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TYPE Original Research
PUBLISHED 16 March 2023
DOI 10.3389/fcomp.2023.1113903
OPEN ACCESS
EDITED BY
Simona Aracri,
National Research Council (CNR), Italy
REVIEWED BY
Galena Pisoni,
University of Nice Sophia Antipolis, France
Mohammad Faiz Iqbal Faiz,
Tezpur University, India
*CORRESPONDENCE
Philipp Brauner
brauner@comm.rwth-aachen.de
SPECIALTY SECTION
This article was submitted to
Human-Media Interaction,
a section of the journal
Frontiers in Computer Science
RECEIVED 01 December 2022
ACCEPTED 28 February 2023
PUBLISHED 16 March 2023
CITATION
Brauner P, Hick A, Philipsen R and Ziefle M
(2023) What does the public think about
artificial intelligence?—A criticality map to
understand bias in the public perception of AI.
Front. Comput. Sci. 5:1113903.
doi: 10.3389/fcomp.2023.1113903
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terms.
What does the public think about
artificial intelligence?—A
criticality map to understand bias
in the public perception of AI
Philipp Brauner*, Alexander Hick, Ralf Philipsen and Martina Ziefle
Human-Computer Interaction Center, RWTH Aachen University, Aachen, Germany
Introduction: Artificial Intelligence (AI) has become ubiquitous in medicine,
business, manufacturing and transportation, and is entering our personal lives.
Public perceptions of AI are often shaped either by admiration for its benefits and
possibilities, or by uncertainties, potential threats and fears about this opaque and
perceived as mysterious technology. Understanding the public perception of AI,
as well as its requirements and attributions, is essential for responsible research
and innovation and enables aligning the development and governance of future
AI systems with individual and societal needs.
Methods: To contribute to this understanding, we asked 122 participants in
Germany how they perceived 38 statements about artificial intelligence in dierent
contexts (personal, economic, industrial, social, cultural, health). We assessed
their personal evaluation and the perceived likelihood of these aspects becoming
reality.
Results: We visualized the responses in a criticality map that allows the
identification of issues that require particular attention from research and policy-
making. The results show that the perceived evaluation and the perceived
expectations dier considerably between the domains. The aspect perceived as
most critical is the fear of cybersecurity threats, which is seen as highly likely and
least liked.
Discussion: The diversity of users influenced the evaluation: People with lower
trust rated the impact of AI as more positive but less likely. Compared to people
with higher trust, they consider certain features and consequences of AI to be
more desirable, but they think the impact of AI will be smaller. We conclude that
AI is still a “black box” for many. Neither the opportunities nor the risks can yet
be adequately assessed, which can lead to biased and irrational control beliefs in
the public perception of AI. The article concludes with guidelines for promoting
AI literacy to facilitate informed decision-making.
KEYWORDS
artificial intelligence, aect heuristic, public perception, user diversity, mental models,
technology acceptance, responsible research and innovation (RRI), collingridge dilemma
1. Introduction
Artifical Intelligence (AI), Deep Neural Networks (DNN) and Machine Learning (ML)
are the buzzwords of the moment. Although the origins of AI and ML date back decades, they
have received a tremendous boost in recent years due to increased computing power, more
available digital data, improved algorithms and a substantial increase in funding (Lecun et al.,
2015;Statista, 2022).
While we are still a long way from Artificial General Intelligence (AGI) (“strong AI”)—
referring to an AI that matches human intelligence, and can adapt as well as transfer
learning to new tasks (Grace et al., 2018)—it is undeniable that even “weak AI” and ML
that focus on narrow tasks already have a huge impact on individuals, organizations and
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our societies (West, 2018). While the former, aims at recreating
human-like intelligence and behavior, the latter is applied to solve
specific and narrowly defined tasks, such as image recognition,
medical diagnosis, weather forecasts, or automated driving
(Flowers, 2019). Interestingly, recent advancements in AI and its
resulting increase in media coverage, can be explained by the
progress in the domain of weak AI, like faster and more reliable
image recognition, translation, text comprehension through DNNs
and their sub types (Vaishya et al., 2020;Statista, 2022), as well
as image or text generation (Brown et al., 2020). Despite the
tremendous progress in weak AI in the recent years, AI still has
considerable difficulties in transferring its capabilities to other
problems (Binz and Schulz, 2023). However, public perceptions
of AI are often shaped by science fiction characters portrayed as
having strong AI, such as Marvin from The Hitchhiker’s Guide to
the Galaxy, Star Trek’s Commander Data, the Terminator, or HAL
9000 from Space Odyssey (Gunkel, 2012;Gibson, 2019;Hick and
Ziefle, 2022). These depictions can influence the public discourse
on AI and skew it into an either overly expectant or unwarranted
pessimistic narrative (Cugurullo and Acheampong, 2023;Hirsch-
Kreinsen, 2023).
Much research has been done on developing improved
algorithms, generating data, labeling for supervised learning, and
studying the economic impact of AI on organizations (Makridakis,
2017;Lin, 2023), the workforce (Acemoglu and Restrepo, 2017;
Brynjolfsson and Mitchell, 2017), and society (Wolff et al., 2020;
Floridi and Cowls, 2022;Jovanovic et al., 2022). However, despite
an increased interest in the public perception of AI (Zuiderwijk
et al., 2021), it is essential to regularly update these academic
insights. Understanding the individual perspective plays a central
part since the adoption and diffusion of new technologies such as AI
and ML can be driven by greater acceptance or significantly delayed
by perceived barriers (Young et al., 2021).
