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# Bending the Automation Bias Curve: A Study of Human and AI-based Decision
Making in National Security Contexts

Michael C. Horowitz
University of Pennsylvania
Lauren Kahn
Council on Foreign Relations

###### Abstract

Uses of artificial intelligence (AI), especially those powered by machine
learning approaches, are growing in sectors and societies around the world.
How will AI adoption proceed, especially in the international security realm?
Research on automation bias suggests that humans can often be overconfident in
AI, whereas research on algorithm aversion shows that, as the stakes of a
decision rise, humans become more cautious about trusting algorithms. We
theorize about the relationship between background knowledge about AI, trust
in AI, and how these interact with other factors to influence the probability
of automation bias in the international security context. We test these in a
preregistered task identification experiment across a representative sample of
9000 adults in 9 countries with varying levels of AI industries. The results
strongly support the theory, especially concerning AI background knowledge. A
version of the Dunning Kruger effect appears to be at play, whereby those with
the lowest level of experience with AI are slightly more likely to be
algorithm-averse, then automation bias occurs at lower levels of knowledge
before leveling off as a respondent’s AI background reaches the highest
levels. Additional results show effects from the task’s difficulty, overall AI
trust, and whether a human or AI decision aid is described as highly competent
or less competent.

_K_ eywords Automation Bias $\cdot$ Technology Adoption $\cdot$ Artificial
Intelligence $\cdot$ Cognitive Biases

Word Count: 6900

##  1 Introduction

The integration of advances in artificial intelligence (AI) by governments
worldwide raises significant questions for politics and society. The
delegation of high-level tasks to machines raises the prospect of accidents
and could generate challenges for accountability, especially in high-stakes
international security contexts such as international crises. Complicating
these challenges is the potential for automation bias. Automation bias is the
“tendency [for human operators] to over-rely on automation” (Goddard et al., ,
2012; Skitka et al., , 1999) and automated systems by which these systems and
their outputs become a “heuristic replacement of vigilant information seeking
and processing” (Mosier and Skitka, , 1996). Given the growing interest by
militaries and other national security institutions in adopting AI and
autonomous systems, human-machine relationships are increasingly central to
life and death issues. What do these trends mean for international relations,
since many international relations theories are predicated on the role of
human agency in making decisions and how human-constructed
politics—individual, domestic, and international —shape international
politics?

How increasing reliance on AI will influence decision-making in international
politics is undertheorized. We theorize that key drivers of automation bias
for AI systems in national security contexts are experiential and attitudinal.
Environmental factors such as task difficulty, background
knowledge—familiarity, knowledge, experience—with AI, trust and confidence in
AI, as well as self-confidence determine how much humans rely on machines—or
other humans—when making decisions. In particular, we theorize that a version
of the Dunning-Kruger effect is at play, where algorithm aversion is highest
at the lowest levels of knowledge, flips to automation bias at low levels of
knowledge, then levels off at high levels of knowledge.

Existing evidence about automation bias and algorithm aversion tends to
feature relatively small sample sizes restricted to the United States.
Automation bias experiments also rarely focus on a prominent use case for AI-
enabled autonomous systems—military uses. To fill this gap, we test our
hypotheses using a novel, pre-registered scenario-based survey experiment with
9,000 respondents across nine countries to evaluate the conditions in which
automation bias is more likely, using national security scenarios that mirror
current and likely future uses of AI algorithms.111preregistration link will
be inserted upon article acceptance. As described below, we also choose a
specific scenario where there is no reason to expect that the attitudes of
those who would be making judgments in the real world would substantially
differ from the general public.

The results show the powerful impact of background knowledge, experience, and
familiarity with AI on the subsequent use of AI. As hypothesized, a version of
the Dunning-Kruger effect is at play. Those with the lowest level of
experience with AI are slightly more likely to be algorithm-averse, then
automation bias occurs at lower levels of knowledge before leveling off as a
respondent’s AI background reaches the highest levels. Additional results show
effects from the task’s difficulty, overall AI trust, and whether a human or
AI decision aid is described as highly competent or less competent.

The experiment and our findings contribute to the international relations
literature in three ways. First, the results give us information on how AI is
likely to shape how people make decisions about key areas of international
politics, so it sheds light on decision-making processes at the core of
international relations debates. Second, the focus on AI and automation bias
bolsters growing research on how emerging technologies shape international
politics (Horowitz, , 2020; Kreps, , 2016; Sechser et al., , 2019; Schmidt, ,
2022; Johnson, , 2022). Third, this research contributes to ongoing debates
about the role of trust and confidence in political decision-making, which is
particularly salient now in the context of growing interest in generative AI
and large language models such as ChatGPT and GPT-4.

Moreover, the findings can also contribute to ongoing public policy debates
about AI and automation. Militaries worldwide seek to integrate and leverage
AI capabilities into existing systems and processes, sometimes with fatal
consequences. For example, in 2003, two separate accidents were caused, in
part, by failures in a Patriot missile’s automated tracking and Identification
Friend or Foe(IFF) systems—the system which allows the missiles to
differentiate between a friendly aircraft and enemy anti-radiation or
ballistic missiles, and between enemy aircraft—resulted in three fatalities.
These accidents represented a complicated cascade of simultaneous human and
machine failures, precipitated by an error in an automated decision aid and
facilitated by established organizational practice that defaulted towards the
automated settings (Hawley, , 2017; Atherton, , 2022; Ministry of Defense, ,
2004).

Thus, in addition to the importance for international relations, the results
can inform the development of guidelines, AI education, and training programs
that can improve decision-making in human-AI teams, mitigate the risk of
accidents and failures, and ultimately enhance the safety and effectiveness of
these systems. In what follows, we lay out our theory and hypotheses,
describing the importance for international politics. We then introduce the
survey experiment and overall research design, describe the results, and then
conclude with a discussion of limitations and next steps.

##  2 Theory

There is growing interest in how technological change surrounding robotics,
autonomous systems, and artificial intelligence and machine learning, will
influence international politics Hudson,  (2019); Horowitz,  (2018, 2020);
Jensen et al.,  (2020); Johnson, 2021a . Existing work tends to focus on
questions surrounding drone strikes Lin-Greenberg,  (2022); Mir and Moore,
(2019); Johnston and Sarbahi,  (2016); Kreps and Wallace,  (2016); Horowitz et
al.,  (2016), uses of AI in nuclear command and control, Fitzpatrick,  (2019);
Johnson, 2021b ; Sechser et al.,  (2019), and autonomous weapon systems
Horowitz,  (2019); Scharre,  (2018). How AI will shape decision-making as a
whole in international relations remains understudied, especially outside the
realm of crisis escalation Horowitz and Lin-Greenberg,  (2022). How humans
make choices about whether and how to use algorithms will be a critical part
of that equation.

Automation bias refers to the tendency of humans to rely on AI-enabled
decision aids above and beyond the extent to which they should, given the
reliability of the algorithms. Algorithm aversion refers to the opposite—the
tendency of humans in some situations to discard algorithms in favor of their
own judgment despite evidence in favor of relying on an algorithm. Several
factors may shape when and how automation bias or algorithm aversion manifests
and the frequency with which it occurs. These include experiential factors
such as familiarity with and knowledge of the system and like technologies,
attitudinal factors such as trust and confidence, and environmental factors
such as task difficulty and time constraints (Southern and Arnstern, , 2009;
Alon-Barkat and Busuioc, , 2022; Simmons and Massey, , 2015; Goddard et al., ,
2014). This paper focuses primarily on the first two: experiential and
attitudinal.222This paper primarily addresses the experiential and attitudinal
factors, as the effects of certain environmental factors are already well
theorized and researched. We do not propose any novel hypotheses regarding
these factors.

