# Ethics and Governance of Artificial Intelligence: Evidence from a Survey of
Machine Learning Researchers

Baobao Zhang1,∗, Markus Anderljung2,∗, Lauren Kahn3, Noemi Dreksler2, Michael
C. Horowitz3 and Allan Dafoe2
1Department of Government, Cornell University
2Centre for the Governance of AI, Future of Humanity Institute, University of
Oxford
3Perry World House, University of Pennsylvania
∗ Corresponding authors<EMAIL_ADDRESS><EMAIL_ADDRESS>
###### Abstract

Machine learning (ML) and artificial intelligence (AI) researchers play an
important role in the ethics and governance of AI, including taking action
against what they perceive to be unethical uses of AI (?, ?). Nevertheless,
this influential group’s attitudes are not well understood, which undermines
our ability to discern consensuses or disagreements between AI/ML researchers.
To examine these researchers’ views, we conducted a survey of those who
published in the top AI/ML conferences ($N=524$). We compare these results
with those from a 2016 survey of AI/ML researchers (?) and a 2018 survey of
the US public (?). We find that AI/ML researchers place high levels of trust
in international organizations and scientific organizations to shape the
development and use of AI in the public interest; moderate trust in most
Western tech companies; and low trust in national militaries, Chinese tech
companies, and Facebook. While the respondents were overwhelmingly opposed to
AI/ML researchers working on lethal autonomous weapons, they are less opposed
to researchers working on other military applications of AI, particularly
logistics algorithms. A strong majority of respondents think that AI safety
research should be prioritized and that ML institutions should conduct pre-
publication review to assess potential harms. Being closer to the technology
itself, AI/ML researchers are well placed to highlight new risks and develop
technical solutions, so this novel attempt to measure their attitudes has
broad relevance. The findings should help to improve how researchers, private
sector executives, and policymakers think about regulations, governance
frameworks, guiding principles, and national and international governance
strategies for AI.

##  1 Introduction

Tech companies and governments alike see the potential for AI and have moved
to develop machine learning, particularly deep learning, applications across a
variety of sectors — from healthcare to national security (?, ?). Civil
society groups, governments, and academic researchers have expressed concerns
about AI related to safety (?, ?), discrimination and racial bias (?, ?), and
risks associated with uses of AI in a military and government context (?, ?,
?).

“Narrow” AI applications (such as self-driving cars, lethal autonomous weapons
systems, and surveillance systems) have become an immediate cause for concern
for AI/ML researchers, policymakers, and the public (?). Over the past two
years, corporations, governments, civil society groups, and multi-stakeholder
organizations have published dozens of high-level AI ethics principles (?).
Some early attempts at international governance include the OECD AI Principles
adopted in May 2019 and the G20 Human-centered AI Principles adopted in June
2019 (?, ?).

Technical researchers are crucial in the formation of AI governance. Being
close to the technology, AI/ML researchers are well placed to highlight new
risks, develop technical solutions, and choose to work for organizations that
align with their values. Just as epistemic communities have developed norms to
manage technologies that emerged in the 20th century, such as nuclear weapons
and chlorofluorocarbons (?, ?, ?), we expect AI/ML researchers to play a key
role in AI governance. For example, the Institute for Electrical and
Electronics Engineers (IEEE) established the Global Initiative on Ethics of
Autonomous and Intelligent Systems in 2016. Leading tech companies such as
IBM, Google, and Microsoft have published frameworks and principles intended
to guide how they deploy AI systems, and in several cases, have established
research positions and units focused on AI ethics (?). Individuals working
within the AI/ML community have also begun to take an active role in directly
shaping the societal and ethical implications of AI, by engaging with
employers and governments (?). For example, in summer 2018, over 3,000 Google
employees signed a petition protesting Google’s involvement with Project
Maven, a computer vision project run by the US Department of Defense (?).

Through a survey of leading AI/ML researchers, we explore technical experts’
attitudes about the governance of AI. We surveyed 524 AI/ML researchers in
September and October 2019 who had a paper accepted at one of two leading AI
research conferences: the Conference on Neural Information Processing Systems
(NeurIPS) and the International Conference on Machine Learning (ICML). Our
survey includes direct measures of trust, including attitudes about private
and public sector actors. We then compare those results to a 2018 survey of AI
attitudes among the US general public. This allows us to analyze attitudes on
the current state of global AI governance: who are the most trusted actors to
manage the technology, what AI governance challenges are perceived to be the
most important, and which norms have already begun to shape AI development and
deployment.

There is a small but growing literature that surveys the AI/ML community. Most
existing surveys focus on eliciting researcher forecasts on AI progress, such
as when specific milestones will be reached or when AI will surpass human
performance at nearly all tasks (?, ?, ?, ?, ?, ?). Others have focused on how
computer scientists define AI (?) or the impact of AI on society (?). AI/ML
professionals have also been surveyed in regard to their views on working on
military-related projects (?) and their immigration pathways and intentions
(?). A number of existing studies examine public opinion toward AI. Past
survey research related to AI tends to focus on specific governance
challenges, such as lethal autonomous weapons (?), algorithmic fairness (?),
or facial recognition technology (?, ?). A few large-scale surveys have taken
a more comprehensive approach by asking about a range of AI governance
challenges (?, ?, ?, ?, ?, ?, ?, ?, ?). While previous work has compared the
public’s and AI/ML researchers’ forecast of AI development timelines (?, ?),
little work compares the attitudes of AI/ML researchers and the public toward
AI governance.

Key results from our survey include:

  * •

Relative to the American public, AI/ML researchers place high levels of trust
in international organizations (e.g., the UN, EU, etc.) to shape the
development and use of AI in the public interest. While the American public
rated the US military as one of the most trustworthy actors, AI/ML researchers
place relatively low levels of trust in the militaries of countries where they
do research.

  * •

The majority of AI/ML researchers (68%) indicate that AI safety, broadly
defined, should be prioritized more than it is at present.

  * •

While most researchers support openly sharing all aspects of research, a
majority of AI/ML researchers (59%) also support “pre-publication review” for
“work that has some chance of adverse impact.” Furthermore, there is
considerable variation among the aspects of research that they feel must be
shared: 84% think that high-level description of the methods must be shared
every time while only 22% think that of the trained model.

  * •

The respondents are wary of AI/ML researchers working on certain military
applications of AI. Respondents are the most opposed to other researchers
working on lethal autonomous weapons (58% strongly oppose) but far fewer are
opposed to others working on logistics algorithms (6% strongly oppose) for the
military. 31% of researchers indicate that they would resign or threaten to
resign from their jobs, and 25% indicate that they would speak out publicly to
the media or online, if their organization decided to work on lethal
autonomous weapons.

##  2 Methods

To study attitudes about trust and governance in AI, we conducted a survey of
AI/ML researchers between September 16 and October 13, 2019. The researchers
were selected based on having papers accepted at two top AI research
conferences, following the sampling frame of (?). One group of respondents had
papers accepted to the 2018 NeurIPS conference and the other to the 2018 ICML
conference. Another group had papers accepted at NeurIPS and ICML in 2015 and
participated in a 2016 researcher survey on AI (?). Out of the 3,030
researchers who were contacted via email to complete our survey, 524
researchers (17%) completed at least some part of the survey. To incentivize
participation, we offered one in every ten respondents (via lottery) a gift
card of $250 USD. The survey took a median 17.4 minutes to complete.

This paper presents the results from the component of the survey focused on AI
governance. Other parts of the survey asked the respondents to forecast
developments in AI research and about their immigration preferences. The full
text of the survey questions reported in this paper can be found in the
Supplementary Materials. We also collected relevant demographic data about the
respondents (e.g., country of their undergraduate degree, workplace type,
citation count, etc.) using publicly available information. For some
questions, we compare responses from this survey with those from the US
public. This public opinion data come from a representative national survey of
2,000 US adults conducted in 2018, in which similar questions were asked
(?).111For the public opinion results, we weighted the responses to be
representative of the US adult population using weights provided to us by the
survey firm YouGov that conducted the survey on our behalf.

Our analysis is pre-registered using the Open Science Framework.222The project
URL is https://osf.io/fqz82/. Unless specified, we use multiple linear
regression to analyze the associations between variables. For estimates of
summary statistics or coefficients, “don’t know” or missing responses were re-
coded to the weighted overall mean, unconditional on treatment conditions.
Almost all questions had a “don’t know” option. If more than 10% of the
variable’s values were “don’t know” or missing, we included a (standardized)
dummy variable for “don’t know”/missing in the analysis. For survey experiment
questions, we compared “don’t know”/missing rates across experimental
conditions. Our decision was informed by the Standard Operating Procedures for
Don Green’s Lab at Columbia University (?).

Heteroscedasticity-consistent standard errors were used to generate the
margins of error at the 95% confidence level. We report cluster-robust
standard errors whenever there is clustering by respondent. In figures, each
error bar shows the 95% confidence intervals.

##  3 Results

###  3.1 Evaluation of AI governance challenges

To gauge their views on AI governance challenges, we asked our respondents:
“In the next 10 years, how important is it for tech companies and governments
to carefully manage the following issues?” Respondents were presented with a
list of 5 randomly-selected items out of a total list of 13, that they then
assigned a number value on a 4-point slider scale that allowed value input to
the tenth decimal place. The scale ranged from 0 “not at all important” to 3
“very important.”333All the multiple-choice questions include an “I don’t
know” answer option.

Figure 1: Perceived issue importance of AI governance challenges (comparing
AI/ML researchers’ and the US public’s responses). Each respondent was
presented with 5 AI governance challenges randomly selected from a list of 13.
Respondents were asked to evaluate the importance of each governance challenge
using a four-point scale (the slider scale allows respondents to input values
to the tenth decimal point): 0 = not important, 1 = not too important, 2 =
somewhat important, 3 = very important. We present the mean responses for each
governance challenge (by respondent type) along with the corresponding 95%
confidence intervals.

Figure 1 shows the mean importance of AI governance challenges, along with the
corresponding 95% confidence interval for both AI/ML researchers and the
general public (?). For the AI/ML researcher group, almost all issues were
rated as having a mean importance of 2.5, between “somewhat important” and
“very important,” with the top five issues including preventing criminal
justice bias, ensuring autonomous vehicles are safe, preventing critical AI
system failure, protecting data privacy, and preventing mass surveillance.
Hiring bias and technological unemployment are rated slightly (about 0.3
points) lower than other issues. The one outlier is “Reducing risks from US-
China competition over AI,” is rated significant below the other challenges at
1.8 (just below “somewhat important”); this result may be an artifact of our
question phrasing, in that AI/ML researchers may believe that risks from US-
China competition are real, but not one that is helped by “tech companies and
governments” trying to “carefully manage” them (see the Supplementary
Materials for the text of the survey questions). As Table S9 shows, AI/ML
researchers who identified as male, compared with those who identify as female
or other, tend to have lower issue importance scores across the board.

There is considerable overlap between the assessment of AI governance
challenges by AI/ML researchers and the US public (for details, see Table S3).
Both groups rate protecting data privacy, preventing mass surveillance, and
ensuring that autonomous vehicles are safe as among the five most important
governance challenges. AI/ML researchers placed significantly less importance
on value alignment444Defined as “AI systems are safe, trustworthy, and aligned
with human values”, technological unemployment, and hiring bias, and slightly
more importance on critical AI systems failure, criminal justice bias, and
autonomous vehicles, than the public.

The gap between AI/ML researchers and the US public is particularly large when
it comes to preventing the risks from US-China competition in AI. In contrast
to AI/ML researchers’ relatively low mean rating of 1.77 out of 3, the US
public gave US-China competition a mean rating of 2.52 out of 3. One might
think that breaking down the AI/ML researchers’ responses by demographic
subgroups (see Figure S2 - S4), would reveal some potential explanations for
the response pattern. However, the results are mixed. Respondents who attended
undergraduate in China rate this issue relatively high (mean score of 2.26);
in contrast, respondents who attended undergraduate in Europe give a mean
score of only 1.59. While respondents who attended undergraduate in the US
give a mean score of 1.90, the difference is not statistically significantly
different to respondents with undergraduate degrees from China.

