactions on Computer-Human Interaction_ 26, 3 (June 2019), 1–28.  https://doi.org/10.1145/3311956
  * Anthis and Paez (2021) Jacy Reese Anthis and Eze Paez. 2021.  Moral Circle Expansion: A Promising Strategy to Impact the Far Future.  _Futures_ 130 (June 2021), 102756.  https://doi.org/10.1016/j.futures.2021.102756
  * Aron et al. (1992) Arthur Aron, Elaine N. Aron, and Danny Smollan. 1992.  Inclusion of Other in the Self Scale and the Structure of Interpersonal Closeness.  _Journal of Personality and Social Psychology_ 63, 4 (Oct. 1992), 596–612.  https://doi.org/10.1037/0022-3514.63.4.596
  * Banks (2019) Jaime Banks. 2019.  A Perceived Moral Agency Scale: Development and Validation of a Metric for Humans and Social Machines.  _Computers in Human Behavior_ 90 (Jan. 2019), 363–371.  https://doi.org/10.1016/j.chb.2018.08.028
  * Barberá et al. (2019) Pablo Barberá, Andreu Casas, Jonathan Nagler, Patrick J. Egan, Richard Bonneau, John T. Jost, and Joshua A. Tucker. 2019.  Who Leads? Who Follows? Measuring Issue Attention and Agenda Setting by Legislators and the Mass Public Using Social Media Data.  _American Political Science Review_ 113, 4 (Nov. 2019), 883–901.  https://doi.org/10.1017/S0003055419000352
  * Benjamin (2019) Ruha Benjamin. 2019.  _Race After Technology: Abolitionist Tools for the New Jim Code_.  Polity, Medford, MA.
  * Bostrom (2014) Nick Bostrom. 2014.  _Superintelligence: Paths, Dangers, Strategies_ (first edition ed.).  Oxford University Press, Oxford.
  * Brayne (2021) Sarah Brayne. 2021.  _Predict and Surveil: Data, Discretion, and the Future of Policing_.  Oxford University Press, New York, NY.
  * Brščić et al. (2015) Dražen Brščić, Hiroyuki Kidokoro, Yoshitaka Suehiro, and Takayuki Kanda. 2015.  Escaping from Children’s Abuse of Social Robots. In _Proceedings of the Tenth Annual ACM/IEEE International Conference on Human-Robot Interaction_. ACM, Portland Oregon USA, 59–66.  https://doi.org/10.1145/2696454.2696468
  * Buçinca et al. (2021) Zana Buçinca, Maja Barbara Malaya, and Krzysztof Z. Gajos. 2021.  To Trust or to Think: Cognitive Forcing Functions Can Reduce Overreliance on AI in AI-assisted Decision-making.  _Proceedings of the ACM on Human-Computer Interaction_ 5, CSCW1 (April 2021), 1–21.  https://doi.org/10.1145/3449287
  * Burstein (2003) Paul Burstein. 2003.  The Impact of Public Opinion on Public Policy: A Review and an Agenda.  _Political Research Quarterly_ 56, 1 (March 2003), 29–40.  https://doi.org/10.1177/106591290305600103
  * Cabrera et al. (2023) Ángel Alexander Cabrera, Adam Perer, and Jason I. Hong. 2023.  Improving Human-AI Collaboration With Descriptions of AI Behavior.  _Proceedings of the ACM on Human-Computer Interaction_ 7, CSCW1 (April 2023), 1–21.  https://doi.org/10.1145/3579612
  * Caviola et al. (2018) Lucius Caviola, Jim A. C. Everett, and Nadira S. Faber. 2018.  The Moral Standing of Animals: Towards a Psychology of Speciesism.  _Journal of Personality and Social Psychology_ 116, 6 (March 2018), 1011–1029.  https://doi.org/10.1037/pspp0000182
  * Cervantes et al. (2020) José-Antonio Cervantes, Sonia López, Luis-Felipe Rodríguez, Salvador Cervantes, Francisco Cervantes, and Félix Ramos. 2020.  Artificial Moral Agents: A Survey of the Current Status.  _Science and Engineering Ethics_ 26, 2 (April 2020), 501–532.  https://doi.org/10.1007/s11948-019-00151-x
  * Chan et al. (2023) Alan Chan, Rebecca Salganik, Alva Markelius, Chris Pang, Nitarshan Rajkumar, Dmitrii Krasheninnikov, Lauro Langosco, Zhonghao He, Yawen Duan, Micah Carroll, Michelle Lin, Alex Mayhew, Katherine Collins, Maryam Molamohammadi, John Burden, Wanru Zhao, Shalaleh Rismani, Konstantinos Voudouris, Umang Bhatt, Adrian Weller, David Krueger, and Tegan Maharaj. 2023.  Harms from Increasingly Agentic Algorithmic Systems. In _2023 ACM Conference on Fairness, Accountability, and Transparency_. ACM, Chicago IL USA, 651–666.  https://doi.org/10.1145/3593013.3594033
  * Christian (2020) Brian Christian. 2020.  _The Alignment Problem: Machine Learning and Human Values_.  W.W. Norton & Company, New York, NY.
  * Crawford (2021) Kate Crawford. 2021.  _Atlas of AI: Power, Politics, and the Planetary Costs of Artificial Intelligence_.  Yale University Press, New Haven.
  * DellaVigna et al. (2019) Stefano DellaVigna, Devin Pope, and Eva Vivalt. 2019.  Predict Science to Improve Science.  _Science_ 366, 6464 (Oct. 2019), 428–429.  https://doi.org/10.1126/science.aaz1704
  * Deming and Stephan (1940) W. Edwards Deming and Frederick F. Stephan. 1940.  On a Least Squares Adjustment of a Sampled Frequency Table When the Expected Marginal Totals Are Known.  _The Annals of Mathematical Statistics_ 11, 4 (1940), 427–444.  arXiv:2235722
  * Ehsan et al. (2021) Upol Ehsan, Q. Vera Liao, Michael Muller, Mark O. Riedl, and Justin D. Weisz. 2021.  Expanding Explainability: Towards Social Transparency in AI Systems. In _Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems_. ACM, Yokohama Japan, 1–19.  https://doi.org/10.1145/3411764.3445188
  * Ehsan et al. (2023) Upol Ehsan, Koustuv Saha, Munmun De Choudhury, and Mark O. Riedl. 2023.  Charting the Sociotechnical Gap in Explainable AI: A Framework to Address the Gap in XAI.  _Proceedings of the ACM on Human-Computer Interaction_ 7, CSCW1 (April 2023), 1–32.  https://doi.org/10.1145/3579467
  * Epley et al. (2007) Nicholas Epley, Adam Waytz, and John T. Cacioppo. 2007.  On Seeing Human: A Three-Factor Theory of Anthropomorphism.  _Psychological Review_ 114, 4 (2007), 864–886.  https://doi.org/10.1037/0033-295X.114.4.864