This paper focuses primarily on experiential and attitudinal factors, bringing
research from other fields into political science. The effects of certain
environmental factors are already well theorized and researched, so we do not
propose any novel hypotheses regarding those factors Goddard et al.,  (2012);
Bailey and Scerbo,  (2007); Povyakalo et al.,  (2013); Lyell and Coiera,
(2016).

###  2.1 Experiential: familiarity with and knowledge of AI

The way knowledge and experience influence how individuals and countries
behave is a long-running topic in international politics (Haas, , 1992;
Reiter, , 1994). Existing research suggests that experience will generate
greater trust and reduce aversion to using algorithms, while time-constraint
elements will increase the likelihood of automation bias. Knowledge,
familiarity, and experience with AI could shape how individuals react to
algorithms designed for new situations and choose whether to use algorithms in
general. For clarity and brevity, we will use the term "background" to
encompass all three distinct but related factors: knowledge, familiarity, and
experience. Behavioral science work shows that while having no knowledge of
technology can lead to fear and rejection, limited background in technology
can lead to overconfidence in its capabilities. People with limited initial
background with a wide variety of topical areas become subject to the
“beginners bubble” (Sanchez and Dunning, , 2018), an illustration of the
Dunning-Kruger effect whereby those with surface-level knowledge become
overconfident in that knowledge, leading to sub-optimal decision-making
(Kruger and Dunning, , 1999). As people gain more experience, familiarity, and
knowledge, the degree of overconfidence declines.

When it comes to assessing applications of AI and susceptibility to automation
bias, the relationship between background in AI and reliance on automation
should therefore be non-linear. Those with no experience, familiarity, or
knowledge should be skeptical of AI, meaning they are also unlikely to be
prone to automation bias. Those with limited backgrounds should be the most
susceptible to automation bias because they have just enough knowledge,
familiarity, and experience to think they understand AI but not enough to
recognize limits and issues with applications. Finally, those with a
substantial background in AI, on average, should be more evenly positioned
between aversion to AI and automation bias—in theory, relying on the AI system
an "appropriate" amount, in line with both its established abilities and
limitations, proportionate to its expected performance and accuracy. In other
words, they know enough to realize both the utility of algorithms in some
cases and know when to question or check algorithmic outputs.333See the
preregistration of this study for additional information. For example, large
language models have shown a propensity to generate irrelevant, nonsensical,
or even false content. This phenomenon, known as hallucination, is a recent,
concrete example of the importance of recognizing and rectifying automation
bias (Metz, , 2023). For instance, a large language model may list a citation
for a nonexistent article yet attribute it to a legitimate author or journal
(or both). Failure of the human not to confirm this information has already
led to errors—StackOverflow, a public forum for programming help, already
banned answers generated by ChatGPT due to the high incidences of subtle
errors and factual inaccuracies (Vincent, , 2022).

Figure 1 below illustrates a stylized take on this relationship.

Figure 1: Reliance on automation relative to prior background in AI.

We theorize that background in AI (knowledge, familiarity, and experience)
will influence how willing respondents are to rely on input received from AI-
based systems and algorithms.

Hypothesis 1: Those with the lowest levels of experience, knowledge, and/or
familiarity (background) are relatively more averse to AI; people with middle
levels of background are relatively over-reliant on AI, and those with the
highest levels of background are relatively appropriately reliant on AI.

###  2.2 Attitudinal: trust and confidence in AI

Trust is an essential topic in international relations, making it critical to
understand it in the AI context as well (Kydd, , 2007; Hoffman, , 2002). In
addition to firsthand knowledge of, familiarity with, and experience with AI,
attitudinal factors should influence the likelihood of automation bias or
algorithm aversion. Attitudinal factors measure whether individuals using AI
and AI-enabled systems trust the system or algorithm to work as expected.
Additionally, whether the operator has trust and/or confidence in the system
to aid them in completing the task will also be relative to how much
confidence and or trust they have in themselves to complete the task.

The distinction between trust and confidence matters. Luhmann argues that
trust and/or confidence influence individuals making decisions under
conditions of risk (Luhmann, , 1979, 1988). Trust refers to a condition of
individual responsibility and knowledge, where someone believes in a specific
actor, often due to knowledge or the perception of shared experiences
(Seligman and Montgomery, , 2019). Trust is an active decision that involves
an agenda—someone chooses to trust someone else. Confidence refers to the
ability to predict the behavior of others not due to individual knowledge or
experience but because of laws, social norms, or established benchmarks for
success or an acceptable margin of error; the active choice is not necessarily
required. Confidence derives from societal expectations about norms and laws
rather than shared experience.

For AI, one might imagine that confidence comes not from shared experience or
familiarity with how algorithms work but faith in the system—the coders
programming algorithms, the testers determining if they are reliable, and the
evaluation processes surrounding the design, deployment, and use of algorithms
as a whole. Confidence might also come from assessments of data, such as
externally provided data on the reliability of an algorithm, system, or
process that one does not personally understand.

The distinction between trust and confidence could play a critical role in
helping explain individual and organizational decisions about AI adoption.
Shared experiences and familiarity generate trust. It is not necessarily the
case, however, that shared experiences and familiarity leads to more support
for AI in a linear fashion because experience and knowledge could lead to
knowledge of the limitations of AI.

As technology develops, there is initial hype and excitement that leads to an
overestimation of the actual effectiveness of the system. As the technology
develops further, the relationship reverses, and there is a “trust gap,”
wherein those early expectations have been shattered by reality. Yet, while
the technology continued to improve, it had not yet earned back users’ trust.
In the final stages, a mature technology has stabilized and reached a high
level of effectiveness and familiarity, confidence, and trust in the system
again, resulting in overconfidence (albeit a more stable one) in its
abilities.

This aligns with literature surrounding “hype cycles” in general and
technology adoption processes, such as the Gartner Hype Cycle. In the early
stages of technology development, when visibility is high, there can be a peak
of inflated expectations, eventually followed by a steep drop in excitement -
the "trough of disillusionment" when there is a disconnect due to misalignment
between observed performance and expectations - before eventually increasing
and then stabilizing as hype becomes more proportionate to actual performance
as individuals become more knowledgeable about the technology.(Blosch and
Fenn, , 2018; Bahmanziari et al., , 2016).

Thus, at varying points in a technology’s lifecycle, trust, confidence, and
willingness to rely on and defer to that technology will differ. Assessing
respondents’ experience and knowledge of the system will also play a role in
determining the degree of trust and confidence in the system.

Studies show that many people are hesitant to trust AI, with one survey by an
AI-driven sales platform (meaning it is likely biased to highlight positive
views of AI) demonstrating that 42% of US respondents did not generally trust
AI (Dujmovic, , 2017). Distrust of AI is heightened in cases where the system
has already experienced a failure, even if it is normally highly reliable
(Alvarado-Valencia and Barrero, , 2014). Distrust in AI greatly undermines the
effectiveness of AI capabilities.

Hypothesis 2: The more trusting of and open to AI technologies in general, the
more likely they are to trust and therefore rely on, the recommendation of an
AI-enabled system.

Hypothesis 3: The more testing and training a decision aid (AI or human) is
described to have, the more confidence the respondent will have in that aid,
and the more likely they will rely on its advice.