In this article, we present a study in which we measured
novices’ expectations and evaluations of AI. Participants assessed
the likelihood that certain AI related developments will occur and
whether their feelings about these developments are positive or
negative. In this way, we identify areas where expectations and
evaluations are aligned, as well as areas where there are greater
differences and potential for conflict. Since areas of greater disparity
can hinder social acceptance (Slovic, 1987;Kelly et al., 2023), they
need to be publicly discussed. Based on accessible and transparent
information about AI and a societal discourse about its risks and
benefits, these discrepancies can either be reduced or regulatory
guidelines for AI can be developed.
A result of this study is a spatial criticality map for AI-based
technologies that 1) can guide developers and implementers of AI
technology with respect to socially critical aspects, 2) can guide
policy making regarding specific areas in need of regulation, 3)
inform researchers about areas that could be addressed to increase
social acceptance, and 4) identify relevant points for school and
university curricula to inform future generations about AI.
The article is structured as follows: Section 2 defines our
understanding of AI and reviews recent developments and current
projections on AI. Section 3 presents our approach to measuring
people’s perceptions of AI and the sample of our study. Section 4
presents the results of the study and concludes with a criticality
map of AI technology. Finally, Section 5 discusses the findings, the
limitations of this work, and concludes with suggestions on how
our findings can be used by others.
2. Related work
This section first presents some of the most commonly used
definitions of AI and elaborates on related concepts. It then presents
studies in the field of AI perception and identifies research gaps.
2.1. Overview on AI
Definitions of Artifical Intelligence (AI) are as diverse as
research on AI. The term AI was coined during the “Dartmouth
workshop on Artificial Intelligence” in 1955. During that year’s
summer, the proposed definition of Artifical Intelligence (AI) was
that “every aspect of learning or any other feature of intelligence can
in principle be so precisely described that a machine can be made
to simulate it” (McCarthy et al., 2006). In the year 1955—almost
70 years ago—researchers were convinced that—within a 2 month
period—these machines would understand language, use abstract
concepts, and could improve themselves. It was an ambitious goal
that was followed by even more ambitious research directions and
working definitions for AI.
AI is a branch of computer science that deals with the creation
of intelligent machines that can perform tasks that typically require
human intelligence, such as visual perception, speech recognition,
decision-making, and language translation (Russell and Norvig,
2009;Marcus and Davis, 2019). ML, conversely, is a subset of
AI that focuses on the development of algorithms and statistical
models that enable machines to improve their performance on
a specific task over time by learning from data, without being
explicitly programmed.
A central introductory textbook on AI by Russel and Norvig
defines it as “the designing and building of intelligent agents that
receive percepts from the environment and take actions that affect
that environment” (Russell and Norvig, 2009). The Cambridge
Dictionary takes a somewhat different angle by defining AI as “the
study of how to produce computers that have some of the qualities
of the human mind, such as the ability to understand language,
recognize pictures, solve problems, and learn” or as “computer
technology that allows something to be done in a way that is similar
to the way a human would do it” (Cambridge Dictionary, 2022).
This kind of AI approximates the human mind and is built into
a computer which is then used to solve some form of complex
problem. On the one hand, this approach serves us with a well-
defined line of events: We have a problem, develop a solution and,
hopefully, will be able to solve the initial problem. The machine’s
job, or more precisely, an AI’s job would be to find a solution, that
is, give an answer to our question. On the other hand, this approach
is rather narrow in scope. As Pablo Picasso famously commented
in an interview for the Paris Review in 1964 “[Computers1] are
useless. They can only give you answers.” Picasso wanted to convey
that a computer, or an AI for that matter, can only present outputs
1 Picasso was referring to mechanical calculation machines, nowadays
called computers.
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specific to an input, i.e., a specific answer to a specific question.
However, it is currently beyond the capabilities of any AI algorithm
to transfer its “knowledge” to any previously unseen problem and
excel at solving it (Binz and Schulz, 2023). This is why there are
many algorithms and many AI models, one for each particular
problem. Going back to definitions—at least today—there is no
single universal definition that captures the essence of AI.
Current AI research focuses on automating cognitive work
that is often repetitive or tiring (Fosso Wamba et al., 2021).
Its aim is to provide technological solutions to an otherwise
inefficient or less efficient way of working. However, there
are many other areas of (potential) AI applications that are
merely an extension of what the human mind can do, such
as creativity. In a recent example, a AI-based art generator
won a prestigious art competition in the USA. In this case,
the piece of art was entitled The death of art and received
a mixed reception on Twitter, with some people fearing for
their jobs, which may soon be replaced by a machine (Jumalon,
2022).
Many research articles focus on workers’ perceptions of
machine labor and its potential to replace some aspect of their
work (Harari, 2017). In most cases, the machine is not a
replacement, but rather an addition to the workforce (Topol,
2019). However, fear of replacement still exists among people
working in jobs that are particularly easy to automate, such as
assembly line work, customer service or administrative tasks (Smith
and Anderson, 2014). A recent study found that workers’ level
of fear of being replaced did not significantly affect their level
of preparation for this potential replacement, such as acquiring
new skills. Furthermore, appreciation of the new technology
and perceived opportunity positively influenced workers’ attitudes
toward automation (Rodriguez-Bustelo et al., 2020). This is just
one example of the importance of perception of e.g., a new
technology, and consequently its understanding, to accurately
judge its implications.