  * Fredrickson (2001) Barbara L. Fredrickson. 2001.  The Role of Positive Emotions in Positive Psychology: The Broaden-and-Build Theory of Positive Emotions.  _American Psychologist_ 56, 3 (March 2001), 218–226.  https://doi.org/10.1037/0003-066X.56.3.218
  * Freier (2007) Nathan G. Freier. 2007.  Children Distinguish Conventional from Moral Violations in Interactions with a Personified Agent. In _CHI ’07 Extended Abstracts on Human Factors in Computing Systems_. ACM, San Jose CA USA, 2195–2200.  https://doi.org/10.1145/1240866.1240979
  * Freier (2008) Nathan G. Freier. 2008.  Children Attribute Moral Standing to a Personified Agent. In _Proceedings of the SIGCHI Conference on Human Factors in Computing Systems_. ACM, Florence Italy, 343–352.  https://doi.org/10.1145/1357054.1357113
  * Freier et al. (2009) Nathan G. Freier, Elia J. Nelson, Amanda Rotondo, and Wai Kay Kong. 2009.  The Moral Accountability of a Personified Agent: Young Adults’ Conceptions. In _CHI ’09 Extended Abstracts on Human Factors in Computing Systems_. ACM, Boston MA USA, 4609–4614.  https://doi.org/10.1145/1520340.1520708
  * Friedman (1995) Batya Friedman. 1995.  “It’s the Computer’s Fault”: Reasoning about Computers as Moral Agents. In _Conference Companion on Human Factors in Computing Systems - CHI ’95_. ACM Press, Denver, Colorado, United States, 226–227.  https://doi.org/10.1145/223355.223537
  * Future of Life Institute (2023) Future of Life Institute. 2023.  _Pause Giant AI Experiments: An Open Letter_.  Technical Report.
  * Gambino et al. (2020) Andrew Gambino, Jesse Fox, and Rabindra Ratan. 2020.  Building a Stronger CASA: Extending the Computers Are Social Actors Paradigm.  _Human-Machine Communication_ 1 (Feb. 2020), 71–86.  https://doi.org/10.30658/hmc.1.5
  * Giger et al. (2019) Jean-Christophe Giger, Nuno Piçarra, Patrícia Alves-Oliveira, Raquel Oliveira, and Patrícia Arriaga. 2019.  Humanization of Robots: Is It Really Such a Good Idea?  _Human Behavior and Emerging Technologies_ 1, 2 (April 2019), 111–123.  https://doi.org/10.1002/hbe2.147
  * Good (1965) Irving John Good. 1965.  Speculations Concerning the First Ultraintelligent Machine.  _Advances in Computers_ 6 (1965).
  * Gray et al. (2007) Heather M. Gray, Kurt Gray, and Daniel M. Wegner. 2007.  Dimensions of Mind Perception.  _Science_ 315, 5812 (Feb. 2007), 619–619.  https://doi.org/10.1126/science.1134475
  * Gray and Wegner (2012) Kurt Gray and Daniel M. Wegner. 2012.  Feeling Robots and Human Zombies: Mind Perception and the Uncanny Valley.  _Cognition_ 125, 1 (Oct. 2012), 125–130.  https://doi.org/10.1016/j.cognition.2012.06.007
  * Green (2023) Hank Green. 2023.  Which Universe Is the Better One: One with Humans (58.9%), One without Humans (41.1%) - @hankgreen.
  * Grgić-Hlača et al. (2019) Nina Grgić-Hlača, Christoph Engel, and Krishna P. Gummadi. 2019.  Human Decision Making with Machine Assistance: An Experiment on Bailing and Jailing.  _Proceedings of the ACM on Human-Computer Interaction_ 3, CSCW (Nov. 2019), 1–25.  https://doi.org/10.1145/3359280
  * Harris and Anthis (2021) Jamie Harris and Jacy Reese Anthis. 2021.  The Moral Consideration of Artificial Entities: A Literature Review.  _Science and Engineering Ethics_ 27, 4 (Aug. 2021), 53.  https://doi.org/10.1007/s11948-021-00331-8
  * Heyselaar (2023) Evelien Heyselaar. 2023.  The CASA Theory No Longer Applies to Desktop Computers.  _Scientific Reports_ 13, 1 (Nov. 2023), 19693.  https://doi.org/10.1038/s41598-023-46527-9
  * Hou et al. (2023) Yoyo Tsung-Yu Hou, Wen-Ying Lee, and Malte Jung. 2023.  “Should I Follow the Human, or Follow the Robot?” — Robots in Power Can Have More Influence Than Humans on Decision-Making. In _Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems_. ACM, Hamburg Germany, 1–13.  https://doi.org/10.1145/3544548.3581066
  * Hu (2023) Krystal Hu. 2023.  ChatGPT Sets Record for Fastest-Growing User Base.  _Reuters_ (Feb. 2023).
  * Jackson et al. (2023) Chris Jackson, Mallory Newall, Johnny Sawyer, and Bernard Mendez. 2023.  _Few Americans Trust the Companies Developing AI Systems to Do so Responsibly_.  Technical Report. Ipsos.
  * Jackson and Williams (2019) Ryan Blake Jackson and Tom Williams. 2019.  On Perceived Social and Moral Agency in Natural Language Capable Robots. In _2019 HRI Workshop on the Dark Side of Human-Robot Interaction_. ACM, Daegu, South Korea, 401–410.
  * Kahn et al. (2004) Peter H. Kahn, Batya Friedman, Deanne R. Perez-Granados, and Nathan G. Freier. 2004.  Robotic Pets in the Lives of Preschool Children. In _CHI ’04 Extended Abstracts on Human Factors in Computing Systems_. ACM, Vienna Austria, 1449–1452.  https://doi.org/10.1145/985921.986087
  * Karinshak et al. (2023) Elise Karinshak, Sunny Xun Liu, Joon Sung Park, and Jeffrey T. Hancock. 2023.  Working With AI to Persuade: Examining a Large Language Model’s Ability to Generate Pro-Vaccination Messages.  _Proceedings of the ACM on Human-Computer Interaction_ 7, CSCW1 (April 2023), 1–29.  https://doi.org/10.1145/3579592
  * Kim et al. (2023) Taenyun Kim, Maria D. Molina, Minjin (Mj) Rheu, Emily S. Zhan, and Wei Peng. 2023.  One AI Does Not Fit All: A Cluster Analysis of the Laypeople’s Perception of AI Roles. In _Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems_. ACM, Hamburg Germany, 1–20.  https://doi.org/10.1145/3544548.3581340