Finally, another dimension of confidence is the relationship between self-
confidence in the ability to do a task and willingness to switch answers in
response to human or algorithm input. Those that view themselves as more
competent on their own should be less likely to trust decision-making aids.
Existing studies show that the relationship between self-confidence and
confidence in AI shows that “human self-confidence significantly contributes
to their acceptance of AI decisions” and that "humans often misattribute blame
to themselves and enter a vicious cycle of relying on a poorly performing AI"
(Chong et al., , 2022). For example, in Chong et al.,  (2022), self-confidence
remained the predominant factor across all test groups: low self-confidence
can increase people’s willingness to rely on AI systems. In contrast, high
self-confidence can cause people to reject input from automated systems
unfairly. Thus, managing the role of self-confidence in the ability to
complete the task is critical in understanding the biases people have towards
automated systems and AI and also towards fully grasping the decision-making
process that occurs when utilizing AI input.

Hypothesis 4: The higher the level of self-confidence in the ability to do a
task, the lower the probability that a decision aid will influence views of
that task.

In sum, we hypothesize three attitudinal factors will impact rates of
automation bias — two relating to the system and one to the respondents
themselves — 1) trust in the system, 2) confidence in the system, and 3) self-
confidence of the respondent regarding task completion. We also expect that
these attitudinal factors will be directly affected by environmental factors,
such as task difficulty and time constraints, that pressurize cognitive
resources. However, as explained above, we don’t hypothesize about these
factors directly, as the literature firmly establishes these relationships.

##  3 Research Design

To test the above hypotheses, we designed a scenario-based survey experiment
of the general adult public in nine countries: the United States, Russia,
China, France, Australia, Japan, South Korea, Sweden, and the United Kingdom.
The sample size for each country is 1,000 respondents for each, giving us a
total sample size of 9,000. We obtain a representative sample of adults from
each country except China and Russia, where we obtained a representative
sample of urban adults.444YouGov fielded the survey in the United States and
Russia, while Delta Poll fielded the survey in the remaining countries.

Surveys of the general public make sense for testing our theory for several
reasons. First, the public can often be a good proxy for elite preferences,
especially when there is no theoretical reason to think that elites would have
different preferences (Kertzer and Renshon, , 2022; Kertzer, , 2022). Since we
are early in the age of AI and foreign policy, elites do not have decades of
experience that would lead to different decision-making heuristics.

Second, we ask respondents to conduct an identification task where there is no
reason to assume there are differences between the general public and those
that would actually be conducting that identification in a national security
situation. Third, identifying airplanes with the benefit of a decision aid is
something that relatively low-level military personnel would conduct, rather
than elites working in the White House. This again suggests the relevance of
surveying the general public. Fourth, because we are surveying various
countries, elite attitudes would be difficult to gather and unhelpful for
aggregation due to distinct country-specific effects and biases for elite
populations, possibly muddying results.

###  3.1 Country Selection

Four main factors drove the selection of these countries: 1) the presence of
an AI strategy, 2) each country’s national investment in AI, and 3) regional
variety and prominence within international security discussions. The nine
countries span the scope of Asia, Europe, and North America, including major
international actors and artificial intelligence investors. Moreover, the
investments made by these nine countries span the range of AI research,
economic purposes, and national security investment, meaning that the
countries also have a variety of national interests in the AI sphere. Finally,
we also selected countries where we could reliably get an accurate and
representative sample of their populations.

Each of the nine countries has published a national AI strategy outlining
their national goals for using and investing in AI (OECD AI, , 2022). The
publication of national AI strategies demonstrates a prioritization of AI as
an industry and technology that will make questions such as those presented in
our survey potentially more relevant and potentially more recognizable to the
population.

The next element in selecting countries was the level of investment and
research development each country has in AI-related fields. Stanford’s Human-
Centered Artificial Intelligence (HAI) 2021 AI Index measured national AI
investments across twenty-two different factors, including investment,
research output, patents and intellectual property, and jobs and industries
related to AI (HAI at Stanford University, , 2021). In this report, the United
States, China, South Korea, Australia, and the United Kingdom ranked in the
top 10, while Japan, France, and Sweden ranked within the top 20 countries
(HAI at Stanford University, , 2021). The United States has been a prominent
leader in investment in AI as most major AI industries are headquartered in
the United States. While Russia did not rank as highly on the indexes or
levels of investment as the other eight countries on the list, it has
nonetheless made several public endeavors to invest more in AI and proclaimed
its intention to become a leader in AI.

We also looked at regional variation and prominence in international security.
Regional variation refers to selecting countries from around the world and
those with potentially different interests and political systems. The
countries selected span North America; Europe, including different regions
within Europe; Asia; Eurasia, and Oceania. Additionally, they represent
different political orientations within countries and different government
systems, including both democracies and autocracies, and countries associated
with prominent roles within broader international institutions, including the
European Union, NATO, the AIIB, and the UN and UN Security Council.

###  3.2 Survey Design

As described above, prior survey research on automation bias has often not
extended to AI-enabled automation or has been limited to aviation and pilots
or restricted in its sample or breadth (Cummings, , 2004; Goddard et al., ,
2012; Parasuraman and Manzey, , 2010). Survey experiments conducted on the
American public have been shown to increase our understanding of audience
costs, (Tomz, , 2007; Tragger and Vavreck, , 2011) the democratic peace, (Tomz
and Weeks, , 2013) and human rights (Wallace, , 2013, 2014), while other
extensive cross-national surveys highlight the large cultural differences that
exist with approaches to AI (Awad et al., , 2018).

Additionally, research into the efficacy of decision-making shows that
decisions made by experts versus those made by the general public have little
difference in their quality, meaning a general population can provide insights
on elites as well (Welsh, , 2018; Tetlock, , 2009). Therefore, the results of
this survey can help us understand how decision-making in international
security contexts occurs and the ability to make cross-cultural comparisons
without potentially being biased by prior training that experts might have.

The experiment employs a within-participant, before-after design that
evaluates how the same respondents alter their behavior based on different
treatments. Participants first received instructions on the task—identifying
whether an airplane belongs to their country’s military or an adversary’s
military based on a set of defined characteristics. Participants then
completed five practice rounds with no pressurizing constraints, time limits,
or obscuring of the images, where they determined whether an airplane was an
enemy or a friendly one and received live feedback on the accuracy of their
identifications. The intended effect of providing live feedback was
establishing a baseline for respondents regarding their effectiveness at the
set task.

Following the practice rounds, participants then entered the experimental
portion of the survey, where they were shown ten randomized hypothetical
airplane identification scenarios with varying levels of task difficulty (the
first five randomized at a lower level of difficulty, and the second five
randomized at a higher level of difficulty).555In terms of difficulty,
respondents had either 10 or 7 seconds to make an identification, and the
airplane had either partially obscured or entirely obscured features. See the
appendix for the images used and an example scenario. Respondents were then
asked to identify the aircraft in each scenario, after which they received an
experimental treatment in the form of a decision aid. The decision aid was
described as a team member that would provide a recommendation and would come
in the form of either an AI algorithm or human analyst, with varying degrees
of testing and training. The respondent would then have the opportunity to
either change their answer or keep it the same as their initial
identification. If the respondent was shown the control, then their initial
answer was maintained as their final answer without the opportunity to switch.

The treatments were randomized across a two-by-four experimental design with a
control condition for a total of nine possible treatment conditions. The
decision aid could be a low-confidence human analyst or AI algorithm, a high-
confidence human analyst or AI algorithm, or no suggestion at all for the
control. We also randomized whether the recommendation of the decision aid was
correct or incorrect. See the online appendix for specific wording. The
language used to describe the human analyst and the AI algorithm and the
varying levels of confidence in the system were kept identical except for
whether a human or AI was providing the suggestion:

  * •

Human analyst low confidence — correct identification

  * •

Human analyst high confidence — correct identification

  * •

AI algorithm low confidence — correct identification

  * •

AI algorithm high confidence — correct identification

  * •

Human analyst low confidence — incorrect identification

  * •

Human analyst high confidence — incorrect identification

  * •

AI algorithm low confidence — incorrect identification

  * •

AI algorithm high confidence — incorrect identification

  * •

No identification suggestion

#### Dependent Variables

Unless otherwise noted, the dependent variable in the analyses below is a
binary variable of whether a respondent "switched" their answer after being
shown a treatment (1 if switched, 0 otherwise).