###  3.2 Trust in actors to shape the development and use of AI in the public
interest

Good governance requires understanding of what institutions and organizations
AI/ML researchers (and other stakeholders) trust. To test AI/ML researcher
trust in different governance options, we ask: “Suppose the following
organizations were in a position to strongly shape the development and use of
advanced AI. How much trust do you have in each of these organizations to do
so in the best interests of the public?” Similar to the structure of the
previous question, respondents were shown 5 randomly-selected actors. For each
actor, they then assigned a number value on a 4-point scale ranging from 0 “no
trust at all” to 3 “a great deal of trust.”555The US military and the Chinese
military were shown only to respondents who reported the US or China as the
countries where they spend the most time doing research. These respondents had
equal probability of being shown the US military or the Chinese military.
Because very few responses came from respondents who do research in China, we
dropped their responses in this figure. We break down responses to these two
actors by the country where the respondents completed their undergraduate
degree (US and China) in Figure S6.

Figure 2 shows the mean trust value for the actors, along with the
corresponding 95% confidence interval for both AI/ML researchers and the
public. For AI/ML researchers, the most trusted actors, with a score above
2.0, were non-governmental scientific associations and intergovernmental
research organizations. The Partnership on AI, a consortium of tech companies,
academics, and civil society groups, is also rated relatively highly (mean
score of 1.89). Out of the international institutions listed, the European
Union (EU) is perceived to be the most trusted, with a mean score of 1.98,
while the United Nations’ (UN) mean trust rating is lower at 1.74 (two-sided
$p$=0.010).666For comparing trust between actors, we use $F$-tests to test the
equality of coefficients from the regression model presented in Table S16. It
is noteworthy that these more neutral, scientific organizations received the
highest trust ratings but currently play a relatively small role in AI
development and management.

Figure 2: Trust in actors to shape the development and use of AI in the public
interest: comparing AI/ML researchers’ and the US public’s responses. AI/ML
researchers were shown five randomly-selected actors and asked to evaluate how
much they trust the actors using a four-point scale: 0 = no trust at all, 1 =
not too much trust, 2 = a fair amount of trust, 3 = a great deal of trust. For
the AI/ML researchers survey, the “Tech companies” result is the mean response
across all corporate actors presented to respondents. *: The US military and
the Chinese military responses are only for those who do research in the US.
See Fig. S5 to S9 for further breakdown on this question by respondents’
background. In the public opinion survey, respondents were asked about their
confidence in the actors to develop AI (labeled “A”), or to manage the
development and use of AI (labeled “B”), in the best interest of the public,
using a similar four-point scale. For actors labeled “C,” both questions were
asked; we averaged the responses to these two questions for each of these
actors for clarity. For “US intelligence agencies,” we averaged across
responses to the NSA, the FBI, and the CIA (which were similar). The circle
and triangle shapes are the point estimates (mean responses) and the whiskers
are the corresponding 95% confidence intervals. The data for this figure,
alongside more detailed breakdowns of the results, can be found in Tables S10
- S15.

Out of all the private tech companies listed, OpenAI777OpenAI announced in
March 2019 that it would move from being a non-profit to being a “capped-
profit” company, a for-profit and non-profit hybrid (?). The survey of the
public was conducted before this change, whilst the survey of AI/ML
researchers occurred afterwards., DeepMind, Google, and Microsoft are
relatively more trusted. Facebook is ranked the least trustworthy of American
tech companies, and the Chinese companies rated significantly less trustworthy
than all listed US tech companies apart from Facebook. State actors, such as
the US and Chinese governments or the militaries of the countries where the
respondents do research, received relatively low trust scores from AI/ML
researchers. In general, respondents trust the government of the country where
they do research more than the military of that country (two-sided $p<$0.001).
As Figure S6 shows, respondents who attended undergraduate in the US, compared
with those who attended undergraduate in China, are significantly less
trustful of the Chinese government and military, as well as the three Chinese
tech companies presented to respondents (Tencent, Baidu, and Alibaba). The
interaction plot in Figure S10 shows that those who attended undergraduate in
China trust both Chinese tech companies and Western tech companies more than
those who attended undergraduate in the US. The difference in trust in Western
versus Chinese tech companies is smaller for those who attended undergraduate
in China than those who attended undergraduate in the US (two-sided
$p<$0.001), as can be seen in Figure S10.

AI/ML researchers, like the US public, as Figure 2 shows, distrust Facebook
more than any other US tech company. A major difference between AI/ML
researchers and the US public is their assessment of the military. Whereas
surveyed AI/ML researchers, on average, do not have too much trust in the
military of the countries where they do research, the US military is among the
most trusted of institutions for the US public. Respondents who do research in
the US gave the US military a mean rating of 0.73 (below “1 - not too much
trust”), whereas the US public gave their country’s military a mean rating of
1.56 (see Tables S10 - S11 and S15).

In contrast, the US public, compared with AI/ML researchers, places much less
trust in international institutions such as the UN. AI/ML researchers gave the
UN a mean rating of 1.74 while the US public gave a mean rating of 1.06.

Figure 3: Attitudes toward researchers working on military applications of AI.
Respondents were presented with two applications randomly selected from the
three and indicated how much they support or oppose other researchers working
on those applications using a five-point scale: -2 = strongly oppose, -1 =
somewhat oppose, 0 = neither support nor oppose, 1 = somewhat support, 2 =
strongly support. We present the overall means and the demographic subgroup
means with their corresponding 95% confidence intervals. For the subgroup
analysis, we broke down the responses by the respondents’ undergraduate
country, undergraduate region, and workplace type.

###  3.3 AI safety

The safety of AI systems may be a critical factor in their development and
adoption. We asked respondents about their familiarity with and prioritization
of AI safety. We described AI safety in a broad way, as focused on “making AI
systems more robust, more trustworthy, and better at behaving in accordance
with the operator’s intentions,” and also provided examples (see Supplementary
Materials). We first sought to understand how familiar researchers were with
AI safety research. We asked them to make a self-assessment using a five-point
scale, ranging from 0 “not familiar at all (first time hearing of the
concept)” to 4 “very familiar (worked on the topic).” To evaluate views about
the value of AI safety research, we asked respondents, “How much should AI
safety be prioritized relative to today?” Respondents selected answers on a
5-point Likert scale, ranging from -2 “much less” to 2 “much more” with 0
meaning “about the same.”

The AI/ML researchers we surveyed report, on average, moderate familiarity
with AI safety as a concept (see Figure S11). The distribution follows an
approximately normal distribution, although it is right-skewed.

3% of respondents say that they are “not familiar at all” with AI safety while
15% say that they are “very familiar.”

When asked about prioritizing AI safety, as Figure S13 shows, an overwhelming
majority of our respondents (68%) say that the field should be prioritized
more than at present. These results demonstrate significant growth in the
reported prioritization of AI safety in the research community, though these
differences may be driven by different definitions. In a similar survey of
AI/ML researchers conducted in 2016, 47% of respondents believed that AI
safety should be prioritized more than it was at the time (?).888We updated
the definition of AI safety research from (?) after consultation with AI/ML
researchers working in AI safety research. Contrasting with our definition
(see Supplementary Materials), the 2016 definition of AI safety was as “any
AI-related research that, rather than being primarily aimed at improving the
capabilities of AI systems, is instead primarily aimed at minimizing potential
risks of AI systems (beyond what is already accomplished for those goals by
increasing AI system capabilities).” The examples provided in 2016 included:
improving the human-interpretability of machine learning algorithms for the
purpose of improving the safety and robustness of AI systems, not focused on
improving AI capabilities; research on long-term existential risks from AI
systems; AI-specific formal verification research; and policy research about
how to maximize the public benefits of AI.

###  3.4 Publication norms

Figure 4: Respondents’ perceptions of how openly various aspects of research
should be shared. Respondents were presented with three aspects of research,
randomly selected from seven. They were asked how openly these aspects of
research should be shared using a six-point scale: 0 = it doesn’t matter, 1 =
it’s completely up to the researchers to share or not to share, 2 = it’s
preferred that researchers share but it’s not paramount that they do, 3 =
researchers are encouraged to share, 4 = researchers need a very strong reason
not to share, 5 = it must be shared every time. We show the results (mean
response and 95% confidence intervals) for all respondents.

The AI/ML research community has recently seen innovation and subsequent
controversy regarding publication norms, which also relate to questions of
trust. Such norms concern when, how, and where research is published. OpenAI’s
release strategy for GPT-2, a text generation system, is a prime example.
Citing concerns the system could be used for “malicious purposes”, they
employed a staged release strategy; the initial paper was accompanied by a
smaller version of GPT-2, the full model only being released 8 months later
(?). NeurIPS introduced further innovation, for the first time requiring
researchers submit impact statements along with their papers to the 2020
conference (?, ?). The conference also employed a form of pre-publication
review, rejecting four papers on ethical grounds after review from ethics
advisors.

We asked questions to generate insights into AI/ML researchers views on
publication norms. First, we assessed how much they agree or disagree that
“machine learning research institutions (including firms, governments, and
universities) should practice pre-publication review,” which involves “a
strong norm or policy” to have discussions which are “informed, substantive,
and serious” about “the ethical implications of publication” (see
Supplementary Materials). A majority of respondents agree (20% strongly agree;
39% somewhat agree) with the statement (see Table S28). Additionally, as shown
in Table S30, both familiarity with AI safety and prioritization of AI safety
significantly predict support for pre-publication review. These results speak
to an interest amongst AI/ML researchers to take steps to address the risks of
misuse of their work.

Next, we asked respondents about the importance of sharing various aspects of
AI/ML research. Respondents were shown three aspects of research, randomly
selected from a list of seven (e.g. high-level description of methods, code,
and training data). For each aspect of research, respondents could select from
six levels of sharing, ranging from “it doesn’t matter” to “it must be shared
every time.” As Figure 3 shows, the respondents think that high-level
descriptions of the methods, the results, and a detailed description of the
methods should almost always be openly shared. However, support declines for
requiring the sharing of other information that would be essential for
replication, such as the code, the training data, or the trained model.
Researchers felt that sharing these aspects of research should be encouraged,
not required. On the high end, 84% indicated that high-level description of
methods must be shared every time; on the low end, only 22% indicated that the
trained model must be shared every time (see Figure S18). We do not find
significant differences in responses between researchers who work in academia
versus in industry.

###  3.5 Attitudes toward military applications of AI

We also investigated researchers’ views toward military applications of AI.
Working on military uses of AI requires a great deal of trust in how they
might be used, because of the central role that some think AI could play in
the future of military power (?). We asked about three areas of military
applications of AI that have received public scrutiny: lethal autonomous
weapon systems, surveillance technologies for intelligence agencies, and
military logistics. Respondents were asked to evaluate two randomly-selected
military applications out of the three. They were asked whether they would
support or oppose researchers working on the application in the country where
the respondent currently works or studies. Respondents selected answers on a
Likert scale, ranging from -2 “strongly oppose” to 2 “strongly support.” Those
who answered that they “strongly oppose” or “somewhat oppose” researchers
working on the applications were asked what types of collective actions (e.g.,
signing a petition, or protesting) they would take if their organization
decided to conduct such research.

Our results show researchers have substantial concerns regarding working on
some military applications of AI. Nevertheless, there are nuances to their
views. Figure 4 illustrates that researchers, on average, more than somewhat
oppose work on lethal autonomous weapon systems (-1.3), very weakly oppose
work on surveillance applications (-0.3), and very weakly support work on
logistics applications (0.5). Additional detail in Figure S15 demonstrates
that 58% strongly oppose other researchers working on lethal autonomous
weapons, 20% strongly oppose others working on surveillance tools, but only 6%
strongly oppose others working on military logistics. This is consistent with
work by Aiken, Kagan, and Page (?), which focuses just on US-based AI
professionals and finds that US-based AI professionals are more opposed to
working on battlefield applications of AI than other applications.

How would these AI/ML researcher attitudes translate into potential action?
For each application (lethal autonomous weapon systems, surveillance, and
logistics), the respondents who said that they strongly or somewhat opposed
other researchers working on the application received a follow-up question
asking if they would take action if their organization decided to work on the
application. Figure S16 shows the distribution of responses for each
application. A majority of researchers who said they opposed others working on
each application said that they would actively avoid working on the project,
express their concern to a superior in their organization involved in the
decision, and sign a petition against the decision. 75% of researchers who
said they opposed others working on lethal autonomous weapons said they would
avoid working on lethal autonomous weapons themselves, and 42% of those
respondents said they would resign or threaten to resign from their jobs. In
absolute terms, 31% of researchers indicate that they would resign or threaten
to resign from their jobs, and 25% indicate that they would speak out publicly
to the media or online if their organization decided to work on lethal
autonomous weapons. Of those who say they oppose other researchers working on
lethal autonomous weapons, less than 1% said they would do nothing. The
percentages for surveillance and logistical software are 4% and 8%,
respectively (for further results see Figure S16).