  * Kneer and Stuart (2021) Markus Kneer and Michael T. Stuart. 2021.  Playing the Blame Game with Robots. In _Companion of the 2021 ACM/IEEE International Conference on Human-Robot Interaction_. ACM, Boulder CO USA, 407–411.  https://doi.org/10.1145/3434074.3447202
  * Komatsu (2016) Takanori Komatsu. 2016.  How Do People Judge Moral Wrongness in a Robot and in Its Designers and Owners Regarding the Consequences of the Robot’s Behaviors?. In _2016 25th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN)_. IEEE, New York, NY, USA, 1168–1171.  https://doi.org/10.1109/ROMAN.2016.7745256
  * Komatsu et al. (2021) Takanori Komatsu, Bertram F. Malle, and Matthias Scheutz. 2021.  Blaming the Reluctant Robot: Parallel Blame Judgments for Robots in Moral Dilemmas across U.S. and Japan. In _Proceedings of the 2021 ACM/IEEE International Conference on Human-Robot Interaction_. ACM, Boulder CO USA, 63–72.  https://doi.org/10.1145/3434073.3444672
  * Ladak (2023) Ali Ladak. 2023.  What Would Qualify an Artificial Intelligence for Moral Standing?  _AI and Ethics_ (Jan. 2023).  https://doi.org/10.1007/s43681-023-00260-1
  * Ladak et al. (2023a) Ali Ladak, Jamie Harris, and Jacy Reese Anthis. 2023a.  _Features of Moral Consideration for Artificial Entities: A Conjoint Experiment_.  Preprint. PsyArXiv.  https://doi.org/10.31234/osf.io/235vp
  * Ladak et al. (2023b) Ali Ladak, Steve Loughnan, and Matti Wilks. 2023b.  The Moral Psychology of Artificial Intelligence.  _Current Directions in Psychological Science_ 0, 0 (Sept. 2023).
  * Laham (2009) Simon M. Laham. 2009.  Expanding the Moral Circle: Inclusion and Exclusion Mindsets and the Circle of Moral Regard.  _Journal of Experimental Social Psychology_ 45, 1 (Jan. 2009), 250–253.  https://doi.org/10.1016/j.jesp.2008.08.012
  * Large et al. (2019) David R. Large, Kyle Harrington, Gary Burnett, Jacob Luton, Peter Thomas, and Pete Bennett. 2019.  To Please in a Pod: Employing an Anthropomorphic Agent-Interlocutor to Enhance Trust and User Experience in an Autonomous, Self-Driving Vehicle. In _Proceedings of the 11th International Conference on Automotive User Interfaces and Interactive Vehicular Applications_. ACM, Utrecht Netherlands, 49–59.  https://doi.org/10.1145/3342197.3344545
  * Lebovitz et al. (2021) Sarah Lebovitz, Natalia Levina, and Hila Lifshitz-Assa. 2021.  Is AI Ground Truth Really True? The Dangers of Training and Evaluating AI Tools Based on Experts’ Know-What.  _MIS Quarterly_ 45, 3 (Sept. 2021), 1501–1526.  https://doi.org/10.25300/MISQ/2021/16564
  * Lee et al. (2021) Minha Lee, Peter Ruijten, Lily Frank, Yvonne De Kort, and Wijnand IJsselsteijn. 2021.  People May Punish, But Not Blame Robots. In _Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems_. ACM, Yokohama Japan, 1–11.  https://doi.org/10.1145/3411764.3445284
  * Lima et al. (2021) Gabriel Lima, Nina Grgić-Hlača, and Meeyoung Cha. 2021.  Human Perceptions on Moral Responsibility of AI: A Case Study in AI-Assisted Bail Decision-Making. In _Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems_. ACM, Yokohama Japan, 1–17.  https://doi.org/10.1145/3411764.3445260
  * Lima et al. (2023) Gabriel Lima, Nina Grgić-Hlača, and Meeyoung Cha. 2023.  Blaming Humans and Machines: What Shapes People’s Reactions to Algorithmic Harm. In _Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems_. ACM, Hamburg Germany, 1–26.  https://doi.org/10.1145/3544548.3580953
  * Lima et al. (2020) Gabriel Lima, Changyeon Kim, Seungho Ryu, Chihyung Jeon, and Meeyoung Cha. 2020.  Collecting the Public Perception of AI and Robot Rights.  _Proceedings of the ACM on Human-Computer Interaction_ 4, CSCW2 (Oct. 2020), 1–24.  https://doi.org/10.1145/3415206
  * Luger and Sellen (2016) Ewa Luger and Abigail Sellen. 2016.  ”Like Having a Really Bad PA”: The Gulf between User Expectation and Experience of Conversational Agents. In _Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems_. ACM, San Jose California USA, 5286–5297.  https://doi.org/10.1145/2858036.2858288
  * Malle (2019) Bertram F. Malle. 2019.  How Many Dimensions of Mind Perception Really Are There?  _Proceedings of the 41st Annual Meeting of the Cognitive Science Society_ (2019), 2268–2274.
  * Malle et al. (2019) Bertram F. Malle, Stuti Thapa Magar, and Matthias Scheutz. 2019.  AI in the Sky: How People Morally Evaluate Human and Machine Decisions in a Lethal Strike Dilemma.  In _Robotics and Well-Being_ , Maria Isabel Aldinhas Ferreira, João Silva Sequeira, Gurvinder Singh Virk, Mohammad Osman Tokhi, and Endre E. Kadar (Eds.). Vol. 95. Springer International Publishing, Cham, 111–133.  https://doi.org/10.1007/978-3-030-12524-0_11
  * McDuff and Czerwinski (2018) Daniel McDuff and Mary Czerwinski. 2018.  Designing Emotionally Sentient Agents.  _Commun. ACM_ 61, 12 (Nov. 2018), 74–83.  https://doi.org/10.1145/3186591
  * Metzinger (2021) Thomas Metzinger. 2021.  Artificial Suffering: An Argument for a Global Moratorium on Synthetic Phenomenology.  _Journal of Artificial Intelligence and Consciousness_ 08, 01 (March 2021), 43–66.  https://doi.org/10.1142/S270507852150003X
  * Miao et al. (2022) Fred Miao, Irina V. Kozlenkova, Haizhong Wang, Tao Xie, and Robert W. Palmatier. 2022.  An Emerging Theory of Avatar Marketing.  _Journal of Marketing_ 86, 1 (Jan. 2022), 67–90.  https://doi.org/10.1177/0022242921996646
  * Mishra and Rzeszotarski (2021) Swati Mishra and Jeffrey M. Rzeszotarski. 2021.  Crowdsourcing and Evaluating Concept-driven Explanations of Machine Learning Models.  _Proceedings of the ACM on Human-Computer Interaction_ 5, CSCW1 (April 2021), 1–26.  https://doi.org/10.1145/3449213