Some form of AI is now used in almost all areas of technology,
and it will continue to spread throughout society (Grace et al.,
2018;Almars et al., 2022). Current application areas include
voice assistants, automatic speech recognition, translation, and
generation that can exceed the human performance (Corea,
2019), automated driving and flying (Klos et al., 2020;Kulida
and Lebedev, 2020), and medical technologies (areas where AI
could touch our personal lives) (Klos et al., 2020;Jovanovic
et al., 2022), as well as production control (Brauner et al., 2022),
robotics and human-robot interaction (Onnasch and Roesler,
2020;Robb et al., 2020), human resource management, and
prescriptive machine maintenance (areas where AI could touch our
professional lives).
We suspect that the perception of the benefits and potential
risks of AI is influenced by the application domain and thus that
the evaluation of AI cannot be separated from its context. For
example, AI-based image recognition is used to evaluate medical
images for cancer diagnosis (Litjens et al., 2017) or to provide
autonomously driving cars with a model of their surroundings (Rao
and Frtunikj, 2018). Therefore, people’s perception of AI and its
implications will depend less on the underlying algorithms and
more on contextual factors.
2.2. Studies on human perception of AI
As outlined in the section above, perceptions of AI can be
influenced not only by the diversity of end users (Taherdoost, 2018;
Sindermann et al., 2021), but also by contextual influences. As an
example from the context of automated driving, Awad et al. used
an instance of Foot’s Trolly dilemma (Foot, 1967) to study how
people would prefer a AI-controlled car to react in the event of an
unavoidable crash (Awad et al., 2018). In a series of decision tasks,
participants had to decide if the car should rather kill a varying
number of involved pedestrians or its car passengers. The results
show that, for example, sparing people is preferred to sparing
animals, sparing more people is preferred over sparing fewer people
and, to a lesser extend, pedestrians are preferred to passengers.
The article concludes that consideration of people’s perceptions
and preferences, combined with ethical principles, should guide the
behavior of these autonomous machines.
In a different study (Araujo et al., 2020) examined the perceived
usefulness of AI in three contexts (media, health, and law). As
opposed to the automated driving example, their findings suggest
that people are generally concerned about the risks of AI and
question its fairness and usefulness for society. This means that
in order to achieve appropriate and widespread adoption of AI
technology, end-user perceptions and risk assessments should be
taken into account at both the individual and societal levels.
In line with this claim, another study has investigated whether
people assign different levels of trust to human, robotic or AI-
based agents (Oksanen et al., 2020). In this study, the researchers
investigated the extent to which participants would trust either
an AI-based agent or a robot with their fictitious money during
a so-called trust game, and whether the name of the AI-based
agent or robot would have an influence on this amount of money.
The results showed that the most trusted agent was a robot with
a non-human name, and the least trusted i.e., the agents was
given the least amount of money, was an unspecified control
(meaning that it was not indicated if it was human or not) named
Michael. The researchers concluded that people would trust a
sophisticated technology more in a context where this technology
had to be reliable in terms of cognitive performance and fairness.
They also concluded that, from the Big Five personality model
(McCrae and Costa, 1987), the dimension Openness was positively,
and Conscientiousness negatively related to the attributed trust.
The study provided support for the theory that higher levels of
education, previous exposure to robots, or higher levels of self-
efficacy in interacting with robots may influence levels of trust in
these technologies.
In addition to this angle, the domain of implementation of AI,
i.e., the role it takes on in a given context, was explored (Philipsen
et al., 2022). Here, the researchers investigated what the roles of
an AI are and how an AI has to be designed in order to fulfill the
expected roles. On the one hand, the results show that people do
not want to have a personal relationship with an AI, e.g., an AI as
a friend or partner. On the other hand, the diversity of the users
influenced the evaluation of the AI. That is, the higher the trust in
an AI’s handling of data, the more likely personal roles of AI were
seen as an option. Preference for subordinate roles, such as an AI
as a servant, was associated with general acceptance of technology
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and a belief in a dangerous world. Thus, subordinate roles were
preferred when participants believed that the world we live in is
more dangerous than it is not. However, the attribution of roles was
independent of the intention to use AI. Semantic perceptions of AI
also differed only slightly from perceptions of human intelligence,
e.g., in terms of morality and control. This supports our claim that
initial perceptions of e.g., AI can influence subsequent evaluations
and both, potentially and ultimately, AI adoption.
With AI becoming an integral part of lives as personal assistants
(Alexa, Siri, . . . ) (Burbach et al., 2019), large language models
(ChatGPT, LaMDA, . . . ), smart shopping lists, and the smart
home (Rashidi and Mihailidis, 2013), end-user perception and
evaluation fo these technologies becomes increasingly important
(Wilkowska et al., 2018;Kelly et al., 2023). This is also evident in
professional contexts, where AI is used—for example—in medical
diagnosis (Kulkarni et al., 2020), health care (Oden and Witt, 2020;
Jovanovic et al., 2022), aviation (Klos et al., 2020;Kulida and
Lebedev, 2020), and production control (Brauner et al., 2022). The
continued development of increasingly sophisticated AI can lead
to profound changes for individuals, organizations and society as a
whole (Bughin et al., 2018;Liu et al., 2021;Strich et al., 2021).