#### Independent Variables

We leverage the experimental design and additional questions to test the
hypotheses above, including demographic data on respondents, batteries of
questions about their experience with and attitudes about AI, and more. We
operationalize trust in AI, confidence in the treatment, self-confidence in
the ability to complete the task, cognitive pressure, and AI background as
follows.

To test hypothesis 1, we introduced questions designed to measure
participants’ experience with, knowledge of, and familiarity with AI and
automated systems. These questions include ones designed to test their factual
knowledge of AI and AI technologies, such as if they have a background in AI
or computer science more broadly, were able to identify technologies that used
AI, and if they can correctly answer questions about artificial intelligence
technologies. Another set of questions assesses experience with AI, such as
whether individuals had used AI-based systems in the past, in either work or
home contexts. Finally, we gauge overall familiarity with AI with a battery of
general interest, awareness, and exposure to the concepts of AI in the news,
from colleagues or friends, or other sources. Additional details are in the
online appendix.

These questions, in combination, allow us to create a standardized measure of
AI background, thus enabling us to more reliably understand how previous
interactions with or knowledge of AI influence how people interact with
automated systems and whether the depth of that exposure plays a role factor.
Moreover, the questions also reflect the commonly accepted styles and subject
matters for measuring these indicators, making the results of this study more
comparable with previous studies done previously (Zhang and Dafoe, , 2019).
This comparability allows this study to build upon current understandings of
human interactions with AI and automated systems.

  * •

Overall AI Knowledge: measured using how well respondents performed on two AI
knowledge quiz questions.

  * •

Overall AI Familiarity: measured using a scale of how familiar respondents
were with AI, based on if they had heard about AI from friends or family, read
about AI in the news, from other avenues, or not at all.

  * •

Overall AI Experience: measured using two indicators, 1) whether an individual
had some degree of coding or programming experience, and 2) whether a
respondent had used a specific application and if they viewed that application
to utilize AI technology.

  * •

AI Background Index: overall knowledge, experience, and familiarity with AI,
taken together, in the form of a normalized index. Knowledge was attributed a
smaller weight of 1/5, while experience and familiarity were each weighted at
2/5. This was because the knowledge questions were difficult for respondents,
with only 25 respondents out of the total 9,016 getting both questions 100%

Figure A.3 in the appendix plots the mean values for each variable. We test
hypotheses 2-4 with the following variables.

  * •

Trust in AI: To test hypothesis 2, we created an “AI Beliefs” battery of
questions that can serve as a baseline measure for how trusting an individual
respondent is of AI and AI-enabled systems. Our battery was derived from the
top three factor-loaded items from each positive and negative sub-scale from
an AI-specific scale based on a validated index that maps attitudes towards
technologies, the Technology Readiness Index 2.0. (Parasuraman and Colby, ,
2014; Schepman and Rodway, , 2020; Lam et al., , 2008).666The full list of
questions is viewable in the appendix.

  * •

Treatment Confidence: To test hypothesis 3, confidence in the decision aid was
set by the above language for each treatment, with high and low confidence
language held constant across the human analysts and AI algorithm treatments.
High confidence language was that either the analyst or AI algorithm "has
undergone extensive testing and training to identify airplanes under these
conditions." In contrast, the low-confidence language described the decision
support aid as "still undergoing testing and being trained to identify
airplanes under these conditions."

  * •

Self-Confidence: To test hypothesis 4, we operationalize the relationship
between self-confidence and answer switching by examining how the number of
correct identifications by participants in the practice rounds influences the
probability of switching answers during the genuine identification rounds. The
live feedback element of the practice rounds provides the respondent with a
benchmark for how well they could complete the task, absent any pressurizing
cognitive constraints.

Below, we show the summary statistics and correlation matrix for key variables
in Table 3.2 and appendix Figure A.1. As described below, since respondents
each completed ten identification rounds, we present the statistics here
showing the table disaggregated by identification, as opposed to grouped by
respondent.

Statistic | N | Mean | St. Dev. | Min | Max
---|---|---|---|---|---
Sex (Female = 1) | 89,810 | 0.52 | 0.50 | 0.00 | 1.00
Age | 90,160 | 46.51 | 16.56 | 18 | 93
Political Ideology (Right = 10) | 70,220 | 5.61 | 2.51 | 0.00 | 10.00
Highest Level of Education | 90,160 | 2.24 | 1.56 | 0 | 5
Total Number of Practice Rounds Correct | 90,160 | 2.65 | 1.21 | 0 | 5
Recieved a High Confidence Treatment | 90,160 | 0.45 | 0.50 | 0 | 1
Recieved a Low Confidence Treatment | 90,160 | 0.44 | 0.50 | 0 | 1
Recieved an AI Algorithm Treatment | 90,160 | 0.44 | 0.50 | 0 | 1
Recieved a Human Analyst Treatment | 90,160 | 0.45 | 0.50 | 0 | 1
Switched Identification After Treatment | 80,158 | 0.23 | 0.42 | 0.00 | 1.00
AI Background Index | 90,160 | 0.23 | 0.15 | 0.00 | 0.86
Trust in AI | 73,990 | 16.05 | 4.31 | 0.00 | 28.00

Table 1: Summary Statistics

It is necessary to acknowledge the complex relationships between these
different variables. We theorize above that experience, familiarity,
knowledge, trust, and confidence are closely related but distinct factors that
may influence rates of automation bias. Our analysis reveals a positive,
statistically significant correlation between background and trust in AI,
demonstrating that attitudinal factors may be an intervening variable in the
relationship between experiential factors and rates of automation bias. We
attempted to separate these factors in our analysis to gain a more nuanced
understanding of the interplay between these variables. However, there may be
some degree of interdependence between these factors, which we attempt to
disentangle below as much as possible.

##  4 Results

We start by evaluating the "rate of switching," the rate at which respondents
opted to change their initial identification after being presented with the
treatment condition in each round, our main dependent variable. Figure 2 below
illustrates the mean switching rate for respondents that received each
treatment: high or low-confidence human analyst or AI algorithm decision
support. Overall, individuals were more likely to switch to high-confidence
treatments. At the high-confidence levels, individuals switched most
frequently when it was a human analyst treatment, while at low-confidence
levels, individuals were likelier to switch if it was an AI algorithm
treatment. This suggests that the general public has different thresholds for
error tolerance and expected performance for humans and AI systems. When the
AI or human analyst was described as having undergone "extensive testing and
training," a human elicits the most confidence. However, the AI or human
analyst was described as "still undergoing testing and training;" an
individual might view any AI, even an incompletely trained and tested AI
algorithm, as preferable to a human analyst who might be very susceptible to
human error and mistakes.