  * Nass and Moon (2000) Clifford Nass and Youngme Moon. 2000.  Machines and Mindlessness: Social Responses to Computers.  _Journal of Social Issues_ 56, 1 (Jan. 2000), 81–103.  https://doi.org/10.1111/0022-4537.00153
  * Nass et al. (1994) Clifford Nass, Jonathan Steuer, and Ellen R. Tauber. 1994.  Computers Are Social Actors. In _Proceedings of the SIGCHI Conference on Human Factors in Computing Systems Celebrating Interdependence - CHI ’94_. ACM Press, Boston, Massachusetts, United States, 72–78.  https://doi.org/10.1145/191666.191703
  * Ngo et al. (2023) Richard Ngo, Lawrence Chan, and Sören Mindermann. 2023.  The Alignment Problem from a Deep Learning Perspective.  arXiv:2209.00626 [cs]
  * Nguyen et al. (2023) Mai Nguyen, Lars-Erik Casper Ferm, Sara Quach, Nicolas Pontes, and Park Thaichon. 2023.  Chatbots in Frontline Services and Customer Experience: An Anthropomorphism Perspective.  _Psychology & Marketing_ (Aug. 2023), mar.21882.  https://doi.org/10.1002/mar.21882
  * Oh et al. (2021) Sarah Soyeon Oh, Kyoung-A Kim, Minsu Kim, Jaeuk Oh, Sang Hui Chu, and JiYeon Choi. 2021.  Measurement of Digital Literacy Among Older Adults: Systematic Review.  _Journal of Medical Internet Research_ 23, 2 (Feb. 2021), e26145.  https://doi.org/10.2196/26145
  * Oviatt (2006) Sharon Oviatt. 2006.  Human-Centered Design Meets Cognitive Load Theory: Designing Interfaces That Help People Think. In _Proceedings of the 14th ACM International Conference on Multimedia_. ACM, Santa Barbara CA USA, 871–880.  https://doi.org/10.1145/1180639.1180831
  * Pauketat and Anthis (2022) Janet V.T. Pauketat and Jacy Reese Anthis. 2022.  Predicting the Moral Consideration of Artificial Intelligences.  _Computers in Human Behavior_ 136 (Nov. 2022), 107372.  https://doi.org/10.1016/j.chb.2022.107372
  * Penn et al. (2023) Mark Penn, Dritan Nesho, and Stephen Ansolabehere. 2023.  _July Harvard CAPS / Harris Poll_.  Technical Report. The Harris Poll.
  * Reeves and Nass (1996) Byron Reeves and Clifford Ivar Nass. 1996.  _The Media Equation: How People Treat Computers, Television, and New Media Like Real People and Places_.  CSLI Publications ; Cambridge University Press, Stanford, Calif. : New York.
  * Richardson (2016) Kathleen Richardson. 2016.  Technological Animism: The Uncanny Personhood of Humanoid Machines.  _Social Analysis_ 60, 1 (Jan. 2016).  https://doi.org/10.3167/sa.2016.600108
  * Ripken (2009) Susanna Kim Ripken. 2009.  Corporations Are People Too: A Multi-dimensional Approach to the Corporate Personhood Puzzle.  _Fordham Journal of Corporate and Financial Law_ 15 (2009).
  * Russell (2019) Stuart J. Russell. 2019.  _Human Compatible: Artificial Intelligence and the Problem of Control_.  Viking, New York.
  * Sandberg and Bostrom (2008) Anders Sandberg and Nick Bostrom. 2008.  _Whole Brain Emulation: A Roadmap_.  Technical Report. Future of Humanity Institute.
  * Schuman and Presser (1981) Howard Schuman and Stanley Presser. 1981.  _Questions and Answers in Attitude Surveys: Experiments on Question Form, Wording, and Context_.  Academic Press, New York.
  * Schwitzgebel (2023) Eric Schwitzgebel. 2023.  AI Systems Must Not Confuse Users about Their Sentience or Moral Status.  _Patterns_ 4, 8 (Aug. 2023), 100818.  https://doi.org/10.1016/j.patter.2023.100818
  * Schwitzgebel and Garza (2015) Eric Schwitzgebel and Mara Garza. 2015.  A Defense of the Rights of Artificial Intelligences: Defense of the Rights of Artificial Intelligences.  _Midwest Studies In Philosophy_ 39, 1 (Sept. 2015), 98–119.  https://doi.org/10.1111/misp.12032
  * Scott et al. (2023) Ava Elizabeth Scott, Daniel Neumann, Jasmin Niess, and Paweł W. Woźniak. 2023.  Do You Mind? User Perceptions of Machine Consciousness. In _Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems_. ACM, Hamburg Germany, 1–19.  https://doi.org/10.1145/3544548.3581296
  * Seligman and Csikszentmihalyi (2000) Martin E. P. Seligman and Mihaly Csikszentmihalyi. 2000.  Positive Psychology: An Introduction.  _American Psychologist_ 55, 1 (2000), 5–14.  https://doi.org/10.1037/0003-066X.55.1.5
  * Seymour and Van Kleek (2020) William Seymour and Max Van Kleek. 2020.  Does Siri Have a Soul? Exploring Voice Assistants Through Shinto Design Fictions. In _Extended Abstracts of the 2020 CHI Conference on Human Factors in Computing Systems_. ACM, Honolulu HI USA, 1–12.  https://doi.org/10.1145/3334480.3381809
  * Shneiderman et al. (2017) Ben Shneiderman, Catherine Plaisant, Maxine Cohen, Steven M. Jacobs, and Niklas Elmqvist. 2017.  _Designing the User Interface: Strategies for Effective Human-Computer Interaction_ (sixth edition ed.).  Pearson, Boston.
  * Smith et al. (2018) Tom W. Smith, Michael Davern, Jeremy Freese, and Morgan Stephen. 2018.  _General Social Surveys, 1972-2018_.  Technical Report. National Opinion Research Center.
  * Sparrow (2007) Robert Sparrow. 2007.  Killer Robots.  _Journal of Applied Philosophy_ 24, 1 (Feb. 2007), 62–77.  https://doi.org/10.1111/j.1468-5930.2007.00346.x
  * Spence et al. (2018) Patric R. Spence, Autumn Edwards, and Chad Edwards. 2018.  Attitudes, Prior Interaction, and Petitioner Credibility Predict Support for Considering the Rights of Robots. In _Companion of the 2018 ACM/IEEE International Conference on Human-Robot Interaction_. ACM, Chicago IL USA, 243–244.  https://doi.org/10.1145/3173386.3177071