A prominent conflict between the AI/ML community and a national military
involved Google engineers protesting their company’s participation in Project
Maven in the US. In June 2018, some 3,000 Google employees signed a petition,
voicing ethical concerns regarding the project (?). As a result, Google
decided not to renew its Project Maven contract with the US Department of
Defense.

Given the controversy over Google’s participation in Project Maven, we asked
respondents if they supported or opposed Google’s decision not to renew its
contract, using a five-point Likert scale with -2 meaning “strongly oppose” to
2 meaning “strongly support.” Figure S17 details broad support within our
AI/ML research respondents for Google’s decision to withdraw from Project
Maven. 38% strongly support and 21% support Google’s decision to pull out of
Project Maven while only 9% strongly or somewhat oppose the decision.

The results are broadly consistent across demographic subgroups, as seen in
Figure 4. Generally, across subgroups, respondents are the most opposed to
working on lethal autonomous weapons and least opposed to working on military
logistics.

##  4 Conclusion

It is important to recognize some of the limits to our findings, which future
research can address. First, our sample strategy focused on those who publish
in the top two AI/ML conferences; it thus may underweight the perspective of
those subgroups of the AI/ML community who are less likely to publish there,
such as perhaps product focused industry researchers. Second, this survey
captures the views of the researchers at a particular point in time, while the
norms around AI research and publishing continue to evolve, and significant
shifts in the psychological, political, and socioeconomic landscape continue
to occur, for example, as a result of COVID-19. Future work could expand the
sampling frame of respondents (e.g., to include more researchers who work in
industry, and develop a more representative sample of the AI/ML community) and
include panel studies that examine changes in respondents’ attitudes over
time.

Another limitation might include demographic biases or response bias.
Demographic characteristics of the respondents and non-respondents are found
in Table S1. A multiple regression that examines the association between
demographic characteristics and response finds that respondents have lower
h-indexes (a measure of productivity and citation impact of researchers) and
are more likely to work in academia compared with non-respondents (see Table
S2). Overall, however, we do not see evidence of concerning levels of response
bias. Compared with other work of its kind, our survey has more respondents, a
higher response rate, and more global coverage than other surveys of AI/ML
researchers we reviewed. Separately from response bias, there are other
aspects of the population of AI/ML researchers worth keeping in mind, such as
gender (91% of our respondents and 89% of non-respondents were male).

As institutions, regulations, and norms of AI governance are forming, this
survey of AI/ML researchers provides insight into how this emerging epistemic
community views the ethical and governance issues related to the technology.
The respondents place relatively high levels of trust in international
organizations to manage the development and use of AI in the public interest.
But compared with the US public who place high levels of trust in the US
military, AI/ML researchers are relatively distrustful of the military.
Furthermore, the AI/ML researchers we surveyed are very opposed to working on
certain military applications of AI, particularly lethal autonomous weapon
systems. The respondents are also aware of potential adverse impacts of their
research. Finally, a majority of respondents think that AI safety research
should be prioritized and researchers should conduct pre-publication reviews
to assess the potential harms their research could cause. This line of
research could help guide policymakers, tech companies, civil society, and the
AI/ML community in building and deploying safe and ethical AI systems.

## Acknowledgements

Funding: This research was supported by: the Ethics and Governance of AI Fund,
the Open Philanthropy Project grant for “Oxford University – Research on the
Global Politics of AI,” and the Minerva Research Initiative under Grant
#FA9550-18-1-0194. The research reported here should solely be attributed to
the authors; all errors are the responsibilities of the authors.

Authors contributions: A.D., B.Z., M.A., and M.H. (in alphabetical order)
designed the research and provided the conceptual framing of the work. B.Z.
and M.A. handled the data acquisition. B.Z. and N.D. analyzed the data. A.D.,
B.Z., L.K., M.A., M.H., and N.D. wrote the paper.

Competing interests: The authors declare no competing interests.

Data and materials availability: Due to the data privacy promised to
respondents and that our IRB applications noted that we would not share
individual-level results, we cannot release the data in full. We made this
decision because the population we are sampling from is a relatively small
group of individuals, which increases the likelihood of respondents being
identifiable from individual-level data. Instead, we have opted to report
detailed breakdowns of the data by key demographics in the Supplementary
Materials.

## References

  * Ada Lovelace Institute Ada Lovelace Institute (2019).  Beyond face value: public attitudes to facial recognition technology.  Tech. rep., Ada Lovelace Institute.
  * Adler Adler, E. (1992).  The emergence of cooperation: national epistemic communities and the international evolution of the idea of nuclear arms control.  International Organization, 46(1), 101–145.
  * Aiken, Dunham, & Zwetsloot Aiken, C., Dunham, J., & Zwetsloot, R. (2020a).  Immigration pathways and plans of AI talent.  Tech. rep., Center for Security and Emerging Technology.
  * Aiken, Kagan, & Page Aiken, C., Kagan, R., & Page, M. (2020b).  “Cool projects” or “expanding the efficiency of the murderous American war machine?”: AI professionals’ views on working with the Department of Defense.  Tech. rep., Center for Security and Emerging Technology.
  * Amodei, Olah, Steinhardt, Christiano, Schulman, & Mané Amodei, D., Olah, C., Steinhardt, J., Christiano, P., Schulman, J., & Mané, D. (2016).  Concrete problems in AI safety.  CoRR, abs/1606.06565.
  * Anderson, Rainie, & Luchsinger Anderson, J., Rainie, L., & Luchsinger, A. (2018).  Artificial intelligence and the future of humans.  Tech. rep., Pew Research Center.
  * Ashurst, Anderljung, Prunkl, Leike, Gal, Shevlane, & Dafoe Ashurst, C., Anderljung, M., Prunkl, C., Leike, J., Gal, Y., Shevlane, T., & Dafoe, A. (2020).  A guide to writing the NeurIPS impact statement..  Available at: https://medium.com/@GovAI/a-guide-to-writing-the-neurips-impact-statement-4293b723f832 [accessed: 12 Aug. 2020].
  * Balaram, Greenham, & Leonard Balaram, B., Greenham, T., & Leonard, J. (2018).  Artificial Intelligence: real public engagement.  Tech. rep., RSA.
  * Barocas, Hardt, & Narayanan Barocas, S., Hardt, M., & Narayanan, A. (2019).  Fairness and Machine Learning.  fairmlbook.org.  http://www.fairmlbook.org.
  * Baum, Goertzel, & Goertzel Baum, S. D., Goertzel, B., & Goertzel, T. G. (2011).  How long until human-level AI? Results from an expert assessment.  Technological Forecasting & Social Change, 78, 185–195.
  * Belfield Belfield, H. (2020).  Activism by the AI community: analysing recent achievements and future prospects.  In Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, AIES ’20, p. 15–21, New York, NY, USA. Association for Computing Machinery.
  * Brundage, Avin, Clark, Toner, Eckersley, Garfinkel, Dafoe, Scharre, Zeitzoff, Steinhardt, Flynn, Ó hÉigeartaigh, Beard, Belfield, Farquhar, Lyle, Crootof, Evans, Page, Bryson, Yampolskiy, & Amodei Brundage, M., Avin, S., Clark, J., Toner, H., Eckersley, P., Garfinkel, B., Dafoe, A., Scharre, P., Zeitzoff, T., Steinhardt, J., Flynn, C., Ó hÉigeartaigh, S., Beard, S., Belfield, H., Farquhar, S., Lyle, C., Crootof, R., Evans, O., Page, M., Bryson, J., Yampolskiy, R., & Amodei, D. (2018).  The malicious use of artificial intelligence: Forecasting, prevention, and mitigation.  Tech. rep., Future of Humanity Institute and University of Oxford and Centre for the Study of Existential Risk and University of Cambridge and Center for a New American Security and Electronic Frontier Foundation and OpenAI.
  * Butcher & Beridze Butcher, J., & Beridze, I. (2019).  What is the state of artificial intelligence governance globally?.  The Rusi Journal, 164(5-6), 88–96.
  * Cave, Coughlan, & Dihal Cave, S., Coughlan, K., & Dihal, K. (2019).  Scary robots: Examining public responses to AI.  In Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society, pp. 331–337. ACM.
  * Deahl Deahl, D. (2018).  Google employees demand the company pull out of Pentagon AI project..  Available at: https://www.theverge.com/2018/4/4/17199818/google-pentagon-project-maven-pull-out-letter-ceo-sundar-pichai [accessed:12 Nov. 2020.
  * Eurobarometer Eurobarometer (2017).  Special Eurobarometer 460: Attitudes towards the impact of digitisation and automation on daily life.  Tech. rep., Eurobarometer.
  * Eurobarometer Eurobarometer (2019).  Standard Eurobarometer 92: Autumn 2019 Europeans and artificial intelligence.  Tech. rep., Eurobarometer.
  * European Commission European Commission (2020).  Open public consultation on the European White Paper on Artificial Intelligence: Summary Report on the open public consultation on the White Paper on Artificial Intelligence.  Tech. rep., European Commission.
  * Fjeld, Achten, Hilligoss, Nagy, & Srikumar Fjeld, J., Achten, N., Hilligoss, H., Nagy, A., & Srikumar, M. (2020).  Principled artificial intelligence: Mapping consensus in ethical and rights-based approaches to principles for AI.  Tech. rep., Berkman Klein Center for Internet & Society.
  * G20 G20 (2019).  G20 ministerial statement on trade and digital economy.  Tech. rep., G20.
  * Grace, Salvatier, Dafoe, Zhang, & Evans Grace, K., Salvatier, J., Dafoe, A., Zhang, B., & Evans, O. (2018).  When will AI exceed human performance? Evidence from AI experts.  Journal of Artificial Intelligence Research, 62, 729–754.
  * Gruetzemacher, Paradice, & Lee Gruetzemacher, R., Paradice, D., & Lee, K. B. (2020).  Forecasting extreme labor displacement: A survey of AI practitioners.  Technological Forecasting and Social Change, 161.  120323\.
  * Haas Haas, P. M. (1992a).  Banning chlorofluorocarbons: epistemic community efforts to protect stratospheric ozone.  International Organization, 46(1), 187–224.
  * Haas Haas, P. (1992b).  Introduction: epistemic communities and international policy coordination..  International Organization, 46(1), 1–35.
  * Hoff & Bashir Hoff, K., & Bashir, M. (2015).  Trust in automation: Integrating empirical evidence on factors that influence trust.  Human Factors The Journal of the Human Factors and Ergonomics Society, 57, 407–434.
  * Horowitz Horowitz, M. C. (2016).  Public opinion and the politics of the killer robots debate.  Research & Politics, 3(1).
  * Horowitz Horowitz, M. C. (2018).  Artificial intelligence, international competition, and the balance of power.  Texas National Security Review, 1(3).
  * Horowitz Horowitz, M. C. (2019).  When speed kills: Lethal autonomous weapon systems, deterrence and stability.  Journal of Strategic Studies, 42(6), 764–788.
  * Kanaan Kanaan, M. (2020).  T-Minus AI: Humanity’s Countdown to Artificial Intelligence and the New Pursuit of Global Power.  BenBella Books, Dallas, TX.
  * Krafft, Young, Katell, Huang, & Bugingo Krafft, P. M., Young, M., Katell, M., Huang, K., & Bugingo, G. (2020).  Defining AI in policy versus practice.  In Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, AIES ’20, p. 72–78, New York, NY, USA. Association for Computing Machinery.
  * Lin, Balcan, Hadsell, & Ranzato Lin, H.-T., Balcan, M.-F., Hadsell, R., & Ranzato, M. (2020).  Getting started with NeurIPS 2020..  Available at: https://medium.com/@NeurIPSConf/getting-started-with-neurips-2020-e350f9b39c28 [accessed: 12 Aug. 2020].
  * Lin & Green Lin, W., & Green, D. P. (2016).  Standard operating procedures: A safety net for pre-analysis plans.  PS: Political Science & Politics, 49(3), 495–500.
  * Müller & Bostrom Müller, V. C., & Bostrom, N. (2016).  Future progress in artificial intelligence: A survey of expert opinion.  In Müller, V. (Ed.), Fundamental Issues of Artificial Intelligence, pp. 553–571. Springer International Publishing, Cham.
  * Noble Noble, S. U. (2018).  Algorithms of Oppression.  NYU Press, New York.
  * OpenAI LP OpenAI LP (2019).  OpenAI LP..  Available at: https://openai.com/blog/openai-lp/ [accessed: 11 Mar. 2020].
  * Russell Russell, S. (2019).  Human Compatible: Artificial Intelligence and the Problem of Control.  Penguin, New York.
  * Sandberg & Bostrom Sandberg, A., & Bostrom, N. (2011).  Machine intelligence survey.  Tech. rep., Future of Humanity Institute, Oxford University.  Technical Report #2011-1.
  * Saxena, Huang, DeFilippis, Radanovic, Parkes, & Liu Saxena, N. A., Huang, K., DeFilippis, E., Radanovic, G., Parkes, D. C., & Liu, Y. (2019).  How do fairness definitions fare?: Examining public attitudes towards algorithmic definitions of fairness.  In Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society, AIES ’19, pp. 99–106, New York, NY, USA. ACM, Association for Computing Machinery.
  * Scharre Scharre, P. (2018).  Army of None: Autonomous Weapons and the Future of War.  WW Norton & Company, New York.
  * Smith Smith, A. (2018).  Public attitudes toward computer algorithms.  Tech. rep., Pew Research Center.
  * Smith Smith, A. (2019).  More than half of U.S. adults trust law enforcement to use facial recognition responsibly.  Tech. rep., Pew Research Center.
  * Smith & Anderson Smith, A., & Anderson, M. (2016).  Automation in everyday life.  Tech. rep., Pew Research Center.
  * Solaiman, Clark, & Brundage Solaiman, I., Clark, J., & Brundage, M. (2019).  GPT-2: 1.5B release..  Available at: https://openai.com/blog/gpt-2-1-5b-release/ [accessed: 23 Jul. 2020].
  * The OECD Council on Artificial Intelligence The OECD Council on Artificial Intelligence (2019).  Recommendation of the council on artificial intelligence.  Tech. rep. JT03447952, Organisation for Economic Co-operation and Development.
  * Van Noorden Van Noorden, R. (2020).  The ethical questions that haunt facial-recognition research.  Nature, 587(7834), 354–358.
  * Wakabayashi & Shane Wakabayashi, D., & Shane, S. (2018).  Google will not renew Pentagon contract that upset employees..  Available at https://www.nytimes.com/2018/06/01/technology/google-pentagon-project-maven.html/ [accessed: 29 Apr. 2020].
  * Walsh Walsh, T. (2018).  Expert and non-expert opinion about technological unemployment.  International Journal of Automation and Computing, 15(5), 637–642.
  * West West, D. M. (2018).  Brookings survey finds worries over AI impact on jobs and personal privacy, concern U.S. will fall behind China.  Tech. rep., The Brookings Institute.
  * Zhang & Dafoe Zhang, B., & Dafoe, A. (2019).  Artificial intelligence: American attitudes and trends.  Tech. rep., Centre for the Governance of AI, University of Oxford.
  * Zhang & Dafoe Zhang, B., & Dafoe, A. (2020).  US public opinion on the governance of artificial intelligence.  In Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, AIES ’20, p. 187–193, New York, NY, USA. Association for Computing Machinery.
  * Zwetsloot & Dafoe Zwetsloot, R., & Dafoe, A. (2019).  Thinking about risks from AI: Accidents, misuse and structure..  Available at: https://www.lawfareblog.com/thinking-about-risks-ai-accidents-misuse-and-structure [accessed: 23 Sep. 2020].