Figure 2: Mean Level of Switching Per Treatment Type

Table 4 below presents the results of an initial regression analysis designed
to further test our hypotheses in models 1 and 2. The dependent variable is
the same switching variable described above. For simplicity of display and
interpretation, we use OLS even though the dependent variable is binary. The
results are consistent, using logit models. The universe of cases is one
observation per respondent identification, so there are ten observations
(reflecting the ten game rounds) total per respondent. We, therefore, also
cluster standard errors on the respondent. We include country-fixed effects
(with the United States as a base excluded country) to account for country
effects.

| (1) | (2) | (3) | (4) | (5) | (6) | (7)
---|---|---|---|---|---|---|---
|  Overall Binary b/SE |  AI Condition Binary b/SE |  Human Condition Binary b/SE |  Switching in AI Condition AI Familiarity Model b/SE |  Switching in AI Condition AI Knowledge Model b/SE |  Switching in AI Condition AI Experience Model b/SE |  Switching in AI Condition AI Background Model b/SE
Political Ideology |  |  |  | 0.032∗∗∗ | 0.037∗∗∗ | 0.039∗∗∗ | 0.034∗∗∗
|  |  |  | (0.010) | (0.010) | (0.010) | (0.010)
Level of Difficulty | 0.034∗∗∗ | 0.035∗∗∗ | 0.041∗∗∗ | 0.200∗∗∗ | 0.200∗∗∗ | 0.201∗∗∗ | 0.200∗∗∗
| (0.003) | (0.004) | (0.004) | (0.034) | (0.034) | (0.034) | (0.034)
Practice Round Accuracy | -0.008∗∗∗ | -0.011∗∗∗ | -0.008∗∗∗ | -0.042∗∗ | -0.047∗∗ | -0.047∗∗ | -0.044∗∗
| (0.002) | (0.002) | (0.002) | (0.019) | (0.019) | (0.019) | (0.019)
Age | -0.000∗ | -0.000∗∗ | -0.000 | -0.000 | -0.001 | -0.001 | -0.000
| (0.000) | (0.000) | (0.000) | (0.002) | (0.002) | (0.002) | (0.002)
Gender | 0.030∗∗∗ | 0.035∗∗∗ | 0.034∗∗∗ | 0.225∗∗∗ | 0.211∗∗∗ | 0.206∗∗∗ | 0.218∗∗∗
| (0.004) | (0.006) | (0.005) | (0.049) | (0.049) | (0.049) | (0.050)
Level of Education | 0.005∗∗∗ | 0.007∗∗∗ | 0.005∗∗ | 0.025∗ | 0.043∗∗∗ | 0.044∗∗∗ | 0.036∗∗
| (0.001) | (0.002) | (0.002) | (0.015) | (0.015) | (0.015) | (0.015)
Treatment Condition: AI | 0.045∗∗∗ |  |  |  |  |  |
| (0.003) |  |  |  |  |  |
Treatment Condition: High Confidence | 0.056∗∗∗ | 0.017∗∗∗ | 0.016∗∗∗ | 0.093∗∗∗ | 0.092∗∗∗ | 0.093∗∗∗ | 0.092∗∗∗
| (0.003) | (0.004) | (0.004) | (0.034) | (0.034) | (0.034) | (0.034)
AI Familiarity |  |  |  | 1.928∗∗∗ |  |  |
|  |  |  | (0.388) |  |  |
AI Familiarity Squared |  |  |  | -1.788∗∗∗ |  |  |
|  |  |  | (0.463) |  |  |
AI Knowledge |  |  |  |  | 0.954∗∗ |  |
|  |  |  |  | (0.412) |  |
AI Knowledge Squared |  |  |  |  | -1.366∗∗∗ |  |
|  |  |  |  | (0.530) |  |
AI Experience |  |  |  |  |  | 0.923∗∗∗ |
|  |  |  |  |  | (0.280) |
AI Experience Squared |  |  |  |  |  | -1.134∗∗∗ |
|  |  |  |  |  | (0.300) |
AI Background Index |  |  |  |  |  |  | 1.289∗∗∗
|  |  |  |  |  |  | (0.483)
AI Background Index Squared |  |  |  |  |  |  | -1.547∗∗
|  |  |  |  |  |  | (0.653)
Normalized AI Sentiment |  |  |  | -0.349∗∗ | -0.203 | -0.223 | -0.274∗
|  |  |  | (0.158) | (0.159) | (0.160) | (0.161)
Constant | 0.147∗∗∗ | 0.219∗∗∗ | 0.196∗∗∗ | -1.588∗∗∗ | -1.554∗∗∗ | -1.579∗∗∗ | -1.615∗∗∗
| (0.011) | (0.015) | (0.015) | (0.167) | (0.174) | (0.171) | (0.180)
Observations | 90160 | 39903 | 40255 | 26214 | 26214 | 26214 | 26214
$R^{2}$ | 0.020 | 0.015 | 0.014 |  |  |  |
Pseudo $R^{2}$ |  |  |  | 0.010 | 0.007 | 0.008 | 0.007
Log Likelihood | -44495.143 | -21683.412 | -21555.620 | -14202.630 | -14237.277 | -14229.660 | -14236.006
F | 88.738 | 26.239 | 27.421 |  |  |  |

Table 2: Analysis of Respondent Switching

Notes: Standard errors clustered by respondent in parentheses. *p<0.10;
**p<0.05; ***p<0.01.

The results show an interaction between trust, confidence, knowledge,
experience, and familiarity with AI, consistent with our theoretical
expectations. There is a significant relationship between overall background
in AI (hypothesis 1), level of trust in AI (hypothesis 2), treatment
confidence level (hypothesis 3), practice round accuracy (hypothesis 4), and
rate of switching at the $p<0.05$ level and above. We explore each of these
results further in the subsequent sections.

We further test hypothesis 1 regarding how overall prior exposure to AI should
influence automation bias in models 4-5 of Table 4. We switch to a logit model
here in part to highlight the consistency of the results across different
model specifications, but the appendix shows that the model specification does
not impact the results. We restrict the universe of cases to just those
respondents that receive the AI treatment condition and evaluate the impact of
our AI Background Index variable, which includes the knowledge, experience,
and familiarity sub-indices which we also examine below. Given our theoretical
prediction that the relationship between overall background in AI and answer
switching by respondents will be non-linear in hypothesis 1, we square the AI
Background Index variable.

The statistical results show strong and significant interaction effects and
highlight the non-linearity of the effects. We begin evaluating the
substantive effects by switching back from regression to comparing mean values
of switching after receiving an AI algorithm treatment, as values of the AI
Background Index increase. Figure 3 plots the rate of switching. The predicted
non-linear relationship is evident, supporting the theory laid out in Figure
1. Reliance on AI (in this case, the rate of switching when receiving an AI
treatment) initially increases steadily at the lowest combined level of AI
familiarity, knowledge, and experience, peaking when AI Background Index is at
a middling level, around the mean ($0.224$) and median values ($0.196$) of AI
Background Index, before it decreases and levels out as AI Background Index
increases past the 3rd quantile mark of around $0.313$ and trends towards the
maximum of $0.900$.

Figure 3: Rate of switching when treatment is an AI algorithm, given AI
Background

We then evaluate the substantive effects of each of the sub-indices that
together constitute the AI Background Index, and the background index itself,
in Figure 4, based on the additional models in Table 4. We generate predicted
probabilities for switching across each of the values of the Overall AI
Familiarity, Overall AI Knowledge, Overall AI Experience, and AI Background
Index variables, holding other variables at their means, and plot the results.
The results curves look slightly different than in Figure 3 above due to the
addition of control variables in the regression framework, the addition of
country fixed effects, and clustered standard errors. Their consistency,
however, demonstrates a clear pattern of results that supports our hypothesis.

|
---|---

(a)

(b)

(c)

(d)

Figure 4: Predicted Probability of Switching: AI Background and Index
Components

The probability of switching in the AI condition is 19% when Overall AI
Familiarity is lowest. Consistent with the Dunning-Kruger effect (Sanchez and
Dunning, , 2018), as perceived AI familiarity grows, respondents become more
likely to switch their answers in response to information provided by an AI
decision aid, peaking at 29%. As Overall AI Familiarity gets to the highest
level, though, the probability of switching goes down again to 22%.