However, the assessment of the societal impact of a technology
in general, and the assessment of AI in particular, is a typical
case of the Collingridge (1982): These are developments that are
either difficult to predict if they do not exist, or difficult to manage
and regulate if they are already ubiquitous. On the one hand, if
the technology is sufficiently developed and available, it can be
well evaluated, but by then it is often too late to regulate the
development. On the other hand, if the technology is new and
not yet pervasive in our lives, it is difficult to assess its perception
and potential impact, but it is easier to manage its development
and use. Responsible research and innovation requires us to
constantly update our understanding of the societal evaluations and
implications as technologies develop (Burget et al., 2017;Owen and
Pansera, 2019). Here, we aim to update our understanding of the
social acceptability of AI and to identify any need for action (Owen
et al., 2012).
3. Method
Above, we briefly introduced the term AI, showed that AI
currently involves numerous areas of our personal and professional
lives, and outlined studies on the perception of AI. The present
study is concerned with laypersons’ perceptions, their assessment
of an AI development and its expected likelihood of actually
happening. Thus, our approach is similar to the Delphi method,
where (expert) participants are asked to make projections about
future developments (Dalkey and Helmer, 1963), by aggregating
impartial reflections of current perceptions into insights about
technology adoption and technology foresight.
To assess perceptions of AI, we used a two-stage research
model. In the first stage of our research, topics were identified in
an expert workshop to get an accurate list. Then, these topics were
rated by a convenient sample in the manner described above. This
approach for studying laypeople’s perception of AI will be further
discussed later in the article.
3.1. Identification of the topics
To develop the list of topics we conducted a three-stage expert
workshop with four experts in the field of technology development
and technology forecasting. In the first stage, we brainstormed
possible topics. In the second stage, similar topics were grouped
and then the most relevant topics were selected, resulting 38 topics.
In the third and final stage, the labels of the 38 defined topics
were reworded so that they could be easily understood by the
participants in the survey which followed.
3.2. Survey
We designed an online survey to assess non-experts’
perceptions of AI. It consisted of two main parts: First, we asked
about the participant’s demographics and additional explanatory
factors (see below). Second, we asked participants about numerous
aspects and whether they thought the given development was likely
to occur (i.e., Will this development happen?), and, as a measure
of acceptability (Kelly et al., 2023), how they personally evaluated
this development (i.e., Do you think this development is good?).
Overall, we asked about the expectation (likelihood) and evaluation
(valence) of 38 different aspects, ranging from the influence on
the personal and professional life, to the perceived impact of AI
on the economy, healthcare, and culture, as well as wider societal
implications. The questionnaire was administered in German and
the items were subsequently translated into English for this article.
Figure 1 illustrates the research approach and the structure of the
survey. Table 2 lists all statements from the AI scenarios.
3.3. Demographics and explanatory user
factors
In order to investigate possible influences of user factors
(demographics, attitudes) on the expectation and evaluation of the
scenarios, the survey started with a block asking for demographic
information and attitudes of the participants. Specifically, we asked
participants about their age in years, their gender and their highest
level of education. We then asked about the following explanatory
user factors that influenced the perception and evaluation of
technology in previous studies. We used 6-point Likert-scales to
capture the explanatory user factors (ranging from 1 to 6). Internal
reliability was tested using Cronbach’s alpha (Cronbach, 1951).
Affinity for Technology Interaction refers to a person’s “tendency
to actively engage in intensive technology interaction” (Franke
et al., 2019) and is associated with a positive basic attitude toward
various technologies and presumably also toward AI. We used five
items with the highest item-total-correlation. The scale achieved
excellent internal reliability (α=0.804, n=122, 5 items).
Trust is an important prerequisite for human coexistence and
cooperation (Mc Knight et al., 2002;Hoff and Bashir, 2015).
Mayer et al. (1995) defined trust as “the willingness of a party
to be vulnerable to another party.” As technology is perceived as
social actor (Reeves and Nass, 1996), trust is also relevant to the
acceptance and use of digital products and services. We used three
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FIGURE 1
Multi-stage research design of this study with expert workshop and subsequent survey study. The questionnaire captures demographics, exploratory
user factors and the evaluation of the 38 AI-related scenarios.
scales to measure trust: First, we measured interpersonal trust using
the psychometrically well validated KUSIV3 short scale with three
items (excellent internal reliability, α=0.829) (Beierlein, 2014).
The scale measures the respondent’s trust in other people. Secondly
and thirdly, we developed two short scales with three items each to
specifically model trust in AI and distrust in AI. Both scales achieved
an acceptable internal reliability of α=0.629 (trust in AI) and
α=0.634 (distrust in AI).
3.4. Perception of artificial intelligence
We asked about various topics in which AI already plays or
could play a role in the future. The broader domains ranged
from implications for the individual, over economical and societal
changes, to questions of governance. Some of the topics were more
straightforward and others rather far-flung.
For each of the 38 topics, we asked the participants whether this
development is likely or not (likelihood) and if they evaluate this
development as positive or negative (evaluation). Table 3 presents
these topics that ranged from “AI will promote innovation,” over
“AI will create significant cultural assets,” to “AI will lead to the
downfall of society.”
The questionnaire displayed the items in three columns: The
item text on the left and two Likert scales to query the participants’
expected likelihood and evaluation should the development come
true on the right. The order of the items was randomized across the
participants to compensate for question order biases. We used 4-
point Likert scales to measure the expected likelihood of occurrence
and evaluation of the given statements.