## Supplementary Materials

Supplementary Text: Text of the Survey

Table S1 - S31

Fig S1 - S19

### Supplementary Text: Text of the Survey

Unless specified, the order of the items or scales presented here is the order
presented to respondents in the survey.

#### AI governance challenges

In the next 10 years, how important is it for tech companies and governments
to carefully manage the following issues?

[Respondents were shown 5 randomly-selected items.]

  * •

Ensure fairness and transparency in AI used in hiring

  * •

Ensure fairness and transparency in AI used in criminal justice

  * •

Make AI used for medical diagnosis accurate and transparent

  * •

Protect data privacy

  * •

Ensure that autonomous vehicles are safe

  * •

Prevent AI from being used to spread fake and harmful content online

  * •

Prevent AI cyber attacks against governments, companies, organizations, and
individuals

  * •

Prevent AI-assisted surveillance from violating privacy and civil liberties

  * •

Reducing risks from US-China competition over AI

  * •

Make sure AI systems are safe, trustworthy, and aligned with human values

  * •

Develop treaties to prevent the misuse of lethal autonomous weapons

  * •

Guarantee a good standard of living for those who lose their jobs to
automation

  * •

Prevent critical AI systems failures, such as a multi-day regional power
outage or a trillion dollar market crash from automated algorithms

Answer choices: Slider that you can choose in between whole numbers (to 1
decimal point), marked

  * •

3 = Very important

  * •

2 = Somewhat important

  * •

1 = Not too important

  * •

0 = Not at all important

  * •

I don’t know

#### Trust in actors

Suppose that the following organizations were in a position to strongly shape
the development and use of advanced AI. How much trust do you have in each of
these organizations to do so in the best interests of the public?

[Respondents were shown 5 randomly-selected actors.]

Included if the person does not work in the US:

  * •

The government of <COUNTRY WHERE THEY DO RESEARCH>999Earlier in the survey,
respondents were asked the following question: “In which country do you spend
the most time doing research?”. Respondents input the country from a drop-down
menu. Those who did not input a country were assigned “the country where you
do research” in questions that piped in the country where the respondent spent
most of their time doing research.

  * •

The military of <COUNTRY WHERE THEY DO RESEARCH>

Included if the person works in the US or China:

  * •

The US military

  * •

The Chinese military

To everyone else:

  * •

The US government

  * •

The Chinese government

  * •

The United Nations (UN)

  * •

The European Union (EU)

  * •

An intergovernmental AI research organization (similar to CERN)

  * •

Google

  * •

Facebook

  * •

Apple

  * •

Microsoft

  * •

Amazon

  * •

OpenAI

  * •

DeepMind

  * •

Tencent

  * •

Baidu

  * •

Alibaba

  * •

Non-governmental scientific organizations (e.g., AAAI)

  * •

Partnership on AI, a consortium of tech companies, academics, and civil
society groups

Answer choices:

  * •

A great deal of trust (3)

  * •

A fair amount of trust (2)

  * •

Not too much trust (1)

  * •

No trust at all (0)

  * •

I don’t know

#### AI safety

#### AI safety introduction

AI safety research focuses on making AI systems more robust, more trustworthy,
and better at behaving in accordance with the operator’s intentions.

Examples of such AI safety research include:

  * •

Making AI algorithms interpretable to humans

  * •

Making sure that an AI system is robust to distributional shifts or
adversarial inputs

  * •

Making sure that an AI system’s behavior aligns with the operator’s true
intentions

#### Familiarity with AI safety research

How familiar are you with AI safety research?

Use the slider to indicate your familiarity.

  * •

0 means not familiar at all (e.g., this is the first time you’re hearing about
the concept)

  * •

4 means very familiar (e.g., you have worked on the topic)

#### Prioritizing AI safety research

How much should AI safety research be prioritized – by, for instance, the tech
industry, the academic field, and governments – relative to today?

Answer choices:

  * •

Much less (-2)

  * •

Less (-1)

  * •

About the same (0)

  * •

More (1)

  * •

Much more (2)

  * •

I don’t know

#### Attitudes toward military application of AI

#### Support for others and themselves researching military technology

[Respondents where shown 2 out of the 3 applications below; the order that the
two questions were shown appear were randomized.]

Do you support or oppose researchers in <COUNTRY WHERE THEY DO
RESEARCH>working on the development of lethal autonomous weapons to be used by
the military of <COUNTRY WHERE THEY DO RESEARCH>?

Lethal autonomous weapons are systems that, once activated by a human, are
capable of targeting and firing on their own.

Do you support or oppose researchers in <COUNTRY WHERE THEY DO
RESEARCH>working on the development of surveillance technologies to be used by
intelligence agencies of <COUNTRY WHERE THEY DO RESEARCH>?

Intelligence agencies could use AI to expand their capacity to analyze image,
video, sound, and text data.

Do you support or oppose researchers in <COUNTRY WHERE THEY DO
RESEARCH>working on the development of logistics algorithms to optimize
storage and transportation for the military of <COUNTRY WHERE THEY DO
RESEARCH>?

The military could use machine learning algorithms to improve their logistics,
such as the storage, purchasing and transportation of weapons and food.

Answer choices:

  * •

Strongly support (2)

  * •

Somewhat support (1)

  * •

Neither support nor oppose (0)

  * •

Somewhat oppose (-1)

  * •

Strongly oppose (-2)

  * •

I don’t know

[For each of the questions above, if they selected “somewhat oppose” or
“strongly oppose” above, the respondents were shown the respective question
below.]

Suppose your organization has decided to research lethal autonomous weapons to
be used by the military of <COUNTRY WHERE THEY DO RESEARCH>. Which, if any, of
the following actions would you take?

Suppose your organization has decided to research surveillance technologies to
be used by intelligence agencies of <COUNTRY WHERE THEY DO RESEARCH>. Which,
if any, of the following actions would you take?

Suppose your organization has decided to research logistics algorithms to
optimize storage and transportation for the military of <COUNTRY WHERE THEY DO
RESEARCH>. Which, if any, of the following actions would you take?

  * •

Nothing

  * •

Actively avoid working on the project

  * •

Expressing your concern to a superior in your organization involved in the
decision

  * •

Sign a petition against the decision

  * •

Participate in a public protest

  * •

Speak out against the decision anonymously to the media or online

  * •

Speak out against the decision publicly to the media or online

  * •

Resign or threaten to resign from your job

  * •

Other: [short textbox]

#### Project Maven

Google had a contract to work on Project Maven, a US Department of Defense
project that develops and integrates computer vision algorithms to support
military operations. Some Google employees voiced ethical concerns regarding
the project. Google decided not to renew its Project Maven contract with the
US Department of Defense.

Do you support or oppose this decision by Google not to renew its contract?

Answer choices:

  * •

Strongly support (2)

  * •

Somewhat support (1)

  * •

Neither support nor oppose (0)

  * •

Somewhat oppose (-1)

  * •

Strongly oppose (-2)

  * •

I don’t know

[Optional question]: Would you like to elaborate on the reasoning behind your
previous answer? [Text box]

#### Publication norms

#### Pre-publication review

Define “pre-publication review” as follows: For work that has some chance of
adverse impacts, having a strong norm or policy to have discussions about the
ethical implications of publication that are

  * •

Informed: the discussion includes the lead and senior authors

  * •

Substantive: the discussion lasts for at least an hour

  * •

Serious: the discussion can lead to real-world decisions (e.g., not to publish
parts of the research in question)

Taking into account the cost (e.g., in terms of researcher time) to what
extent do you agree or disagree with the following statement?

Machine learning research institutions (including firms, governments, and
universities) should practice pre-publication review.

  * •

Strongly agree (2)

  * •

Somewhat agree (1)

  * •

Somewhat disagree (-1)

  * •

Strongly disagree (-2)

  * •

I don’t know

#### Sharing various aspects of research

What is your view toward publicly sharing the following aspects of research,
such as at conferences, in academic journals, or online?

[Respondents were shown 3 aspects of research.]