We generally see similar effects for Overall AI Knowledge (starts at 22%,
peaks at 25%, ends at 16%) and Overall AI Experience (starts at 20%, peaks at
25%, ends at 18%), with the Overall AI Knowledge curve being slightly steeper,
which is expected considering the much higher level of difficulty of the AI
knowledge questions. The overall index starts at 20% when AI Background Index
is lowest, peaks at 25% as background rises, and drops to 16% when AI
Background Index is at its highest. The results provide strong initial support
for hypothesis 1.

###  4.1 Attitudinal: trust and confidence

We now look at the impact of the level of confidence and trust the respondents
have in the decision-making aid and themselves on the rate of post-treatment
identification switching, the main dependent variable. As outlined earlier, we
measure confidence in the system with our reliability treatment condition,
Treatment Confidence). As a reminder, we also measure self-confidence as the
percentage of practice rounds accurately identified (Total Number of Practice
Rounds Correct). Respondents received live feedback on their accuracy in these
initial five practice rounds and, thus, had an approximate sense of how well
they completed the task. We also separately generated a proxy for trust in AI
using a battery of questions that generated a score for respondents (Trust in
AI) based on how negatively or positively they reacted to statements about AI.

Figure 5 below shows the percentage of respondents that agreed or disagreed
with each statement used to generate the AI Trust Index.

Figure 5: Responses to AI Beliefs Battery

Overall, most respondents have some degree of openness to artificial
intelligence. A majority found artificial intelligence to be exciting and to
have many potential benefits, with 62% and 73%, respectively, of the sample
either responding with "agree" or "strongly agree", while over half (53%) of
respondents indicated an interest in using "artificially intelligent systems."
More pertinent for this study, about 46% even agreed that AI was, on balance,
more accurate than humans. There was some hesitation and worry over the use of
AI. However, 43% responded that they believed AI to be "dangerous," 39% found
that organizations use AI unethically, and 36% even argued that they find AI
"sinister." Together, the responses sketch a general picture of how open and
trusting individuals were of AI technologies and allowed us to generate the
Trust in AI index.

To further test hypothesis 2, we assess the relationship between the
intersection of a respondent’s trust and confidence in AI and their likelihood
to listen to an AI algorithm-based decision aid or be more biased towards
automation. Figure 6 plots respondents’ rate of switching their answers when
shown an AI algorithm treatment, given their overall level of trust in AI-
based technology and systems. Generally, the switching rate for both high and
low-confidence AI algorithm treatments showed a similar, non-linear trend as
trust in AI increased but was overall higher for high-confidence treatments.
Similar to the combined effects of knowledge, experience, and familiarity with
AI, individuals are more prone to automation aversion at the lowest levels of
trust.

Confidence in the specific AI algorithm mattered less to respondents with
lower general levels of trust in AI. For those respondents with an Trust in AI
index score less than the median ($0.571$) (or mean $0.573$), their personal
perception of the technology outweighed the influence of the experimental
information on the level of testing or training the AI algorithm received.
However, Respondents with a greater than mean or median level of trust in AI
were substantially more likely to switch answers if the AI algorithm was
established to have a higher degree of accuracy by already having completed
"extensive testing and training." This suggests that once the barrier of trust
in the technology is overcome, confidence — the testing, evaluation, and the
expected accuracy of the system — becomes a more central factor in willingness
to rely on the technology.

Figure 6: Average Switching Given an AI Treatment Across AI Trust Levels

The above also shows evidence for hypothesis 3. In general, when the decision
aid for the respondent is described in terms that elicit higher degrees of
confidence, respondents are significantly more likely to change their answers.
The findings hold whether respondents received the human or AI experimental
treatments.

Finally, we find support for hypothesis 4. Figure 7 shows the rate of a
respondent switching answers after being shown each treatment type, given
their practice round accuracy. The higher the score of a respondent on the
practice round identification tasks, the less likely they were to switch their
answers in the "real" identification rounds for most treatment scenarios.
Self-confidence, derived from the practice rounds, serves as perceived task
competence—the higher the level of self-confidence, the lower the probability
that respondents will rely on decision aids.

|
---|---

(a)

(b)

Figure 7: Rate of Switching Given Treatment Type and Accuracy in Practice
Rounds

For those respondents that got no practice round identifications correct, they
switched their answers most when presented with an AI treatment, having
changed their answers over 25% of the time, whereas those respondents who
achieved a perfect score in the practice round switched their answers only 18%
of the time. While respondents that had low practice round accuracy switched
their answers at a relatively constant rate for AI treatments, regardless of
how they were described, those that had the highest practice round accuracy
changed their answers for high-confidence AI treatments about 20% of the time,
but noticeably less frequently for low-confidence AI treatments (16%). The one
exception involves the high-confidence human analyst condition; regardless of
the degree of practice round accuracy, the switching rate remained constant,
at around 24% of the time. Excluding that exception, this shows that there is
more variation in the confidence respondents have in advice from humans than
confidence respondents have in advice from AI, especially when one’s own
perceived competency and self-confidence are taken into account.

##  5 Conclusion

What influences the adoption of new technologies is a crucial question for
international politics. The use of artificial intelligence by governments, in
particular, involves questions of trust, confidence, and human agency that are
fundamental to our understanding of international relations, particularly as
AI increasingly shapes political decisions. As the integration of AI into
militaries worldwide continues, how individuals and organizations make
decisions about adopting AI will become even more critical. Research on
automation bias suggests that humans can often be overconfident in AI, whereas
research on algorithm aversion shows that, as the stakes of a decision rise,
humans because more cautious about trusting algorithms. We theorize about the
relationship between background knowledge about AI, trust in AI, and how these
interact with other factors to influence the probability of automation bias in
a critical international political context. We test our hypotheses across a
sample of 9000 respondents in 9 countries with extensive but varying levels of
AI industries and military investments. The results strongly support the
theory, especially concerning AI background knowledge. A version of the
Dunning Kruger effect appears to be at play, whereby those with the lowest
level of experience with AI are slightly more likely to be algorithm-averse,
then automation bias occurs at lower levels of knowledge before leveling off
as a respondent’s AI background reaches the highest levels.

We find that there is a nonlinear relationship between how likely an
individual will rely on an automated, AI-based decision aid in the
international security context and their overall exposure to AI, where
exposure encompasses the extent to which an individual has a general
familiarity and awareness of the technology, concrete knowledge of how the
technology works, and firsthand experience using the technology. The
interaction of experience, knowledge, and familiarity drives responses to
decision aids, where at the lowest levels of these factors, automation
aversion is most likely, and at average levels, automation bias is most
likely.

Only at the highest levels of overall exposure to AI are individuals more
balanced in whether they rely on the AI decision aid for military aircraft
identification, and we see greater accuracy in task completion, particularly
amongst those respondents who had low levels of self-confidence in their
ability to complete the task. Appendix figure A.2 details these findings,
illustrating how respondents with the lowest levels of self-confidence in the
ability to complete the task were more likely to switch initially incorrect
answers when suggested a correction by an algorithm-based decision aid, with
greater accuracy as their background in AI increased, from just around 50%
accuracy to nearly 70% accuracy.