3.5. Survey distribution and data analysis
The link to the survey was distributed via email, messaging
services, and social-networks. We checked that none of the user
factors examined were correlated with not completing the survey
and found no systematic bias. We therefore consider the dataset of
122 samples in the following.
We examined the dataset using the social sciences portfolio
of methods (Dienes, 2008). To assess the association between the
variables, we analyzed the data using non-parametric (Spearman’s
ρ) and parametric correlations (Pearson’s r), setting the significance
level at 5% (α=0.05). We used Cronbach’s αto test the
internal consistency of the explanatory user factors and, where
permitted, calculated the corresponding scales. As there is no
canonical order for the statements on the AI developments, we
did not recode the values. We calculated mean scores (M) and the
standard deviation (SD) for likelihood and evaluation for both the
38 developments (individually for each topic across all participants;
vertical in the dataset) and for each participant (individually
for each participant across all topics; horizontal in the dataset).
The former gives the sample’s average assessment of each topic,
while the latter is an individual measure of how likely and how
positive the participants consider the questioned developments are
in general.
3.6. Description of the sample
In total, 122 people participated in the survey. Forty one
identified themselves as men, 81 as women, and no one stated
“diverse” or refused to answer. The age ranged from 18 to 69
years (M=33.9, SD =12.8). In the sample, age was neither
associated with Affinity Toward Technology Interaction, nor with
any of the three trust measures (p>0.05). Gender was associated
to Affinity Toward Technology Interaction (r= −0.381, p<
0.001), with men, on average, reporting higher attitudes toward
interacting with technology. Interpersonal Trust is associated to
higher Trust in AI (r=0.214, p=0.018), but not to higher
distrust in AI (p=0.379). Not surprisingly, there is a negative
relationship between trust and distrust in AI (r= −0.386,
p<0.01. People who have more trust in AI report less distrust
and vice versa. Finally, Affinity Toward Technology Interaction
is related to both trust in AI (r=0.288, p=0.001 and
(negatively) to distrust in AI (r= −0.280, p=0.002). Table 1
shows the correlations between the (explanatory) user factors in
the sample.
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TABLE 1 Descriptive statistics and correlations of the (explanatory) user
factors in the sample of 122 participants.
Variable M(SD) 1 2 3 4 5
1. Age in years 33.88
(12.81)
–
2. Gender 41 male,
81 female
–0.013 –
3. Interpersonal
trust
4.13
(0.96)
0.10 –0.06 –
4. Affinity toward
technology
interaction
3.74
(1.16)
–0.15 –0.38 0.02 –
5. Trust in AI 3.34
(0.87)
.06 –0.06 0.21 0.29 –
6. Distrust in AI 4.01
(1.00)
–0.01 0.09 –0.08 –0.28 –0.39
Note that gender is dummy-coded (0 = male, 1 = female).
4. Results
First, we analyse how participants evaluate the different
statements on AI and map these statements spatially. Figure 2
shows a scatter plot of the participants’ average estimated
probability of occurrence and their average rating for each of the 38
topics in the survey. Each individual point in the figure represents
the evaluation of one topic. The position of the points on the
horizontal axis represents the estimated likelihood of occurrence,
with topics rated as more likely to occur further to the right
of the figure. The position on the vertical axis shows the rating
of the statement, with topics rated as more positive appearing
higher on the graph. Table 2 shows the individual statements and
their ratings.
The resulting graph can be interpreted as a criticality map
and read as follows: In the upper left corner are those aspects
that were rated as positive but unlikely. The upper right corner
shows statements that were rated as both positive and likely. The
lower right corner contains statements that were rated as negative
but likely. Finally, the lower right corner contains statements
that were perceived as both negative and unlikely. Second, dots
on or near the diagonal represent aspects where the perceived
occurrence is consistent with the personal rating of the aspect:
These aspects are either perceived as likely and positive (e.g.,
“promote innovation” or “do unpleasant activities” or as unlikely
and negative (e.g., “occupy leading positions in working life” or
“threaten my professional future”). On the other hand, for points
off the diagonal, expectations and evaluations diverge. The future
is either seen as probable and negative (e.g., “be hackable” or “be
influenced by a few”), or as unlikely and positive (e.g., “create
cultural assets” or “lead to more leisure time for everyone”).
Accordingly, three sets of points deserve particular attention.
Firstly, the points in the bottom half of the graph, as these are seen
as negative by the participants. This is where future research and
development should take people’s concerns into account. Secondly,
the points in the upper left quadrant of the graph, as these are
considered positive but unlikely. These points provide insight into
where participants perceive research and implementation of AI to
fall short of what they want. Finally, all items where there is a
large discrepancy between the likelihood of occurrence and the
assessment (off the diagonal), as these items are likely to lead to
greater uncertainty in the population.
As the figure shows, for some of the statements the estimated
likelihood of occurrence is in line with the participants’ personal
assessment, while for others there is a strong divergence. The
statements with the highest agreement were that AI will support
the performance of unpleasant activities (positive expectation
and evaluation), that it will promote innovation (also positive
expectation and evaluation), that it will threaten the professional
future of participants (both low evaluation and low expectation),
and that AI will occupy leading positions in working life (again,
both low evaluation and low expectation). In contrast, the
statements with the largest difference share the pattern that they
are expected to become reality and are viewed negatively by the
participants. The statements were that the development and use of
AI will be influenced by a few, that the use of AI will lead to less
communication, that AI will be influenced by an elite, that it will
destroy more jobs than it creates, and finally that it will be hackable.