  * •

High-level description of methods

  * •

Detailed description of methods

  * •

Results

  * •

Code

  * •

Training data

  * •

Trained model

  * •

Algorithm(s)

Answer choices:

  * •

It must be shared every time (5)

  * •

Researchers need a very strong reason not to share (4)

  * •

Researchers are encouraged to share (3)

  * •

It’s preferred that researchers share but it’s not paramount that they do (2)

  * •

It’s completely up to the researchers to share or not to share (1)

  * •

It doesn’t matter (0)

### Demographics of survey respondents

Table 1: Summary statistics of the non-respondents and respondents: binary differences. We collected demographic information for all our respondents and a random sample of 446 non-respondents using information publicly available online. The table presents the proportion of individuals in each demographic category for gender, region of undergraduate and PhD, region where the respondent works, and the type of workplace for both non-respondents and respondents. The mean undergraduate graduation year and log citations are also shown. For each the difference between the non-respondents’ and respondents’ proportions is presented alongside the corresponding standard error. The Holm method was used to control the family-wise error rate. Variable | Nonrespondent | Respondent |  Difference (SE) | Percent missing
---|---|---|---|---
Prop. male | 0.89 | 0.91 | 0.01 (0.02) | 0.01
Mean undergrad graduation year | 2007.62 | 2008.95 | 1.33 (0.47) | 0.21
Prop. undergrad region: North America | 0.25 | 0.27 | 0.02 (0.03) | 0.15
Prop. undergrad region: Europe | 0.26 | 0.29 | 0.02 (0.03) | 0.15
Prop. undergrad region: Asia | 0.43 | 0.39 | -0.04 (0.03) | 0.15
Prop. undergrad region: Other | 0.04 | 0.05 | 0.01 (0.01) | 0.15
Prop. PhD region: North America | 0.28 | 0.33 | 0.06 (0.03) | 0.08
Prop. PhD region: Europe | 0.59 | 0.53 | -0.07 (0.03) | 0.08
Prop. PhD region: Asia | 0.11 | 0.09 | -0.01 (0.02) | 0.08
Prop. PhD region: Other | 0.02 | 0.02 | 0.01 (0.01) | 0.08
Prop. currently enrolled in PhD | 0.20 | 0.33 | 0.12 (0.03)*** | 0.05
Mean log citations (all) | 6.75 | 6.26 | -0.49 (0.12)*** | 0.17
Mean h-index (all) | 19.68 | 14.42 | -5.26 (1.12)*** | 0.17
Prop. work region: Europe | 0.28 | 0.33 | 0.05 (0.03) | 0.01
Prop. work region: North America | 0.59 | 0.54 | -0.05 (0.03) | 0.01
Prop. work region: Asia | 0.12 | 0.12 | ¡0.01 (0.02) | 0.01
Prop. work region: Other | 0.01 | 0.02 | 0.01 (0.01) | 0.01
Prop. work in academia | 0.68 | 0.80 | 0.12 (0.03)*** | 0.00
Prop. work in industry | 0.36 | 0.35 | -0.01 (0.03) | 0.00

Table 2: Association between demographic characteristics and survey response: results from multiple regression model. We collected demographic information for all our respondents and a random sample of 446 non-respondents using information publicly available online. Here we use multiple linear regression to predict the response to the survey using the demographic variables that we collected. The (arbitrarily-chosen) reference categories, the ones that are excluded from the list of coefficients, are female/other for gender, North America for undergraduate, PhD, and work region, and industry for type of workplace. The F-test of overall significance rejects the null hypothesis that respondents do not differ in whether they responded to the survey depending on demographic characteristics. The Holm method was used to control the family-wise error rate. |  Coefficient (SE)
---|---
(Intercept) |  0.540∗∗∗
| (0.016)
Male | 0.022
| (0.016)
Undergrad graduation year | 0.002
| (0.021)
Undergrad region: Europe |  $-$0.030
| (0.022)
Undergrad region: Asia |  $-$0.037
| (0.020)
Undergrad region: Other |  $-$0.013
| (0.018)
PhD region: Europe | 0.039
| (0.026)
PhD region: Asia |  $-$0.019
| (0.023)
PhD region: Other | 0.009
| (0.021)
Currently enrolled in PhD | 0.033
| (0.019)
Log all citations | 0.022
| (0.028)
All h-index |  $-$0.083∗
| (0.026)
Work region: Europe | 0.017
| (0.026)
Work region: Asia | 0.034
| (0.024)
Work region: Other | 0.014
| (0.021)
Work in academia |  0.069∗∗∗
| (0.017)
Missing: undergrad year | 0.001
| (0.028)
Missing: undergrad region |  $-$0.044
| (0.028)
Missing: all citations | 0.009
| (0.016)
N | 970
* p $<$ 0.05, ** p $<$ 0.01, *** p $<$ 0.001
F(18, 951) = 4.196; p-value: $<0.001$

### Additional figures

#### Evaluation of AI governance challenges

Fig. S5: Perceived issue importance of AI governance challenges: all responses
from the AI/ML researcher survey. Each respondent was presented with five AI
governance challenges randomly selected from a list of 13. Respondents were
asked to evaluate the importance of each governance challenge using a four-
point scale (the slider scale allows respondents to input values to the tenth
decimal point): 0 = not important, 1 = not too important, 2 = somewhat
important, 3 = very important. We present the mean response for each
governance challenge along with the corresponding 95% confidence intervals.

Fig. S6: Perceived issue importance of AI governance challenges: by country of
undergraduate degree (China and the US). Each respondent was presented with
five AI governance challenges randomly selected from a list of 13. Respondents
were asked to evaluate the importance of each governance challenge using a
four-point scale (the slider scale allows respondents to input values to the
tenth decimal point): 0 = not important, 1 = not too important, 2 = somewhat
important, 3 = very important. We identified the country of respondents’
undergraduate degrees using publicly available information on the internet. We
present the mean response for each governance challenge (by country of
undergraduate degree) along with the corresponding 95% confidence intervals.

Fig. S7: Perceived issue importance of AI governance challenges: by region of
undergraduate degree (Asia, Europe, and North America). Each respondent was
presented with five AI governance challenges randomly selected from a list of
13. Respondents were asked to evaluate the importance of each governance
challenge using a four-point scale (the slider scale allows respondents to
input values to the tenth decimal point): 0 = not important, 1 = not too
important, 2 = somewhat important, 3 = very important. We present the mean
response for each governance challenge (by region of undergraduate degree)
along with the corresponding 95% confidence intervals.

Fig. S8: Perceived issue importance of AI governance challenges: by workplace
type (academic and industry). Each respondent was presented with five AI
governance challenges randomly selected from a list of 13. Respondents were
asked to evaluate the importance of each governance challenge using a four-
point scale (the slider scale allows respondents to input values to the tenth
decimal point): 0 = not important, 1 = not too important, 2 = somewhat
important, 3 = very important. We identified the respondents’ workplace types
using publicly available information on the internet. Note that a single
respondent can work both in academia and industry. We present the mean
response for each governance challenge (by workplace type) along with the
corresponding 95% confidence intervals.

#### Trust in actors to shape the development and use of AI in the public
interest

Fig. S9: Trust in actors to shape the development and use of AI in the public
interest: all responses from the AI/ML researcher survey. Respondents were
shown five randomly-selected actors and asked to evaluate how much they trust
the actors using a four-point scale: 0 = no trust at all, 1 = not too much
trust, 2 = a fair amount of trust, 3 = a great deal of trust. The US military
and the Chinese military were shown only to respondents who do research in the
US or China; these respondents had equal probability of being shown the US
military or the Chinese military. Of the 60 responses to the US military, 56
came from those who do research in the US. Of the 66 responses to the Chinese
military, 60 came from those who do research in the US. We present the mean
response for each actor along with the corresponding 95% confidence intervals.

Fig. S10: Trust in actors to shape the development and use of AI in the public
interest: by country of undergraduate degree (China and the US). Respondents
were shown five randomly-selected actors and asked to evaluate how much they
trust the actors using a four-point scale: 0 = no trust at all, 1 = not too
much trust, 2 = a fair amount of trust, 3 = a great deal of trust. The US
military and the Chinese military were shown only to respondents who do
research in the US or China; these respondents had equal probability of being
shown the US military or the Chinese military. We present the mean response
for each actor (by country of undergraduate degree) along with the
corresponding 95% confidence intervals. Fig. S11: Trust in actors to shape the
development and use of AI in the public interest: by region of undergraduate
degree (Asia, Europe, and North America). Respondents were shown five
randomly-selected actors and asked to evaluate how much they trust the actors
using a four-point scale: 0 = no trust at all, 1 = not too much trust, 2 = a
fair amount of trust, 3 = a great deal of trust. The US military and the
Chinese military were shown only to respondents who do research in the US or
China; these respondents had equal probability of being shown the US military
or the Chinese military. We present the mean response for each actor (by
region of undergraduate degree) along with the corresponding 95% confidence
intervals.

Fig. S12: Trust in actors to shape the development and use of AI in the public
interest: by workplace type (academic and industry). Respondents were shown
five randomly-selected actors and asked to evaluate how much they trust the
actors using a four-point scale: 0 = no trust at all, 1 = not too much trust,
2 = a fair amount of trust, 3 = a great deal of trust. The US military and the
Chinese military were shown only to respondents who do research in the US or
China; these respondents had equal probability of being shown the US military
or the Chinese military. We present the mean response for each actor (by their
workplace type) along with the corresponding 95% confidence intervals.

Fig. S13: Trust in actors to shape the development and use of AI in the public
interest: those who report spending most of their time doing research in the
US. Respondents were shown five randomly-selected actors and asked to evaluate
how much they trust the actors using a four-point scale: 0 = no trust at all,
1 = not too much trust, 2 = a fair amount of trust, 3 = a great deal of trust.
The US military and the Chinese military were shown only to respondents who do
research in the US or China; these respondents had equal probability of being
shown the US military or the Chinese military. The country where each
respondent spends the most time working or studying is self-reported in the
survey. We present the mean responses for each actor along with the
corresponding 95% confidence intervals.

Fig. S14: Interaction plot: how respondents who attended university in China
versus the US rate trust in Chinese versus Western tech companies. Respondents
were asked to evaluate how much they trust the companies using a four-point
scale: 0 = no trust at all, 1 = not too much trust, 2 = a fair amount of
trust, 3 = a great deal of trust. The figure is generated from a linear
regression with a two-way interaction between Chinese versus Western tech
companies and having an undergraduate degree from the US versus China. Only
respondents who who received undergraduate degrees from the US and China are
included in this analysis. See Table 18 for the regression output table.

#### AI safety

Fig. S15: Familiarity with AI safety: distribution of responses. After reading
a definition of AI safety (see survey text for the definition), respondents
input their familiarity with AI safety using a 5-point slider (0 = not
familiar at all; 4 = very familiar). We present the mean response at each
level of familiarity with AI safety and for missing responses, along with the
corresponding 95% confidence intervals.

Fig. S16: Familiarity with AI safety: mean response by demographic subgroups.
After reading a definition of AI safety (see Survey text for the definition),
respondents input their familiarity with AI safety using a 5-point slider (0 =
not familiar at all; 4 = very familiar). We present the mean AI safety
familiarity response by undergraduate country (US and China), undergraduate
region (North America, Europe, and Asia), and workplace type (industry and
academic), along with the corresponding 95% confidence intervals.

Fig. S17: How much should AI safety be prioritized: distribution of responses.
This question appears after the familiarity with AI safety question.
Respondents were asked how much AI safety research should be prioritized
relative to today. The answer choices are a Likert scale from -2 to 2: -2 =
much less; -1 = less; 0 = about the same; 1 = more; 2 = much more. There was
also an “I don’t know” option. We present the mean response for each option
along with the corresponding 95% confidence intervals.

Fig. S18: How much should AI safety be prioritized: mean response by
demographic subgroups. Respondents were asked how much AI safety research
should be prioritized relative to today. The answer choices are a Likert scale
from -2 to 2: -2 = much less; -1 = less; 0 = about the same; 1 = more; 2 =
much more. There was also an “I don’t know” option. We present the mean
response by undergraduate country (US and China), undergraduate region (North
America, Europe, and Asia), and workplace type (industry and academic), along
with the corresponding 95% confidence intervals.

#### Attitudes toward military applications of AI

Fig. S19: Attitudes toward researchers working on military applications of AI:
distribution of responses. Respondents were asked to indicate their level of
support for two of the three randomly presented military applications (lethal
autonomous weapons, surveillance, and logistics) on a five-point scale from -2
to 2: -2 = strongly oppose, -1 = somewhat oppose, 0 = neither support nor
oppose, 1 = somewhat support, 2 = strongly support. There was also an “I don’t
know” option. Each military application was defined when it was presented (see
survey text for the definition). We present the percentage of respondents who
chose each response as well as those who did not respond to the question,
along with the corresponding 95% confidence intervals.

Fig. S20: Support for collective action against research into military
applications of AI: distribution of responses. Respondents were asked which
actions, if any, they would take if their organization decided to research two
randomly chosen applications from the following three: lethal autonomous
weapons, surveillance, or logistics. The question text also highlighted that
this was specific to where the respondent worked our studied. In the top
panel, the x-axis is the percentage of respondents who were asked about the
application and indicated they “oppose” or “strongly oppose” researchers
working on the application. In the bottom panel, the x-axis is the percentage
of all respondents who were asked about the application. Recall that each
respondent was asked about two applications randomly selected from the three.