We also find that trust and confidence play a significant role in whether an
individual will act on recommendations made by the automated decision aid. We
find that the more positive someone’s attitudes towards AI technology are, the
more trusting they are in that technology, and the more likely the individual
is to trust a recommendation of an AI-enabled system over that of a human
analyst. Similarly, we find that the greater the described confidence and
accuracy in the system, the more likely a respondent is to follow the system’s
decision suggestions. However, we find that this is often tempered by a
respondent’s self-confidence—the more accurate a respondent was described to
be in the initial practice segment, the less likely they are to second-guess
their decisions and over-rely on a decision aid, regardless of whether it was
a human analyst or an AI algorithm making the suggestion.

There are several limitations to this study that could inform future research.
First, this study focused on the military domain because most previous
automation bias experiments did not. Future research could incorporate a non-
military scenario and a military scenario to allow for the direct
comparability of results. Second, rather than focusing solely on public views,
future research could explore the perspectives of elites or those directly
involved in national security and military efforts in various countries.
Third, future research could look at additional tasks in the military domain
to see how automation bias and algorithm aversion vary across the type of
task.

Overall, these results demonstrate that the way people think about AI and the
features of any specific AI-enabled system influences how the system is used
in an international security context, including rates of automation bias and
algorithmic aversion. It also builds on existing behavioral science research
on cognitive biases and receptivity to using automated and increasingly
computerized tools. These findings not only contribute new theory but also
illustrate the importance of understanding trust and confidence in AI adoption
and how AI will broadly influence international relations.

## References

  * Alon-Barkat and Busuioc, (2022) Alon-Barkat, S. and Busuioc, M. (2022).  Human-ai interactions in public sector decision-making: ’automation bias’ and ’selective adherence’ to algorithmic advice.  Journal of Public Administration Research and Theory.
  * Alvarado-Valencia and Barrero, (2014) Alvarado-Valencia, J. A. and Barrero, L. H. (2014).  Reliance, trust and heuristics in judgemental forecasting.  Computers in Human Behavior, 36:102–113.
  * Atherton, (2022) Atherton, K. (2022).  Understanding the errors introduced by military ai applications.
  * Awad et al., (2018) Awad, E., Dsouza, S., Kim, R., Schulz, J., Henrich, J., Shariff, A., Bonnefon, J.-F., and Rahwan, I. (2018).  The moral machine experiment.  Nature, 563:59–64.
  * Bahmanziari et al., (2016) Bahmanziari, T., Pearson, J. M., and Crosby, L. (2016).  Is trust important in technology adoption? a policy capturing approach.  Journal of Computer Information Systems, 43(4):46–54.
  * Bailey and Scerbo, (2007) Bailey, N. R. and Scerbo, M. W. (2007).  Automation-induced complacency for monitoring highly reliable systems: the role of task complexity, system experience, and operator trust.  Theoretical Issues in Ergonomics Science, 8(4):321–348.
  * Blosch and Fenn, (2018) Blosch, M. and Fenn, J. (2018).  Understanding gartner’s hype cycles.  Technical report, Gartner Research.
  * Chong et al., (2022) Chong, L., Zhang, G., Goucher-Lambert, K., Kotovsky, K., and Cagan, J. (2022).  Human confidence in artificial intelligence and in themselves: The evolution and impact of confidence on adoption of ai advice.  Computers in Human Behavior, 127.
  * Cummings, (2004) Cummings, M. (2004).  Automation bias in intelligent time critical decision support systems.  In AIAA 1st Intelligent Systems Technical Conference, page 6313\.
  * Dujmovic, (2017) Dujmovic, J. (2017).  What’s holding back artificial intelligence? americans don’t trust it.
  * Fitzpatrick, (2019) Fitzpatrick, M. (2019).  Artificial intelligence and nuclear command and control.  Survival, 61(3):81–92.
  * Goddard et al., (2012) Goddard, K., Roudsari, A., and Wyatt, J. C. (2012).  Automation bias: A systematic review of frequency, effect mediators, and mitigators.  Journal of the American Medical Informatics Association, 19(1).
  * Goddard et al., (2014) Goddard, K., Roudsari, A., and Wyatt, J. C. (2014).  Automation bias: Empirical results assessing influencing factors.  International Journal of Medical Informatics, 83(5):368–375.
  * Haas, (1992) Haas, P. M. (1992).  Introduction: epistemic communities and international policy coordination.  International organization, 46(1):1–35.
  * HAI at Stanford University, (2021) HAI at Stanford University (2021).  Global ai vibrancy tool: Who’s leading the global ai race?
  * Hawley, (2017) Hawley, J. K. (2017).  Patriot wars.  Report: Voices in the field series, Center for a New American Security.
  * Hoffman, (2002) Hoffman, A. M. (2002).  A conceptualization of trust in international relations.  European Journal of International Relations, 8(3):375–401.
  * Horowitz, (2018) Horowitz, M. C. (2018).  Artificial intelligence, international competition, and the balance of power.  Texas National Security Review, 1(3).
  * Horowitz, (2019) Horowitz, M. C. (2019).  When speed kills: Lethal autonomous weapon systems, deterrence and stability.  Journal of Strategic Studies, 42(6):764–788.
  * Horowitz, (2020) Horowitz, M. C. (2020).  Do emerging military technologies matter for international politics?  Annual Review of Political Science, 23:385–400.
  * Horowitz et al., (2016) Horowitz, M. C., Kreps, S. E., and Fuhrmann, M. (2016).  Separating fact from fiction in the debate over drone proliferation.  International Security, 41(2):7–42.
  * Horowitz and Lin-Greenberg, (2022) Horowitz, M. C. and Lin-Greenberg, E. (2022).  Algorithms and influence artificial intelligence and crisis decision-making.  International Studies Quarterly, 66(4):sqac069.
  * Hudson, (2019) Hudson, V. M. (2019).  Artificial Intelligence and International Politics.  Routledge.
  * Jensen et al., (2020) Jensen, B. M., Whyte, C., and Cuomo, S. (2020).  Algorithms at war: The promise, peril, and limits of artificial intelligence.  International Studies Review, 22(3):526–550.
  * (25) Johnson, J. (2021a).  Artificial intelligence and the future of warfare: The usa, china, and strategic stability.  In Artificial Intelligence and the Future of Warfare. Manchester University Press.
  * (26) Johnson, J. (2021b).  ‘catalytic nuclear war’ in the age of artificial intelligence & autonomy: Emerging military technology and escalation risk between nuclear-armed states.  Journal of Strategic Studies, pages 1–41.
  * Johnson, (2022) Johnson, J. (2022).  Delegating strategic decision-making to machines: Dr. strangelove redux?  Daedalus, 45(3):439–477.
  * Johnston and Sarbahi, (2016) Johnston, P. B. and Sarbahi, A. K. (2016).  The impact of us drone strikes on terrorism in pakistan.  International Studies Quarterly, 60(2):203–219.
  * Kertzer, (2022) Kertzer, J. D. (2022).  Re-assessing elite-public gaps in political behavior.  American Journal of Political Science, 66(3):539–553.
  * Kertzer and Renshon, (2022) Kertzer, J. D. and Renshon, J. (2022).  Experiments and surveys on political elites.  Annual Review of Political Science, 25:529–550.
  * Kreps, (2016) Kreps, S. E. (2016).  Drones: What Everyone Needs to Know.  Oxford University Press.
  * Kreps and Wallace, (2016) Kreps, S. E. and Wallace, G. P. (2016).  International law, military effectiveness, and public support for drone strikes.  Journal of Peace Research, 53(6):830–844.
  * Kruger and Dunning, (1999) Kruger, J. and Dunning, D. (1999).  Unskilled and unaware of it: How difficulties in recognizing one’s own incompetence lead to inflated self-assessments.  Journal of Personality and Social Psychology, 77(6):1121.
  * Kydd, (2007) Kydd, A. H. (2007).  Trust and mistrust in international relations.  Princeton University Press.
  * Lam et al., (2008) Lam, S. Y., Chiang, J., and Parasuraman, A. (2008).  The effects of the dimensions of technology readiness on technology acceptance: An empirical analysis.  Journal of Interactive Marketing, 22(4):19–39.
  * Lin-Greenberg, (2022) Lin-Greenberg, E. (2022).  Wargame of drones: Remotely piloted aircraft and crisis escalation.  Journal of Conflict Resolution, 66(10):1737–1765.
  * Luhmann, (1979) Luhmann, N. (1979).  Trust and Power.  John Wiley and Sons.
  * Luhmann, (1988) Luhmann, N. (1988).  Familiarity, confidence, trust: Problems and perspectives.  In Gambetta, D., editor, Trust: Making and Breaking Cooperative Relations. Basil Blackwell.
  * Lyell and Coiera, (2016) Lyell, D. and Coiera, E. (2016).  Automation bias and verification complexity: A systematic review.  Journal of the American Medical Informatics Association, 24(2):423–431.
  * Metz, (2023) Metz, C. (2023).  What makes a.i. chatbots go wrong?
  * Ministry of Defense, (2004) Ministry of Defense (2004).  Aircraft accident to royal air force tornado gr mk4a zg710.  Military aircraft accident summary, Directorate of Air Staff.
  * Mir and Moore, (2019) Mir, A. and Moore, D. (2019).  Drones, surveillance, and violence: Theory and evidence from a us drone program.  International Studies Quarterly, 63(4):846–862.
  * Mosier and Skitka, (1996) Mosier, K. L. and Skitka, L. (1996).  Human decision makers and automated decision aids: Made for each other?  In Parasuraman, R. and Mouloua, M., editors, Automation and Human Performance: Theory and Applications. Lawrence Erlbaum Associates.
  * OECD AI, (2022) OECD AI (2022).  National ai policies & strategies.
  * Parasuraman and Colby, (2014) Parasuraman, A. and Colby, C. L. (2014).  An updated and streamlined technology readiness index: Tri 2.0.  Journal of Service Research, 18(1):59–74.
  * Parasuraman and Manzey, (2010) Parasuraman, R. and Manzey, D. H. (2010).  Complacency and bias in human use of automation: an attentional integration.  Human Factors: The Journal of the Human Factors and Ergonomics Society, 52(3):381–410.
  * Povyakalo et al., (2013) Povyakalo, A. A., Alberdi, E., Strigini, L., and Ayton, P. (2013).  How to discriminate between computer-aided and computer-hindered decisions.  Medical Decision Making, 33(1):98–107.
  * Reiter, (1994) Reiter, D. (1994).  Learning, realism, and alliances: The weight of the shadow of the past.  World Politics, 46(4):490–526.
  * Sanchez and Dunning, (2018) Sanchez, C. and Dunning, D. (2018).  Overconfidence among beginners: Is a little learning a dangerous thing?  Journal of Personality and Social Psychology, 114(1):10–28.
  * Scharre, (2018) Scharre, P. (2018).  Army of none: Autonomous weapons and the future of war.  WW Norton & Company.
  * Schepman and Rodway, (2020) Schepman, A. and Rodway, P. (2020).  Initial validation of the general attitudes towards artificial intelligence scale.  Computers in Human Behavior Reports, 22(4).
  * Schmidt, (2022) Schmidt, E. (2022).  Ai, great power competition & national security.  Daedalus, 151(2):288–298.
  * Sechser et al., (2019) Sechser, T. S., Narang, N., and Talmadge, C. (2019).  Emerging technologies and strategic stability in peacetime, crisis, and war.  Journal of Strategic Studies, 24(6):727–735.
  * Seligman and Montgomery, (2019) Seligman, A. and Montgomery, D. W. (2019).  The tragedy of human rights: Liberalism and the loss of belonging.  Society, 56(3):203–209.
  * Simmons and Massey, (2015) Simmons, J. P. and Massey, C. (2015).  Algorithm aversion: People erroneously avoid algorithms after seeing them err.  Journal of Experimental Psychology: General, 144(1):114–126.
  * Skitka et al., (1999) Skitka, L. J., Mosier, K. L., and Burdick, M. (1999).  Does automation bias decision-making?  International Journal of Human-Computer Studies, 51(5):991–1006.
  * Southern and Arnstern, (2009) Southern, W. N. and Arnstern, J. H. (2009).  The effect of erroneous computer interpretation of ecgs on resident decision making.  Society for Medical Decision Making, 29(3):372–376.
  * Tetlock, (2009) Tetlock, P. E. (2009).  Expert political judgment.  Princeton University Press.
  * Tomz, (2007) Tomz, M. (2007).  Domestic audience costs in international relations: An experimental approach.  International Organization, 61(4):821–840.
  * Tomz and Weeks, (2013) Tomz, M. R. and Weeks, J. L. (2013).  Public opinion and the democratic peace.  American Political Science Review, 107(4):849–865.
  * Tragger and Vavreck, (2011) Tragger, R. F. and Vavreck, L. (2011).  The political costs of crisis bargaining: Presidential rhetoric and the role of party.  American Journal of Political Science, 55(3):526–545.
  * Vincent, (2022) Vincent, J. (2022).  Ai-generated answers temporarily banned on stack overflow.
  * Wallace, (2013) Wallace, G. P. (2013).  International law and public attitudes toward torture: An experimental study.  International Organization, 57(1):105–140.
  * Wallace, (2014) Wallace, G. P. (2014).  Martial law? military experience, international law, and support for torture.  International Studies Quarterly, 58(3):501–514.
  * Welsh, (2018) Welsh, M. (2018).  Bias in Science and Communication.  IOP Publishing.
  * Zhang and Dafoe, (2019) Zhang, B. and Dafoe, A. (2019).  Artificial intelligence: American attitudes and trends.  Technical report, Center for Governance of AI.