4.1. Are the estimated likelihood of
occurrence and the evaluation correlated?
Next, we analyse whether the expected likelihood and perceived
valence ratings are correlated. To do this, we calculated Pearson’s
correlation coefficient between the average ratings of the 38 AI-
related topics. The test showed a weak association of (r=0.215),
but this is not significant (p=0.196 >.05). This means that
expectations of potential developments are not related to people’s
evaluations of them. Thus, our sample does not provide evidence
that the perceived likelihood and valence of AI’s impact on society,
personal and professional life are related.
4.2. Does user diversity influence the
technology foresight?
Finally, we examined whether the explanatory user factors
influenced the evaluation and estimated likelihood of the different
AI topics. To do this, we calculated an average score for the
two target dimensions for each participant. A correlation analysis
shows that both the mean likelihood and the mean evaluations
of the topics are influenced by user diversity and the explanatory
user factors. Table 3 shows the results of the analyses. Across the
participants the mean valence is weakly and negatively related to
trust (r= −0.253, p=0.005) and positively related to distrust in
AI (r=0.221, p=0.014). Thus, participants with higher distrust
in AI rated the potential scenarios as slightly more favorable, while
higher trust is associated with slightly lower evaluations.
The mean estimated likelihood of occurrence is related with
distrust in AI (r= −0.336, p<0.001), Affinity Toward
Technology Interaction (r=0.310, p<0.001), trust in AI (r=
0.203, p=0.025), as well as to interpersonal trust (r=0.183, p=
0.043). Higher distrust in AI is associated with a lower estimated
likelihood, while all other variables are associated with a higher
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FIGURE 2
Criticality map showing the relationship between estimated likelihood and evaluations for the AI predictions.
estimated likelihood. It appears that distrust in AI is associated with
a lower estimated likelihood.
5. Discussion
This article presents the results of a survey of people’s
expectations and evaluations of various statements about the
impact AI might have on their lives and society. Overall,
participants in our study associated AI with both positive and
negative evaluations, and also considered certain developments
to be more or less likely. Thus, AI and its implications are not
perceived as either black or white, but participants had a nuanced
view of how AI will affect their lives. From the perspective of social
acceptance, issues of divergence between the two dimensions of
expectation and evaluation deserve particular attention.
We analyzed the participants’ subjective assessments of the
developments. While this gives an insight into their beliefs and
mental models, some of the assessments are likely to be challenged
by other research. A critical point here is certainly the assessment
of how AI will affect the labor market and individual employment
opportunities. Our study participants are not very concerned
about their professional future or the labor market as a whole.
While a significant shift away from jobs with defined inputs and
outputs (tasks perfectly suited for automation by AI) is predicted,
which could lead to either lower employment or lower wages
(Acemoglu and Restrepo, 2017;Brynjolfsson and Mitchell, 2017),
participants in our sample do not feel personally affected by this
development. They see a clearly positive effect on the overall
economic performance and that in the context of AI it is likely that
few new jobs will be created (and that jobs will rather be cut), but
they do not see their individual future prospects as being at risk.
This may be due to their qualifications or to an overestimation
of their own market value in times of AI. Unfortunately, our
research approach does not allow us to answer this question.
However, comparing personal expectations, individual skills and
future employment opportunities in the age of AI is an exciting
research prospect.
Rather than examining the influence of individual differences,
our study design focused on mapping expectations toward AI.
However, this more explanatory analysis still revealed insights
that deserve attention in research and policy making. Our results
suggest that people with a lower general disposition to trust AI
will, on average, evaluate the different statements more positively
than people with a lower disposition to distrust. Similarly, a
higher disposition to trust AI is associated with a lower average
valence. When it comes to expectations for the future, the picture
is reversed. A high disposition to trust is associated with a
higher probability that the statements will come true, whereas a
high disposition to distrust is associated with a lower expected
probability. As a result, people with less trust rate the impact of
AI as more positive but less likely. Thus, for this group, certain
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TABLE 2 The participants’ estimated likelihood (Likelihood) of
occurrence and subjective assessment (Evaluation) of the various
consequences AI could have on our lives and worka.