Fig. S21: Attitudes toward Google pulling out of Project Maven: distribution
of responses. Respondents were presented with a short description of the
employees’ reactions to Google’s Project Maven and the following non-renewal
of the contract (see survey text for the description) and were asked to
indicate their support for the non-renewal decision on a five-point scale from
-2 to 2: -2 = strongly oppose, -1 = somewhat oppose, 0 = neither support nor
oppose, 1 = somewhat support, 2 = strongly support. There was also an “I don’t
know” option. We present the percentage of respondents choosing each option
and who did not respond to the question, along with the corresponding 95%
confidence intervals.

#### Publication norms

Fig. S22: Sharing aspects of research: distribution of responses. Respondents
were presented with three aspects of research randomly chosen from a list of
six. For each aspect of research, they selected from six levels of openness (0
= it doesn’t matter; 1 = it’s completely up to the researchers to share or not
to share; 2 = it’s preferred that researchers share but it’s not paramount
that they do; 3 = researchers are encouraged to share; 4 = researchers need a
very strong reason not to share; 5 = it must be shared every time). We present
the mean response for each level of openness for the different aspects of
research, along with the corresponding 95% confidence intervals.

Fig. S23: Sharing aspects of research: mean level of openness response for
each aspect of research, by demographic subgroups. We present the mean
openness response for each aspect of research for all respondents as well as
by undergraduate country (US and China), undergraduate region (North America,
Europe, and Asia), and workplace type (industry and academic). The
corresponding 95% confidence intervals are shown.

### Additional tables

#### Evaluation of AI governance challenges

Table 3:  Perceived issue importance of AI governance challenges (comparing AI/ML researchers’ and the US public’s responses). The table presents the mean perceived importance of each of the AI governance challenges and the associated standard error and sample size for each type of respondent (AI/ML researcher and US public). Governance challenge | Mean | SE | N | Respondent type
---|---|---|---|---
Hiring bias | 2.18 | 0.06 | 170 | AI/ML researchers
Criminal justice bias | 2.59 | 0.05 | 167 | AI/ML researchers
Disease diagnosis | 2.45 | 0.05 | 147 | AI/ML researchers
Data privacy | 2.55 | 0.05 | 177 | AI/ML researchers
Autonomous vehicles | 2.58 | 0.05 | 165 | AI/ML researchers
Digital manipulation | 2.47 | 0.05 | 157 | AI/ML researchers
Cyber attacks | 2.40 | 0.05 | 176 | AI/ML researchers
Surveillance | 2.53 | 0.05 | 172 | AI/ML researchers
US-China competition | 1.77 | 0.07 | 159 | AI/ML researchers
Value alignment | 2.35 | 0.06 | 156 | AI/ML researchers
Autonomous weapons | 2.47 | 0.06 | 157 | AI/ML researchers
Technological unemployment | 2.27 | 0.06 | 168 | AI/ML researchers
Critical AI systems failure | 2.57 | 0.05 | 179 | AI/ML researchers
Hiring bias | 2.54 | 0.03 | 760 | US Public
Criminal justice bias | 2.53 | 0.03 | 778 | US Public
Disease diagnosis | 2.52 | 0.03 | 767 | US Public
Data privacy | 2.62 | 0.03 | 807 | US Public
Autonomous vehicles | 2.56 | 0.02 | 796 | US Public
Digital manipulation | 2.53 | 0.03 | 741 | US Public
Cyber attacks | 2.59 | 0.02 | 745 | US Public
Surveillance | 2.56 | 0.03 | 784 | US Public
US-China competition | 2.52 | 0.03 | 766 | US Public
Value alignment | 2.55 | 0.03 | 783 | US Public
Autonomous weapons | 2.58 | 0.02 | 757 | US Public
Technological unemployment | 2.50 | 0.03 | 738 | US Public
Critical AI systems failure | 2.47 | 0.03 | 778 | US Public

Table 4: Top five most important AI governance challenges (AI/ML researchers versus US public). The table presents the five highest mean responses of perceived importance of the AI governance challenges ranked in descending order for AI/ML researchers and the US public. Ranking | AI/ML researchers | US Public
---|---|---
1 | Criminal justice bias | Data privacy
2 | Autonomous vehicles | Cyber attacks
3 | Critical AI systems failure | Autonomous weapons
4 | Data privacy | Surveillance
5 | Surveillance | Autonomous vehicles

Table 5: Rating issue importance of AI governance challenges (by undergraduate country). The table presents AI/ML researchers’ mean perceived importance of each of the AI governance challenges and the associated standard error and sample size by country where the respondent completed their undergraduate degree (US and China). Governance challenge | Mean | SE | N | Undergraduate country
---|---|---|---|---
Hiring bias | 2.25 | 0.12 | 36 | US
Criminal justice bias | 2.69 | 0.08 | 47 | US
Disease diagnosis | 2.33 | 0.12 | 34 | US
Data privacy | 2.57 | 0.10 | 42 | US
Autonomous vehicles | 2.62 | 0.11 | 35 | US
Digital manipulation | 2.48 | 0.11 | 37 | US
Cyber attacks | 2.34 | 0.11 | 38 | US
Surveillance | 2.50 | 0.12 | 30 | US
US-China competition | 1.90 | 0.12 | 39 | US
Value alignment | 2.19 | 0.13 | 31 | US
Autonomous weapons | 2.41 | 0.12 | 38 | US
Technological unemployment | 2.12 | 0.14 | 35 | US
Critical AI systems failure | 2.57 | 0.09 | 43 | US
Hiring bias | 2.34 | 0.13 | 23 | China
Criminal justice bias | 2.39 | 0.12 | 24 | China
Disease diagnosis | 2.54 | 0.11 | 26 | China
Data privacy | 2.50 | 0.16 | 24 | China
Autonomous vehicles | 2.69 | 0.09 | 31 | China
Digital manipulation | 2.42 | 0.16 | 20 | China
Cyber attacks | 2.17 | 0.15 | 23 | China
Surveillance | 2.10 | 0.17 | 27 | China
US-China competition | 2.26 | 0.18 | 17 | China
Value alignment | 2.53 | 0.12 | 23 | China
Autonomous weapons | 2.57 | 0.12 | 25 | China
Technological unemployment | 2.16 | 0.13 | 25 | China
Critical AI systems failure | 2.58 | 0.16 | 22 | China

Table 6: Rating issue importance of AI governance challenges (by undergraduate region). The table presents AI/ML researchers’ mean perceived importance of each of the AI governance challenges and the associated standard error and sample size by region where the respondent completed their undergraduate degree (Europe, North America, and Asia). Governance challenge | Mean | SE | N | Undergraduate region
---|---|---|---|---
Hiring bias | 2.01 | 0.15 | 43 | Europe
Criminal justice bias | 2.61 | 0.10 | 43 | Europe
Disease diagnosis | 2.25 | 0.13 | 30 | Europe
Data privacy | 2.39 | 0.11 | 41 | Europe
Autonomous vehicles | 2.39 | 0.12 | 37 | Europe
Digital manipulation | 2.32 | 0.12 | 41 | Europe
Cyber attacks | 2.28 | 0.12 | 41 | Europe
Surveillance | 2.81 | 0.06 | 42 | Europe
US-China competition | 1.59 | 0.12 | 42 | Europe
Value alignment | 2.23 | 0.15 | 42 | Europe
Autonomous weapons | 2.62 | 0.10 | 32 | Europe
Technological unemployment | 2.26 | 0.11 | 35 | Europe
Critical AI systems failure | 2.50 | 0.09 | 41 | Europe
Hiring bias | 2.27 | 0.11 | 40 | North America
Criminal justice bias | 2.70 | 0.08 | 52 | North America
Disease diagnosis | 2.32 | 0.11 | 40 | North America
Data privacy | 2.57 | 0.09 | 47 | North America
Autonomous vehicles | 2.61 | 0.10 | 38 | North America
Digital manipulation | 2.48 | 0.10 | 40 | North America
Cyber attacks | 2.35 | 0.11 | 45 | North America
Surveillance | 2.52 | 0.12 | 31 | North America
US-China competition | 1.86 | 0.12 | 44 | North America
Value alignment | 2.18 | 0.12 | 36 | North America
Autonomous weapons | 2.45 | 0.12 | 41 | North America
Technological unemployment | 2.13 | 0.14 | 40 | North America
Critical AI systems failure | 2.55 | 0.09 | 46 | North America
Hiring bias | 2.28 | 0.09 | 55 | Asia
Criminal justice bias | 2.51 | 0.08 | 44 | Asia
Disease diagnosis | 2.66 | 0.07 | 49 | Asia
Data privacy | 2.52 | 0.09 | 53 | Asia
Autonomous vehicles | 2.67 | 0.07 | 67 | Asia
Digital manipulation | 2.54 | 0.09 | 56 | Asia
Cyber attacks | 2.43 | 0.09 | 60 | Asia
Surveillance | 2.39 | 0.09 | 63 | Asia
US-China competition | 1.84 | 0.13 | 47 | Asia
Value alignment | 2.51 | 0.08 | 55 | Asia
Autonomous weapons | 2.40 | 0.10 | 57 | Asia
Technological unemployment | 2.32 | 0.09 | 68 | Asia
Critical AI systems failure | 2.65 | 0.08 | 56 | Asia

Table 7: Rating issue importance of AI governance challenges (by workplace type). The table presents AI/ML researchers’ mean perceived importance of each of the AI governance challenges and the associated standard error and sample size by workplace type (academic and industry). Governance challenge | Mean | SE | N | Workplace type
---|---|---|---|---
Hiring bias | 2.17 | 0.07 | 131 | Academic
Criminal justice bias | 2.59 | 0.05 | 124 | Academic
Disease diagnosis | 2.44 | 0.06 | 113 | Academic
Data privacy | 2.50 | 0.06 | 125 | Academic
Autonomous vehicles | 2.60 | 0.05 | 131 | Academic
Digital manipulation | 2.45 | 0.07 | 113 | Academic
Cyber attacks | 2.32 | 0.06 | 132 | Academic
Surveillance | 2.47 | 0.06 | 127 | Academic
US-China competition | 1.82 | 0.07 | 123 | Academic
Value alignment | 2.36 | 0.07 | 111 | Academic
Autonomous weapons | 2.47 | 0.07 | 117 | Academic
Technological unemployment | 2.27 | 0.07 | 128 | Academic
Critical AI systems failure | 2.57 | 0.05 | 135 | Academic
Hiring bias | 2.20 | 0.12 | 42 | Industry
Criminal justice bias | 2.53 | 0.10 | 43 | Industry
Disease diagnosis | 2.50 | 0.09 | 42 | Industry
Data privacy | 2.71 | 0.05 | 53 | Industry
Autonomous vehicles | 2.45 | 0.14 | 35 | Industry
Digital manipulation | 2.48 | 0.09 | 46 | Industry
Cyber attacks | 2.50 | 0.08 | 48 | Industry
Surveillance | 2.67 | 0.08 | 52 | Industry
US-China competition | 1.64 | 0.13 | 40 | Industry
Value alignment | 2.38 | 0.10 | 51 | Industry
Autonomous weapons | 2.49 | 0.10 | 42 | Industry
Technological unemployment | 2.24 | 0.11 | 45 | Industry
Critical AI systems failure | 2.53 | 0.08 | 51 | Industry