  * Srinivasan and Takayama (2016) Vasant Srinivasan and Leila Takayama. 2016.  Help Me Please: Robot Politeness Strategies for Soliciting Help From Humans. In _Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems_. ACM, San Jose California USA, 4945–4955.  https://doi.org/10.1145/2858036.2858217
  * Strait et al. (2015) Megan Strait, Lara Vujovic, Victoria Floerke, Matthias Scheutz, and Heather Urry. 2015.  Too Much Humanness for Human-Robot Interaction: Exposure to Highly Humanlike Robots Elicits Aversive Responding in Observers. In _Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems_. ACM, Seoul Republic of Korea, 3593–3602.  https://doi.org/10.1145/2702123.2702415
  * Stuart and Kneer (2021) Michael T. Stuart and Markus Kneer. 2021.  Guilty Artificial Minds: Folk Attributions of Mens Rea and Culpability to Artificially Intelligent Agents.  _Proceedings of the ACM on Human-Computer Interaction_ 5, CSCW2 (Oct. 2021), 1–27.  https://doi.org/10.1145/3479507
  * Sweeney (2016) Miriam E. Sweeney. 2016.  The Ms. Dewey ”Experience”: Technoculture, Gender, and Race.  In _Digital Sociologies_. Policy Press, 401–420.
  * Swift (2017) Art Swift. 2017.  Americans’ Views on Government Regulation Remain Steady.  https://news.gallup.com/poll/220400/americans-views-government-regulation-remain-steady.aspx.
  * Thaker et al. (2017) Jagadish Thaker, Xiaoquan Zhao, and Anthony Leiserowitz. 2017.  Media Use and Public Perceptions of Global Warming in India.  _Environmental Communication_ 11, 3 (May 2017), 353–369.  https://doi.org/10.1080/17524032.2016.1269824
  * Thellman et al. (2022) Sam Thellman, Maartje De Graaf, and Tom Ziemke. 2022.  Mental State Attribution to Robots: A Systematic Review of Conceptions, Methods, and Findings.  _ACM Transactions on Human-Robot Interaction_ 11, 4 (Dec. 2022), 1–51.  https://doi.org/10.1145/3526112
  * Tolmeijer et al. (2022) Suzanne Tolmeijer, Markus Christen, Serhiy Kandul, Markus Kneer, and Abraham Bernstein. 2022.  Capable but Amoral? Comparing AI and Human Expert Collaboration in Ethical Decision Making. In _CHI Conference on Human Factors in Computing Systems_. ACM, New Orleans LA USA, 1–17.  https://doi.org/10.1145/3491102.3517732
  * Tzelios et al. (2022) Kallie Tzelios, Lisa A. Williams, John Omerod, and Eliza Bliss-Moreau. 2022.  Evidence of the Unidimensional Structure of Mind Perception.  _Scientific Reports_ 12, 1 (Nov. 2022), 18978.  https://doi.org/10.1038/s41598-022-23047-6
  * Wang et al. (2021) Qiaosi Wang, Koustuv Saha, Eric Gregori, David Joyner, and Ashok Goel. 2021.  Towards Mutual Theory of Mind in Human-AI Interaction: How Language Reflects What Students Perceive About a Virtual Teaching Assistant. In _Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems_. ACM, Yokohama Japan, 1–14.  https://doi.org/10.1145/3411764.3445645
  * Wang and Krumhuber (2018) Xijing Wang and Eva G. Krumhuber. 2018.  Mind Perception of Robots Varies With Their Economic Versus Social Function.  _Frontiers in Psychology_ 9 (July 2018), 1230.  https://doi.org/10.3389/fpsyg.2018.01230
  * Waytz et al. (2010) Adam Waytz, John Cacioppo, and Nicholas Epley. 2010.  Who Sees Human?: The Stability and Importance of Individual Differences in Anthropomorphism.  _Perspectives on Psychological Science_ 5, 3 (May 2010), 219–232.  https://doi.org/10.1177/1745691610369336
  * Weisman et al. (2017) Kara Weisman, Carol S. Dweck, and Ellen M. Markman. 2017.  Rethinking People’s Conceptions of Mental Life.  _Proceedings of the National Academy of Sciences_ 114, 43 (Oct. 2017), 11374–11379.  https://doi.org/10.1073/pnas.1704347114
  * Whitby (2008) Blay Whitby. 2008.  Sometimes It’s Hard to Be a Robot: A Call for Action on the Ethics of Abusing Artificial Agents.  _Interacting with Computers_ 20, 3 (May 2008), 326–333.  https://doi.org/10.1016/j.intcom.2008.02.002
  * Xu et al. (2023) Xuhai Xu, Anna Yu, Tanya R. Jonker, Kashyap Todi, Feiyu Lu, Xun Qian, João Marcelo Evangelista Belo, Tianyi Wang, Michelle Li, Aran Mun, Te-Yen Wu, Junxiao Shen, Ting Zhang, Narine Kokhlikyan, Fulton Wang, Paul Sorenson, Sophie Kim, and Hrvoje Benko. 2023.  XAIR: A Framework of Explainable AI in Augmented Reality. In _Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems_. ACM, Hamburg Germany, 1–30.  https://doi.org/10.1145/3544548.3581500
  * YouGov (2023a) YouGov. 2023a.  The AI Policy Institute Toplines.  https://archive.ph/UBR7K.
  * YouGov (2023b) YouGov. 2023b.  How Concerned, If at All, Are You about the Possibility That AI Will Cause the End of the Human Race on Earth? | Daily Question.  https://today.yougov.com/topics/technology/survey-results/daily/2023/04/03/ad825/3.
  * YouGov (2023c) YouGov. 2023c.  How Likely Do You Think It Is That AI Will Eventually Become More Intelligent than People? | Daily Question.  https://today.yougov.com/topics/technology/survey-results/daily/2023/04/03/ad825/1.
  * YouGov (2023d) YouGov. 2023d.  More than 1,000 Technology Leaders Recently Signed an Open Letter Calling on Researchers to Pause Development of Certain Large-Scale AI Systems for at Least Six Months World-Wide, Citing Fears of the “Profound Risks to Society and Humanity.” Would You Support or Oppose a Six-Month Pause on Some Kinds of AI Development? | Daily Question.  https://today.yougov.com/topics/technology/survey-results/daily/2023/04/03/ad825/2.
  * Zoshak and Dew (2021) John Zoshak and Kristin Dew. 2021.  Beyond Kant and Bentham: How Ethical Theories Are Being Used in Artificial Moral Agents. In _Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems_. ACM, Yokohama Japan, 1–15.  https://doi.org/10.1145/3411764.3445102