Likelihood Evaluation
AI will... Mean SD Mean SD
Do unpleasant activities 42.1% 56.8% 42.1% 64.7%
Promote innovation 49.2% 51.5% 49.7% 60.2%
Threaten my professional future –45.4% 60.6% –47.0% 66.9%
Occupy leading positions in
working life
–34.4% 66.4% –43.7% 65.3%
On equal footing at the workplace –19.1% 58.9% –29.0% 60.4%
Be subordinate in working life 8.7% 59.5% 19.7% 66.9%
Increase the standard of living 18.6% 59.1% 30.1% 64.6%
Become a family member –44.3% 63.8% –58.5% 59.6%
Solve complex social problems –7.1% 62.1% 8.7% 66.5%
Lead to more well-paid jobs –6.0% 67.4% 10.4% 69.5%
Threaten my private life –39.3% 64.7% –56.3% 60.5%
Lead to a downfall of society –45.9% 59.6% –63.4% 55.6%
Increase my personal performance –0.5% 66.0% 20.8% 66.6%
Increase economic performance 51.9% 40.4% 29.0% 59.8%
Create more jobs –30.6% 63.2% –7.7% 75.9%
Lead to more leisure time for a few 3.8% 60.0% –23.5% 62.8%
Increase my wealth –21.3% 60.6% 7.1% 68.9%
Create cultural assets –41.0% 55.7% –10.4% 66.8%
Control our dying –25.7% 64.8% –58.5% 59.0%
Make moral decisions –22.4% 70.9% –55.7% 56.6%
Blend work and leisure time –17.5% 59.4% –53.0% 52.6%
Lead to more leisure time for
everyone
–19.1% 55.7% 21.9% 66.8%
Defining political decisions –13.1% 66.9% –59.6% 52.5%
Lead to more low-paid jobs –16.4% 75.0% –67.2% 50.9%
Fuse humans and technology 31.7% 53.8% –19.1% 57.6%
Act responsibly 10.4% 62.8% –41.5% 57.9%
Defining economy 31.7% 57.2% –20.2% 57.9%
Defining our coexistence –2.7% 58.9% –54.6% 47.2%
Control and guide our working life 14.8% 63.1% –51.4% 54.2%
Create social division 3.3% 64.2% –68.3% 42.2%
Control and guide our private life –2.7% 67.0% –76.0% 44.6%
Lead to isolation 2.7% 69.7% –73.2% 49.7%
Make society more lazy 20.2% 70.5% –60.7% 49.1%
Be influenced by a few 27.3% 54.9% –59.0% 52.4%
Lead to less communication 23.5% 70.0% –63.9% 52.2%
Be influenced by an elite 23.5% 65.1% –65.6% 44.7%
Destroy more jobs 26.2% 66.3% –64.5% 49.4%
Be hackable 60.7% 52.7% –79.2% 42.1%
Items sorted from least to strongest discrepancy between likelihood and Evaluation.
aMeasured on two 4-point Likert scales and rescaled to –100% to +100%. Negative values
indicate that the development is seen as unlikely respectively a negative assessment and
positive values indicate a high estimated likelihood respectively positive evaluation.
TABLE 3 Correlations between AI assessment and the (explanatory) user
factors.
Variable Valence Likelihood
1. Age in years 0.06 –0.10
2. Gender –0.08 –0.15
3. Interpers. Trust –0.10 0.18
4. Attitiude in technology interaction –0.05 0.31
5. Trust in AI –0.25 0.20
6. Distrust in AI .22 –0.34
7. Average valence – –0.07
8. Average likelihood –0.07 –
Note that gender is dummy-coded (0 = male, 1 = female).
features and consequences of AI seem desirable, but there is a
lack of conviction that this will happen in such a positive way.
Future research should further differentiate the concept of trust in
this context: On the one hand, trust that the technology is reliable
and not harmful, and on the other hand, trust that the technology
can deliver what is promised to oneself or by others, i.e., trust as
opposed to confidence.
In our explanatory analysis, we examined whether the expected
likelihood was related to the valuation. However, this relationship
was not confirmed, although the (non-significant) correlation was
quite large. We refrain from making a final assessment and suggest
that the correlation between valence and expected likelihood of
occurrence should be re-examined with a larger sample and a
more precise measurement of the target dimensions. This would
provide a deeper understanding of whether there is a systematic
bias between these two dimensions and at the same time allow, if
possible, to derive distinguishable expectation profiles to compare
user characteristics, e.g., between groups that are rather pessimistic
about AI development, groups that have exaggerated expectations
or naive ideas about the possibilities of AI, or groups that reject AI
but fear that it will nevertheless permeate life.
5.1. Implications
As discussed above, AI is at the center of attention when it
comes to innovative and “new” technology. Huge promises have
been made about the impact, both positive and negative, that
AI could have on society as a whole, but also on an individual
level (Brynjolfsson and Mitchell, 2017;Ikkatai et al., 2022). This
development has led to a shift in public attention and attitudes
toward AI. Therefore, it is necessary to elaborate on this perception
and attitude in order to find future research directions and possible
educational approaches to increase people’s literacy about AI
and AI-based technologies. This discussion should also include a
discourse on ethical implications, i.e., possible moral principles that
should guide the way we research and develop AI. These principles
should include individual, organizational and societal values as well
as expert judgements about the context in which AI is appropriate
or not (Awad et al., 2018;Liehner et al., 2021).
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Previous research on this approach shows that cynical distrust
of AI, i.e., the attitude that AI cannot be trusted per se, is a
different construct from the same kind of distrust toward humans
(Bochniarz et al., 2022). This implies that although AI is thought
to be close to the human mind—at least in some circumstances—it
is not confused with the human traits of hostility or emotionality.
Importantly, according to Kolasinka et al., people have different
evaluations of AI depending on the context (Kolasinska et al., 2019):
When asked in which field of AI research they would invest an
unlimited amount of money, people chose the fields of medicine
and cybersecurity. There seems to be an overlap between the
context in which AI is placed and the level of trust required in
that specific context. For example, most people are not necessarily
experts in cybersecurity or medicine. However, because of the
trust placed in an IT expert, a doctor or any other expert, people
generally do not question the integrity of these experts. AI is a
similar matter, as people do not usually attribute emotionality to
it, but rather objectivity, so they tend to trust its accuracy and
disregard its potential for error (Cismariu and Gherhes, 2019;Liu
and Tao, 2022).
Despite the benefits of AI, an accurate knowledge of its
potential and limitations is necessary for a balanced and useful
use of AI-based technology (Hick and Ziefle, 2022). Therefore,
educational programmes for the general public and non-experts
in the field of AI seems appropriate to provide a tool with which
people can evaluate for themselves the benefits and barriers of this
technology (Olari and Romeike, 2021). More research is needed to
find out what are the most important and essential aspects of such
an educational programme, but the map presented here may be a
suitable starting point starting point to identify crucial topics.