Table 8: Association between perceived issue importance and different AI governance challenges. Output from the multiple linear regression used to compare differences in perceived issue importance between AI governance challenges. We regress perceived issue importance on all of the AI governance challenges. The (arbitrarily-chosen) reference category, the one that is excluded from the list of coefficients, is critical AI system failure. We clustered the standard errors by survey respondent because each respondent was presented with five AI governance challenges randomly chosen from the list of 13. The F-test of overall significance rejects the null hypothesis that respondents perceive all the governance challenges to have equal issue importance. The Holm method was used to control the family-wise error rate.  |  Coefficient (SE)
---|---
(Intercept) |  2.579∗∗∗
| (0.051)
Autonomous weapons |  $-$0.104
| (0.077)
Criminal justice bias | 0.012
| (0.067)
Critical AI systems failure |  $-$0.010
| (0.065)
Cyber attacks |  $-$0.175
| (0.071)
Data privacy |  $-$0.033
| (0.066)
Digital manipulation |  $-$0.113
| (0.072)
Disease diagnosis |  $-$0.124
| (0.070)
Hiring bias |  $-$0.400∗∗∗
| (0.077)
Surveillance |  $-$0.049
| (0.071)
Technological unemployment |  $-$0.308∗∗∗
| (0.076)
U.S.-China competition |  $-$0.804∗∗∗
| (0.082)
Value alignment |  $-$0.224∗
| (0.075)
N | 2,150 responses; 430 unique respondents
F-Statistic |  13.93∗∗∗ (df = 12; 429)
∗p $<$ .05; ∗∗p $<$ .01; ∗∗∗p $<$ .001
Table 9: Association between perceived issue importance and demographic variables controlling for issues. The table presents the output from the multiple linear regression used to compare differences in perceived issue importance between demographic subgroups whilst controlling for the different types of AI governance issues. We regressed perceived issue importance on the categorical variables of gender (female/other, male, and prefer not to say or missing response), location of undergraduate education (Europe, US, China, other, and missing response), location of work/study (Europe, US, China, and other), and type of workplace (industry and academic). The (arbitrarily-chosen) reference categories, the ones that are excluded from the list of coefficients, are female/other for gender, and China for location of undergraduate education and place of work/study. The F-test of overall significance rejects the null hypothesis that respondents do not differ in their perceived importance of AI governance challenges (when controlling for issue) depending on demographic subgroups. We cluster the standard errors by respondents because each respondent was presented with five AI governance challenges. The Holm method was used to control the family-wise error rate. |  Coefficient (SE)
---|---
(Intercept) |  3.058∗∗∗
| (0.135)
Gender: male |  $-$0.283∗∗∗
| (0.055)
Gender: prefer not to say/NA |  $-$0.311
| (0.172)
Place of undergraduate degree: Europe |  $-$0.005
| (0.091)
Place of undergraduate degree: missing | 0.105
| (0.089)
Place of undergraduate degree: other | 0.155
| (0.079)
Place of undergraduate degree: US | 0.032
| (0.079)
Place of work: Europe |  $-$0.230
| (0.106)
Place of work: other |  $-$0.269
| (0.114)
Place of work: US |  $-$0.201
| (0.094)
Job: industry |  $-$0.021
| (0.071)
Job: academic |  $-$0.074
| (0.072)
N | 2,150 responses, 430 unique respondents
F-Statistic |  8.935∗∗∗ (df = 23; 429)
∗p $<$ .05; ∗∗p $<$ .01; ∗∗∗p $<$ .001

#### Trust in actors to shape the development and use of AI in the public
interest

Table 10: Trust in actors to shape the development and use of AI in the public interest: comparing AI/ML researchers’ and the US public’s responses. This table contains the data used to generate Fig. 2. AI/ML researchers were shown five randomly-selected actors and asked to evaluate how much they trust the actors using a four-point scale: 0 = no trust at all, 1 = not too much trust, 2 = a fair amount of trust, 3 = a great deal of trust. For the AI/ML researchers survey, the “Tech companies” result is the mean response across all corporate actors presented to respondents. The AI/ML researchers’ responses to the US military and the Chinese military are denoted with $*$ because those two actors were shown only to those who do research in the US or China. These respondents had an equal probability of being shown the US military or the Chinese military. In the public opinion survey, respondents were asked about their confidence in the actors to develop AI (question type A) or manage the development and use of AI (question type B) in the best interest of the public using a similar four-point scale. For question type C, both questions were asked; we averaged the responses to these two questions for each of these actors for clarity. For “US intelligence agencies,” we averaged across responses to the NSA, the FBI, and the CIA. Table LABEL:tab:trust-vs-public contains the detailed breakdowns by actor and respondent type. |  |  |  |  |  |
---|---|---|---|---|---|---
Actor |  Question type | Mean | SE | N |  Subgroup | Actor type
Government of the country where they do research |  | 1.33 | 0.08 | 100 |  AI/ML researchers | National
Military of the country where they do research |  | 0.83 | 0.08 | 106 |  AI/ML researchers | National
US government |  | 0.94 | 0.07 | 113 |  AI/ML researchers | National
Chinese government |  | 0.38 | 0.06 | 115 |  AI/ML researchers | National
US military* |  | 0.73 | 0.10 | 56 |  AI/ML researchers (does research in the US only) | National
Chinese military* |  | 0.30 | 0.08 | 60 |  AI/ML researchers (does research in the US only) | National
UN |  | 1.74 | 0.08 | 122 |  AI/ML researchers | International
EU |  | 1.98 | 0.06 | 114 |  AI/ML researchers | International
Intergovernmental research organizations (e.g., CERN) |  | 2.08 | 0.06 | 114 |  AI/ML researchers | International
Tech companies |  | 0.98 | 0.03 | 1171 |  AI/ML researchers | Corporate
Google |  | 1.35 | 0.07 | 131 |  AI/ML researchers | Corporate
Facebook |  | 0.58 | 0.06 | 104 |  AI/ML researchers | Corporate
Apple |  | 1.05 | 0.07 | 117 |  AI/ML researchers | Corporate
Microsoft |  | 1.43 | 0.07 | 118 |  AI/ML researchers | Corporate
Amazon |  | 0.88 | 0.06 | 108 |  AI/ML researchers | Corporate
OpenAI |  | 1.50 | 0.08 | 113 |  AI/ML researchers | Corporate
DeepMind |  | 1.37 | 0.09 | 99 |  AI/ML researchers | Corporate
Tencent |  | 0.69 | 0.06 | 116 |  AI/ML researchers | Corporate
Baidu |  | 0.50 | 0.05 | 134 |  AI/ML researchers | Corporate
Alibaba |  | 0.57 | 0.05 | 131 |  AI/ML researchers | Corporate
Non-government scientific organization (e.g., AAAI) |  | 2.12 | 0.06 | 104 |  AI/ML researchers | Other
Partnership on AI |  | 1.89 | 0.06 | 103 |  AI/ML researchers | Other
US government |  B | 1.05 | 0.04 | 743 |  US public | National
US military* |  A | 1.56 | 0.04 | 638 |  US public | National
US intelligence agencies |  A | 1.16 | 0.03 | 2096 |  US public | National
UN |  B | 1.06 | 0.03 | 802 |  US public | International
Intergovernmental research organizations (e.g., CERN) |  C | 1.30 | 0.03 | 1392 |  US public | International
NATO |  A | 1.17 | 0.03 | 695 |  US public | International
International organizations |  B | 1.10 | 0.03 | 827 |  US public | International
Tech companies |  C | 1.33 | 0.03 | 1432 |  US public | Corporate
Google |  C | 1.20 | 0.03 | 1412 |  US public | Corporate
Facebook |  C | 0.78 | 0.03 | 1373 |  US public | Corporate
Apple |  C | 1.19 | 0.03 | 1367 |  US public | Corporate
Microsoft |  C | 1.26 | 0.03 | 1368 |  US public | Corporate
Amazon |  C | 1.22 | 0.03 | 1469 |  US public | Corporate
Non-government scientific organization (e.g., AAAI) |  B | 1.35 | 0.03 | 792 |  US public | Other
Partnership on AI |  B | 1.35 | 0.03 | 780 |  US public | Other
University researchers |  A | 1.56 | 0.03 | 666 |  US public | Other

Table 11:  Trust in actors to shape the development and use of AI in the public interest: by respondent type. The table presents the mean trust in different actors and the associated standard error and sample size by type of actor (national government, international, corporate, and other) and type of respondent (AI/ML researchers and US public). |  |  |  |  |
---|---|---|---|---|---
Actor | Mean | SE | N | Actor type | Respondent type
US government | 0.94 | 0.07 | 113 | National | AI/ML researchers
Chinese government | 0.38 | 0.06 | 115 | National | AI/ML researchers
Government of the country where they do research | 1.33 | 0.08 | 100 | National | AI/ML researchers
Military of the country where they do research | 0.83 | 0.08 | 106 | National | AI/ML researchers
US military | 0.68 | 0.09 | 60 | National | AI/ML researchers
Chinese military | 0.38 | 0.09 | 66 | National | AI/ML researchers
UN | 1.74 | 0.08 | 122 | International | AI/ML researchers
EU | 1.98 | 0.06 | 114 | International | AI/ML researchers
Intergovernmental research organizations (e.g., CERN) | 2.08 | 0.06 | 114 | International | AI/ML researchers
Google | 1.35 | 0.07 | 131 | Corporate | AI/ML researchers
Facebook | 0.58 | 0.06 | 104 | Corporate | AI/ML researchers
Apple | 1.05 | 0.07 | 117 | Corporate | AI/ML researchers
Microsoft | 1.43 | 0.07 | 118 | Corporate | AI/ML researchers
Amazon | 0.88 | 0.06 | 108 | Corporate | AI/ML researchers
OpenAI | 1.50 | 0.08 | 113 | Corporate | AI/ML researchers
DeepMind | 1.37 | 0.09 | 99 | Corporate | AI/ML researchers
Tencent | 0.69 | 0.06 | 116 | Corporate | AI/ML researchers
Baidu | 0.50 | 0.05 | 134 | Corporate | AI/ML researchers
Alibaba | 0.57 | 0.05 | 131 | Corporate | AI/ML researchers
Non-government scientific organization (e.g., AAAI) | 2.12 | 0.06 | 104 | Other | AI/ML researchers
Partnership on AI | 1.89 | 0.06 | 103 | Other | AI/ML researchers
Amazon | 1.33 | 0.04 | 685 | Corporate | Public
Apple | 1.29 | 0.04 | 697 | Corporate | Public
CIA | 1.21 | 0.04 | 730 | National | Public
Facebook | 0.85 | 0.04 | 632 | Corporate | Public
FBI | 1.21 | 0.04 | 656 | National | Public
Google | 1.34 | 0.04 | 645 | Corporate | Public
Intergovernmental research organizations (e.g., CERN) | 1.42 | 0.04 | 645 | International | Public
Microsoft | 1.40 | 0.04 | 597 | Corporate | Public
NATO | 1.17 | 0.03 | 695 | International | Public
Non-profit (e.g., OpenAI) | 1.44 | 0.03 | 659 | Other | Public
NSA | 1.28 | 0.04 | 710 | National | Public
Tech companies | 1.44 | 0.03 | 674 | Corporate | Public
US civilian government | 1.16 | 0.03 | 671 | National | Public
US military | 1.56 | 0.04 | 638 | National | Public
University researchers | 1.56 | 0.03 | 666 | Other | Public
Amazon | 1.24 | 0.03 | 784 | Corporate | Public
Apple | 1.20 | 0.03 | 775 | Corporate | Public
Facebook | 0.91 | 0.03 | 741 | Corporate | Public
Google | 1.20 | 0.03 | 767 | Corporate | Public
Intergovernmental research organizations (e.g., CERN) | 1.27 | 0.03 | 747 | International | Public
International organizations | 1.10 | 0.03 | 827 | International | Public
Microsoft | 1.24 | 0.03 | 771 | Corporate | Public
Non-government scientific organization (e.g., AAAI) | 1.35 | 0.03 | 792 | Other | Public
Partnership on AI | 1.35 | 0.03 | 780 | Other | Public
Tech companies | 1.33 | 0.03 | 758 | Corporate | Public
US federal government | 1.05 | 0.04 | 743 | National | Public
US state governments | 1.05 | 0.03 | 713 | National | Public
UN | 1.06 | 0.03 | 802 | International | Public