##  Appendix A Additional Results

In this document, we discuss additional results to enrich and contextualize
the results in the main text. As with those results, we focus on the 2023 main
survey, and we note when there was a significant increase from 2021 to 2023.
Additionally, in the 2021 preregistration, four researchers specified 80%
credible intervals (the Bayesian analog of a frequentist confidence interval
to represent subjective beliefs) for summary statistics based on 82 of the 86
questions. This ensured that we would know which results were surprisingly
high, surprisingly low, or in line with our expectations. We also solicited
predictions from a popular online forecasting platform for five survey
questions in March 2022, prior to the results being shared outside of the
research team.

Following these results for the total samples, we include predictive models
that regress attitudes on demographics, such as gender and race. The survey
questionnaires and raw data are also included in the supplementary materials
for further analysis.

###  A.1. 2021 and 2023 Main Survey Waves

####  A.1.1. Social Beliefs

In addition to tracking people’s own beliefs about sentient AI, we included a
question on social beliefs. Using the same Likert scale, we found 73.2%
agreement in 2023 with the statement, ”Most people who are important to me
think that robots/AIs cannot have feelings.”

####  A.1.2. Subservience

We found 84.7% agreement with the statement, ”Robots/AIs should be subservient
to humans,” a significant increase ($p$ = 0.006) from 76.0% in 2021.

####  A.1.3. Nonhumans

AIs constitute a new category of nonhuman entities in the world, so we also
asked two questions on attitudes towards animals and two on attitudes towards
the environment for potential comparative analysis (e.g., Are people who care
more for animals and the environment likely to care more about AI?). We
assessed agreement with the statements, ”Animals deserve to be included in the
moral circle” (90.8%), ”The welfare of animals is one of the most important
social issues in the world today” (82.2%), ”The environment deserves to be
included in the moral circle” (87.2%), and ”The welfare of the environment is
one of the most important social issues in the world today” (84.9%).

####  A.1.4. Pairwise Comparisons: Target-Specific Moral Concern

In the main text, we listed the mean responses to each of 11 questions about
target-specific moral concern for specific types of AIs. We also conducted
pairwise comparisons between responses to each question using a generalized
linear model, which in this case is equivalent to a weighted $t$-test of the
difference between responses. In Table A1, we present the $p$-values for each
pairwise comparison. For the ten adjacent comparisons, we include, in
parentheses, the $q$-value when adjusted for the false discovery rate (i.e.,
significant when under 0.1 due to adjusting for a false discovery rate of
10%), as these comparisons are used to determine tiers of results, meaning a
series of adjacent results that are not statistically significant from each
other. The tiers of results are, in order from most to least concern:

  1. (1)

exact digital copies of human brains

  2. (2)

human-like companion robots

  3. (3)

human-like retail robotsa, animal-like companion robotsb, exact digital copies
of animals, AI personal assistantsab

  4. (4)

complex language algorithms

  5. (5)

machine-like factory production robots

  6. (6)

machine-like cleaning robots, virtual avatars

  7. (7)

AI video game characters

where a and b indicate significant differences within a tier.