6. Limitations and future work
Of course, this study is not without its limitations. First,
the sample of 122 participants is not representative for the
whole population of our country or even across countries. We
therefore recommend that this method be used with a larger,
more diverse sample, including participants of all ages, from
different educational backgrounds and, ideally, from different
countries and cultures. Nevertheless, the results presented here
have their own relevance: Despite the relatively homogeneous
young and educated sample, certain misconceptions about AI
became apparent and imbalances in estimated likelihood and
valuation could be identified. These could either be an obstacle to
future technology development and adoption and/or are aspects
that require societal debate and possibly regulation.
Second, participants responded to a short item on each topic
and we refrained from explaining each idea in more detail. As a
result, the answers to these items may have been shaped by affective
rather than cognitively considered considerations. However, this
is not necessarily a disadvantage. On the one hand, this approach
made it easier to explore a wide range of possible ways in which AI
might affect our future. On the other hand, and more importantly,
we as humans are not rational agents, but most of our decisions
and behavior are influenced by cognitive biases and our affect (i.e.,
“affect heuristic”) (Finucane et al., 2000;Slovic et al., 2002). In this
respect, this study contributes to affective technology evaluation,
which nonetheless influences evaluation and use.
From a methodological point of view, asking for ratings with
only two single items leads to a high variance and makes it difficult
to examine individual aspects in detail. Although this allowed us
to address a variety of different issues, future work should select
specific aspects and examine them in more detail. Consequently,
future work may further integrate other concepts, such as the
impact of AI on individual mobility, public safety, or even warfare
through automation and control. However, the present approach
allowed us to keep the survey reasonably short, which had a positive
effect on response attention and unbiased dropout rates.
Finally, we propose the integration of expert judgement into
this cartography. We suspect that there are considerable differences
between expert and lay assessments, particularly in the assessment
of the expected likelihood of the developments in question. Again,
it is the differences between expert and lay expectations that are
particularly relevant for informing researchers and policy makers.
7. Conclusion
The continuing and increasing pervasiveness of AI in almost
all personal and professional contexts will reshape our future, how
we interact with technology, and how we interact with others using
technology. Responsible research and innovation on AI-based
products and services requires us to balance technical advances and
economic imperatives with individual, organizational, and societal
values (Burget et al., 2017;Owen and Pansera, 2019).
This work suggests that the wide range of potential AI
applications is assessed differently in terms of perceived likelihood
and perceived valence as a measure of acceptability. The empirically
derived criticality map makes this assessment visible and highlights
issues with urgent potential for research, development, and
governance and can thus contribute to responsible research and
innovation of AI.
We also found individual differences in perceptions of AI
that may threaten both people’s ability to participate in societal
debates about AI and to adequately adapt their future skill sets
to compete with AI in the future of work. It is a political
issue, not a technological one, in which areas AI can influence
our lives and society, and to what extent. As a society, we
need to discuss and debate the possibilities and limits of
AI in a wide range of applications and define appropriate
regulatory frameworks. For this to happen, we all need to
have a basic understanding of AI so that we can participate
in a democratic debate about its potential and its limits. Free
online courses for adults such as “Elements of AI” and modern
school curricula that teach the basics of digitalisation and AI
are essential for this (Olari and Romeike, 2021;Marx et al.,
2022).
Data availability statement
The datasets presented in this study can be found in
online repositories. The names of the repository/repositories and
accession number(s) can be found below: https://osf.io/f9ek6/.
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Brauner et al. 10.3389/fcomp.2023.1113903
Ethics statement
Ethical review and approval was not required for the study on
human participants in accordance with the local legislation and
institutional requirements. The patients/participants provided their
written informed consent to participate in this study.
Author contributions
PB and RP designed the study. PB wrote the original
draft of the manuscript and coordinated the analysis and
writing, while RP and AH made substantial contributions to
the motivation, related work, and discussion sections of the
manuscript. MZ supervised the work and acquired the funding for
this research. All authors contributed to the article and approved
the submitted version.
Funding
This work was funded by the Deutsche
Forschungsgemeinschaft (DFG, German Research Foundation)
under Germany’s Excellence Strategy—EXC-2023 Internet of
Production—390621612 and the VisuAAL project “Privacy-Aware
and Acceptable Video-Based Technologies and Services for Active
and Assisted Living”, funded by the European Union’s Horizon
2020 Research and Innovation Programme under the Marie
Skłodowska-Curie Grant Agreement No. 861091.
Acknowledgments
We would particularly like to thank Isabell Busch for
her support in recruiting participants, Mohamed Behery
for his encouragement in writing this manuscript, Luca
Liehner for feedback on the key figure, and Johannes
Nakayama and Tim Schmeckel for their support in analysis
and writing. Thank you to the referees and the editor
for their valuable and constructive input. The writing of
this article was partly supported by ChatGPT, DeepL, and
DeepL Write.
Conflict of interest
The authors declare that the research was conducted in the
absence of any commercial or financial relationships that could be
construed as a potential conflict of interest.
Publisher’s note
All claims expressed in this article are solely those of the
authors and do not necessarily represent those of their affiliated
organizations, or those of the publisher, the editors and the
reviewers. Any product that may be evaluated in this article, or
claim that may be made by its manufacturer, is not guaranteed or
endorsed by the publisher.
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