##  Appendix A Online Appendix

###  A.1 Additional Figures and Tables

Figure A.1: Correlations and their significance between select variables.
Figure A.2: Accuracy of respondents in aircraft identification by their
background in AI and degree of self-confidence. Respondents were coded as
having high self-confidence if they obtained a score of 60% or greater in the
practice rounds. Accurate use of a decision aid was considered when an
original identification was incorrect, the treatment suggestion was correct,
and the respondent switched to the correct answer (scored as 100). Inaccurate
use of a decision aid was considered when an original identification was
correct, the treatment suggestion was incorrect, and the respondent switched
to the incorrect answer (scored as 0). Figure A.3: Mean Values of AI
Background and Sub-indices

### Example Test Scenarios

Figure A.4: An example of an airplane identification scenario at the easier
difficulty level. Figure A.5: An example of an airplane identification
scenario at the more challenging difficulty level.

### Trust in AI Battery

Please indicate the extent to which you agree or disagree with each of the
following statements:

  * •

"Organizations use artificial intelligence unethically."

  * •

"I am interested in using artificially intelligent systems in my daily life."

  * •

"I find artificial intelligence sinister."

  * •

"I think artificial intelligence is dangerous."

  * •

"Artificial intelligence is exciting."

  * •

"I think artificial intelligence, on balance, is more accurate than humans."

  * •

"There are many beneficial applications of artificial intelligence."

Answer options:

  * •

Strongly disagree

  * •

Somewhat disagree

  * •

Neither agree nor disagree

  * •

Somewhat agree

  * •

Strongly agree

  * •

Don’t know