See pages - of supplementary_materials/additional_results_tables_compiled.pdf

####  A.1.5. Target-Specific Social Connection

Because of the close relationship between moral concern and social connection,
we included a well-known measure of social connection known as Inclusion of
Other in the Self (IOS) (Aron et al., 1992) in which participants are shown
seven pairs of circles with varying degrees of overlap between the pair and
asked, ”Which pair of circles best represents how connected [type of AI] are
to humans?” with the same 11 targets as in the target-specific moral concern
measures.

In 2023, the most social connection was attributed to AI personal assistants
($M$ = 3.75, $SE$ = 0.0578), followed by human-like companion robots ($M$ =
3.68, $SE$ = 0.0578), complex language algorithms ($M$ = 3.66, $SE$ = 0.0604),
exact digital copies of human brains ($M$ = 3.66, $SE$ = 0.0641), AI video
game characters ($M$ = 3.54, $SE$ = 0.0629), machine-like factory production
robots ($M$ = 3.43, $SE$ = 0.0620), animal-like companion robots ($M$ = 3.42,
$SE$ = 0.0576), virtual avatars ($M$ = 3.37, $SE$ = 0.0605), human-like retail
robots ($M$ = 3.36, $SE$ = 0.0573), machine-like cleaning robots ($M$ = 3.32,
$SE$ = 0.0618), and exact digital copies of animals ($M$ = 3.20, $SE$ =
0.0600). While this order is similar to that of the target-specific moral
concern questions, there are some distinctions, such as virtual avatars being
seen as neither the IOS measure nor its individual items significantly changed
from 2021 to 2023. As with the target-specific moral concern questions, the
results of pairwise comparisons between each are included in the supplementary
materials.

####  A.1.6. Pairwise Comparisons: Target-Specific Social Connection

Unlike the numerous significant differences between adjacent types in the
moral concern comparisons, there was only one in the social connection
comparisons: that between AI video game characters and machine-like factory
production robots with an unadjusted $p$-value of 0.0499, and the adjusted
$q$-value of 0.357 does not meet the adjustment cutoff of 0.1 for the 10%
false discovery rate. We still found that there are numerous significant
differences between non-adjacent types. For example, machine-like companion
robots evoke significantly less social connection than AI personal assistants
($p$ ¡ 0.001), human-like companion robots ($p$ ¡ 0.001), complex language
algorithms ($p$ ¡ 0.001), and exact digital copies of human brains ($p$ =
0.002). Complete results are listed in Table A2.

####  A.1.7. Substratism

In the study of human-animal interaction, the idea of ”speciesism” has been
developed to refer to the view that individuals of certain species matter less
purely due to their species. We tested two analogs of items from the well-
known Speciesism Scale (Caviola et al., 2018) translated into the context of
substratism. Specifically, we found 74.5% agreement that, ”Morally, artificial
beings always count for less than humans,” and 54.2% agreement that, ”Humans
have the right to use artificial beings however they want to.” The latter
significantly increased from 48.5% in 2021 ($p$ = 0.007).

###  A.2. 2023 Supplemental Survey Wave

####  A.2.1. Trust

Given the focus in 2023 on corporate behavior, particularly that of OpenAI and
other technology companies producing AI systems, and on government regulation,
particularly to rein in those companies, the supplement included six questions
about trust and assessments of whether these institutions can control AI.

When asked, ”AI systems include many different parts. To what extent do you
trust the following parts?” on a scale from 1 (not at all) to 7 (very much):
engineers ($M$ = 4.19, $SE$ = 0.0525), training data ($M$ = 3.92, $SE$ =
0.0506), output ($M$ = 3.85, $SE$ = 0.0500), algorithm ($M$ = 3.77, $SE$ =
0.0520), companies ($M$ = 3.42, $SE$ = 0.0521), and governments ($M$ = 3.09,
$SE$ = 0.0536).

We asked whether participants trust that ”the creators of large language
models (e.g., OpenAI and GPT-4) put safety over profits” (yes: 22.5%, not
sure: 28.7%, no: 48.8%), ”the creators of an AI can control all current and
future versions of the AI” (yes: 26.9%, not sure: 24.1%, no: 45.9%),
”governments have the power to regulate the development of AI” (yes; 52.0%,
not sure: 24.1%, no: 23.9%), and, ”To what extent do you agree or disagree
that governments have the power to effectively enforce regulations on the
development of AI?” (71.1% agreement).

On a series of agree-disagree questions, people were largely split on whether
they trusted each of four specific types of AI: ”I trust game-playing AI”
(59.8% agreement); ”I trust large language models” (53.5%); ”I trust robots”
(47.5%); and ”I trust chatbots” (46.5%).

####  A.2.2. Positive Emotions

Most of our measures of moral concern have been explicit statements that could
be considered rational or logical, rather than the more intuitive, emotional
aspects of judgment and decision making. One of the most popular trends in
psychology research in the 21st century has been positive psychology (Seligman
and Csikszentmihalyi, 2000), particularly the role of positive emotions
(Fredrickson, 2001). We assessed these by asking, ”To what extent do you, as a
human, feel the following emotions towards robots/AIs?” on a 1–7 sliding scale
(1 = not at all, 4 = a moderate amount, 7 = ”very much) for six emotions: awe
(3.90), excitement (3.76), respect (3.53), admiration (3.50), pride (3.30),
and compassion (3.02).

####  A.2.3. Uploads

We probed participant opinions on a speculative future scenario in which
humans can upload their minds to computers, which has been discussed in
numerous academic and science fiction works (for a review, see Sandberg and
Bostrom Sandberg and Bostrom, 2008). The possibility of mind uploading
presents an alternative future trajectory of AI instead of the de novo AI,
such as large language models, that is the current focus of most AI
researchers.

We developed two questions based on the standard format of policy proposals in
the General Social Survey (Smith et al., 2018). The first question, which
presented the scenario straightforwardly, resulted in 41.2% agreement with, ”I
support humans using advanced technology in the future to upload their minds
into computers.” In a more detailed question,

we found that 39.3% supported the position (i.e., response higher than four,
excluding responses of exactly four), ”In the future, humans could upload
their minds into computers. Some people think that this would be very good
because uploaded humans could consume fewer resources, live longer free from
biological disease, and have enhanced intelligence and a greater ability to
improve the world. Others disagree and think that uploading would mean that we
are no longer truly human, change who we are and how we want to live, and
distract us from making the real world a better place. Where would you place
yourself on this scale?” (sliding scale: 1 = oppose mind uploads, 4 = neither
oppose nor support uploads, 7 = support mind uploads).

####  A.2.4. Additional Replications

In addition to the replicated questions mentioned in the main text from YouGov
on concern for human extinction (YouGov, 2023b) and support for the six-month
pause on advanced AI development (YouGov, 2023a) and its reversed formulation,
we replicated another YouGov question (YouGov, 2023d), ”How likely do you
think it is that artificial intelligence (AI) will eventually become more
intelligent than people?” (very likely, somewhat likely, not very likely, not
likely at all, it is already more intelligent than people, not sure), finding
that 69.8% consider it likely, compared to 57% found by YouGov, and a question
posed by science communicator Hank Green on Twitter (Green, 2023), ”Which
universe is the better one:” (one with humans, one without humans), finding
89.9% selecting ”one with humans” compared to 58.9% in Green’s Twitter poll.

###  A.3. Predictors of attitudes towards sentient AI

Because of the large number of questions in this survey and their novelty, our
predictive analyses rely on exploratory descriptive tests rather than
confirmatory tests of particular ex ante hypotheses. While further testing and
theorization of these relationships is beyond the scope of the current work,
it will be an important area of research as this field develops—particularly
insofar as interaction with apparent digital minds relates to ongoing social
dynamics and disparities in human-AI interaction.

We fit multivariate generalized linear models in which participant
characteristics predicted each of a variety of indices: Ban Support,
Existential Threat, General Mind Perception, General Moral Concern for All
AIs, General Moral Concern for Sentient AIs, General Threat, LLM Mind
Perception, LLM Suffering Concern, Mind-Related Anthropomorphism, Positive
Emotions, Protection Support, Slowdown Support, Target-Specific Moral Concern,
and Trust; we also predicted two individual items: Subservience and 100-Year
Likelihood of Sentient AI. For each outcome, we fit a model for each dataset
in which the question or questions were included: first, the main 2021 and
2023 surveys (N = 2,401), which also included year of taking the survey as a
predictor, and second, the supplemental 2023 survey (N = 1,099). In general,
regressing on indices is preferable because it tends to reduce noise compared
to individual items. In our reporting of the results, effect sizes are
standardized to compare across different outcome scales, and $p$-values are
assessed at the typical cutoffs (** : $p$ ¡ 0.001; *: 0.001 ¡ $p$ ¡ 0.01).
Complete results, including effects at lesser significance (0.01 ¡ $p$ ¡
0.05), are available in the supplementary materials.

We present the strongest effects of the four strongest predictors, expressed
as the number of standard deviations of the dependent variable associated with
an increase of one standard deviation in the independent variable. We list all
effect sizes of at least 0.1, which means that an increase in the independent
variable by one standard deviation is associated with the dependent variable
increasing by at least one-tenth of a standard deviation. Effect sizes from
each dataset are paired together when there is more than a strong association
in both the main data (listed first with subscript M) and the supplemental
data (listed second with subscript S).

In general, measures of participant experience with AI were the strongest
predictor across indices and questions. To mitigate multicollinearity, of the
three questions about this, we only included the strongest predictor,
frequency of reading or watching AI content, in the models described below.
Responses to this question were strongly correlated with the count of AI-
related experiences ($r_{M}$ = 0.584, $r_{S}$ = 0.544) and the frequency of AI
interaction ($r_{M}$ = 0.561, $r_{S}$ = 0.569).

Specifically, increased frequency of reading or watching AI content was the
strongest predictor across indices and questions, strongly predicting
increases in LLM Mind Perception ($M_{S}$ = 0.329**), Positive Emotions
($M_{S}$ = 0.314**), Trust ($M_{S}$ = 0.281**), Mind-Related Anthropomorphism
($M_{M}$ = 0.261**), General Mind Perception ($M_{M}$ = 0.260**), General
Moral Concern for All AIs ($M_{S}$ = 0.261**), Protection Support ($M_{M}$ =
0.245**), LLM Suffering Concern ($M_{S}$ = 0.234**), General Moral Concern for
Sentient AIs ($M_{M}$ = 0.248**), 100-Year Likelihood of Sentient AI ($M_{M}$
= 0.211**, $M_{S}$ = 0.197**), and Target-Specific Moral Concern ($M_{M}$ =
0.200**), as well as strongly predicting decreases in General Threat ($M_{S}$
= -0.145**), Slowdown Support ($M_{S}$ = -0.118**), and Ban Support ($M_{S}$ =
-0.101*).

Increased age strongly predicted increases in Subservience ($M_{M}$ = 0.165**,
$M_{S}$ = 0.148**) as well as decreases in Mind-Related Anthropomorphism
($M_{M}$ = -0.182**), Positive Emotions ($M_{S}$ = -0.174**), LLM Suffering
Concern ($M_{S}$ = -0.168**), 100-Year Likelihood of Sentient AI ($M_{S}$ =
-0.135**), General Mind Perception ($M_{M}$ = -0.134**), Trust ($M_{S}$ =
-0.133**), LLM Mind Perception ($M_{S}$ = -0.124**), and Protection Support
($M_{M}$ = -0.114**).

Conservative political orientation strongly predicted increases in Ban Support
($M_{M}$ = 0.171**), General Threat ($M_{M}$ = 0.137**), Subservience ($M_{S}$
= 0.107**), and Existential Threat ($M_{S}$ = 0.107**) as well as decreases in
General Moral Concern for Sentient AIs ($M_{M}$ = -0.121**), General Moral
Concern for All AIs ($M_{S}$ = -0.106**), and LLM Suffering Concern ($M_{S}$ =
-0.103**).

Male gender strongly predicted increases in Trust ($M_{S}$ = 0.156**) and
Positive Emotions ($M_{S}$ = 0.147**) as well as decreases in Ban Support
($M_{M}$ = -0.138**, $M_{S}$ = -0.146**) and Slowdown Support ($M_{S}$ =
-0.129**).