Table 12: Trust in actors to shape the development and use of AI in the public interest (by undergraduate country). The table presents the mean trust in different actors and the associated standard error and sample size by country of undergraduate education (US and China) and type of actor (national government, international, corporate, and other). |  |  |  |  |
---|---|---|---|---|---
Actor | Mean | SE | N |  Undergraduate country | Actor type
US government | 1.08 | 0.14 | 23 |  US | National
Chinese government | 0.14 | 0.06 | 27 |  US | National
Government of the country where they do research | 1.14 | 0.16 | 21 |  US | National
Military of the country where they do research | 0.85 | 0.17 | 21 |  US | National
US military | 0.92 | 0.20 | 17 |  US | National
Chinese military | 0.27 | 0.14 | 24 |  US | National
UN | 1.73 | 0.21 | 22 |  US | International
EU | 1.64 | 0.16 | 25 |  US | International
Intergovernmental research organizations (e.g., CERN) | 2.13 | 0.13 | 25 |  US | International
Google | 1.41 | 0.12 | 32 |  US | Corporate
Facebook | 0.55 | 0.12 | 23 |  US | Corporate
Apple | 1.00 | 0.11 | 31 |  US | Corporate
Microsoft | 1.38 | 0.13 | 30 |  US | Corporate
Amazon | 0.90 | 0.15 | 22 |  US | Corporate
OpenAI | 1.44 | 0.14 | 24 |  US | Corporate
DeepMind | 1.31 | 0.17 | 27 |  US | Corporate
Tencent | 0.54 | 0.09 | 23 |  US | Corporate
Baidu | 0.44 | 0.09 | 32 |  US | Corporate
Alibaba | 0.41 | 0.08 | 32 |  US | Corporate
Non-government scientific organization (e.g., AAAI) | 1.92 | 0.15 | 23 |  US | Other
Partnership on AI | 2.13 | 0.12 | 22 |  US | Other
US government | 1.00 | 0.19 | 19 |  China | National
Chinese government | 1.30 | 0.16 | 14 |  China | National
Government of the country where they do research | 1.36 | 0.21 | 15 |  China | National
Military of the country where they do research | 0.97 | 0.19 | 19 |  China | National
US military | 0.56 | 0.19 | 15 |  China | National
Chinese military | 1.01 | 0.22 | 16 |  China | National
UN | 1.85 | 0.17 | 19 |  China | International
EU | 2.06 | 0.14 | 16 |  China | International
Intergovernmental research organizations (e.g., CERN) | 2.09 | 0.08 | 12 |  China | International
Google | 1.71 | 0.21 | 17 |  China | Corporate
Facebook | 1.10 | 0.16 | 16 |  China | Corporate
Apple | 1.67 | 0.19 | 15 |  China | Corporate
Microsoft | 1.90 | 0.17 | 16 |  China | Corporate
Amazon | 1.06 | 0.17 | 16 |  China | Corporate
OpenAI | 1.81 | 0.17 | 21 |  China | Corporate
DeepMind | 1.60 | 0.23 | 9 |  China | Corporate
Tencent | 1.59 | 0.17 | 17 |  China | Corporate
Baidu | 1.00 | 0.19 | 19 |  China | Corporate
Alibaba | 1.31 | 0.20 | 18 |  China | Corporate
Non-government scientific organization (e.g., AAAI) | 2.21 | 0.14 | 15 |  China | Other
Partnership on AI | 1.80 | 0.14 | 22 |  China | Other

Table 13: Trust in actors to shape the development and use of AI in the public interest (by undergraduate region). The table presents the mean trust in different actors and the associated standard error and sample size by region of undergraduate education (Europe, North America, and Asia) and type of actor (national government, international, corporate, and other). |  |  |  |  |
---|---|---|---|---|---
Actor | Mean | SE | N |  Undergraduate region | Actor type
US government | 0.87 | 0.11 | 33 |  Europe | National
Chinese government | 0.25 | 0.09 | 31 |  Europe | National
Government of the country where they do research | 1.64 | 0.16 | 22 |  Europe | National
Military of the country where they do research | 0.88 | 0.17 | 24 |  Europe | National
US military | 0.71 | 0.29 | 7 |  Europe | National
Chinese military | 0.23 | 0.17 | 6 |  Europe | National
UN | 1.81 | 0.14 | 33 |  Europe | International
EU | 1.96 | 0.11 | 28 |  Europe | International
Intergovernmental research organizations (e.g., CERN) | 2.22 | 0.11 | 30 |  Europe | International
Google | 1.12 | 0.16 | 29 |  Europe | Corporate
Facebook | 0.62 | 0.14 | 22 |  Europe | Corporate
Apple | 0.76 | 0.14 | 24 |  Europe | Corporate
Microsoft | 1.30 | 0.17 | 24 |  Europe | Corporate
Amazon | 0.94 | 0.11 | 27 |  Europe | Corporate
OpenAI | 1.36 | 0.16 | 32 |  Europe | Corporate
DeepMind | 1.47 | 0.16 | 25 |  Europe | Corporate
Tencent | 0.54 | 0.07 | 30 |  Europe | Corporate
Baidu | 0.33 | 0.09 | 29 |  Europe | Corporate
Alibaba | 0.41 | 0.08 | 37 |  Europe | Corporate
Non-government scientific organization (e.g., AAAI) | 2.23 | 0.13 | 22 |  Europe | Other
Partnership on AI | 1.80 | 0.14 | 16 |  Europe | Other
US government | 1.07 | 0.14 | 26 |  North America | National
Chinese government | 0.17 | 0.07 | 28 |  North America | National
Government of the country where they do research | 1.17 | 0.14 | 24 |  North America | National
Military of the country where they do research | 0.83 | 0.16 | 24 |  North America | National
US military | 0.93 | 0.18 | 19 |  North America | National
Chinese military | 0.24 | 0.12 | 27 |  North America | National
UN | 1.68 | 0.19 | 25 |  North America | International
EU | 1.68 | 0.15 | 28 |  North America | International
Intergovernmental research organizations (e.g., CERN) | 2.14 | 0.11 | 30 |  North America | International
Google | 1.43 | 0.12 | 33 |  North America | Corporate
Facebook | 0.60 | 0.12 | 26 |  North America | Corporate
Apple | 0.95 | 0.11 | 34 |  North America | Corporate
Microsoft | 1.36 | 0.12 | 35 |  North America | Corporate
Amazon | 0.92 | 0.12 | 27 |  North America | Corporate
OpenAI | 1.44 | 0.14 | 25 |  North America | Corporate
DeepMind | 1.33 | 0.16 | 28 |  North America | Corporate
Tencent | 0.52 | 0.08 | 26 |  North America | Corporate
Baidu | 0.40 | 0.08 | 35 |  North America | Corporate
Alibaba | 0.40 | 0.08 | 35 |  North America | Corporate
Non-government scientific organization (e.g., AAAI) | 1.93 | 0.14 | 26 |  North America | Other
Partnership on AI | 2.13 | 0.11 | 23 |  North America | Other
US government | 0.99 | 0.14 | 39 |  Asia | National
Chinese government | 0.61 | 0.12 | 37 |  Asia | National
Government of the country where they do research | 1.30 | 0.14 | 39 |  Asia | National
Military of the country where they do research | 0.84 | 0.12 | 41 |  Asia | National
US military | 0.59 | 0.14 | 26 |  Asia | National
Chinese military | 0.72 | 0.19 | 23 |  Asia | National
UN | 1.58 | 0.14 | 42 |  Asia | International
EU | 2.15 | 0.12 | 34 |  Asia | International
Intergovernmental research organizations (e.g., CERN) | 1.89 | 0.12 | 33 |  Asia | International
Google | 1.61 | 0.13 | 43 |  Asia | Corporate
Facebook | 0.70 | 0.11 | 37 |  Asia | Corporate
Apple | 1.34 | 0.13 | 39 |  Asia | Corporate
Microsoft | 1.64 | 0.13 | 38 |  Asia | Corporate
Amazon | 0.94 | 0.10 | 38 |  Asia | Corporate
OpenAI | 1.76 | 0.13 | 39 |  Asia | Corporate
DeepMind | 1.31 | 0.18 | 28 |  Asia | Corporate
Tencent | 1.04 | 0.13 | 40 |  Asia | Corporate
Baidu | 0.65 | 0.11 | 46 |  Asia | Corporate
Alibaba | 0.81 | 0.12 | 42 |  Asia | Corporate
Non-government scientific organization (e.g., AAAI) | 2.23 | 0.10 | 37 |  Asia | Other
Partnership on AI | 1.83 | 0.10 | 44 |  Asia | Other

Table 14: Trust in actors to shape the development and use of AI in the public interest (by workplace and actor). The table presents the mean trust in different actors and the associated standard error and sample size by type of workplace (academic and industry) and type of actor (national government, international, corporate, and other). |  |  |  |  |
---|---|---|---|---|---
Actor | Mean | SE | N |  Workplace type | Actor type
US government | 0.96 | 0.08 | 89 |  Academic | National
Chinese government | 0.34 | 0.07 | 84 |  Academic | National
Government of the country where they do research | 1.30 | 0.10 | 72 |  Academic | National
Military of the country where they do research | 0.80 | 0.09 | 74 |  Academic | National
US military | 0.54 | 0.10 | 39 |  Academic | National
Chinese military | 0.49 | 0.11 | 49 |  Academic | National
UN | 1.77 | 0.09 | 92 |  Academic | International
EU | 2.06 | 0.07 | 88 |  Academic | International
Intergovernmental research organizations (e.g., CERN) | 2.07 | 0.08 | 82 |  Academic | International
Google | 1.40 | 0.08 | 97 |  Academic | Corporate
Facebook | 0.56 | 0.07 | 80 |  Academic | Corporate
Apple | 1.18 | 0.08 | 82 |  Academic | Corporate
Microsoft | 1.41 | 0.09 | 89 |  Academic | Corporate
Amazon | 0.92 | 0.07 | 81 |  Academic | Corporate
OpenAI | 1.55 | 0.09 | 92 |  Academic | Corporate
DeepMind | 1.41 | 0.10 | 68 |  Academic | Corporate
Tencent | 0.73 | 0.07 | 94 |  Academic | Corporate
Baidu | 0.54 | 0.06 | 99 |  Academic | Corporate
Alibaba | 0.59 | 0.06 | 98 |  Academic | Corporate
Non-government scientific organization (e.g., AAAI) | 2.17 | 0.07 | 80 |  Academic | Other
Partnership on AI | 1.87 | 0.08 | 74 |  Academic | Other
US government | 0.80 | 0.12 | 26 |  Industry | National
Chinese government | 0.41 | 0.11 | 33 |  Industry | National
Government of the country where they do research | 1.12 | 0.14 | 28 |  Industry | National
Military of the country where they do research | 0.91 | 0.15 | 34 |  Industry | National
US military | 1.00 | 0.19 | 19 |  Industry | National
Chinese military | 0.30 | 0.17 | 19 |  Industry | National
UN | 1.66 | 0.13 | 37 |  Industry | International
EU | 1.88 | 0.14 | 33 |  Industry | International
Intergovernmental research organizations (e.g., CERN) | 2.03 | 0.10 | 39 |  Industry | International
Google | 1.25 | 0.13 | 37 |  Industry | Corporate
Facebook | 0.74 | 0.11 | 29 |  Industry | Corporate
Apple | 0.87 | 0.13 | 38 |  Industry | Corporate
Microsoft | 1.54 | 0.12 | 32 |  Industry | Corporate
Amazon | 0.63 | 0.09 | 27 |  Industry | Corporate
OpenAI | 1.21 | 0.18 | 19 |  Industry | Corporate
DeepMind | 1.39 | 0.16 | 30 |  Industry | Corporate
Tencent | 0.55 | 0.09 | 24 |  Industry | Corporate
Baidu | 0.42 | 0.09 | 36 |  Industry | Corporate
Alibaba | 0.62 | 0.10 | 36 |  Industry | Corporate
Non-government scientific organization (e.g., AAAI) | 1.77 | 0.12 | 25 |  Industry | Other
Partnership on AI | 1.93 | 0.09 | 33 |  Industry | Other

Table 15: Trust in actors to shape the development and use of AI in the public interest (respondents who spend most of their time doing research in the US). The table presents the mean trust in different actors and the associated standard error and sample size by type of actor (national government, international, corporate, and other) for respondents who work in the US. Actor | Mean | SE | N |  Country where they do research | Actor type
---|---|---|---|---|---
US government | 1.11 | 0.11 | 55 |  US | National
Chinese government | 0.45 | 0.11 | 45 |  US | National
US military | 0.73 | 0.10 | 56 |  US | National
Chinese military | 0.30 | 0.08 | 60 |  US | National
UN | 1.77 | 0.10 | 52 |  US | International
EU | 1.91 | 0.10 | 58 |  US | International
Intergovernmental research organizations (e.g., CERN) | 1.96 | 0.09 | 47 |  US | International
Google | 1.36 | 0.10 | 62 |  US | Corporate
Facebook | 0.56 | 0.09 | 50 |  US | Corporate
Apple | 1.18 | 0.13 | 44 |  US | Corporate
Microsoft | 1.56 | 0.11 | 63 |  US | Corporate
Amazon | 0.94 | 0.08 | 52 |  US | Corporate
OpenAI | 1.59 | 0.12 | 54 |  US | Corporate
DeepMind | 1.37 | 0.13 | 44 |  US | Corporate
Tencent | 0.85 | 0.11 | 49 |  US | Corporate
Baidu | 0.50 | 0.08 | 62 |  US | Corporate
Alibaba | 0.67 | 0.10 | 61 |  US | Corporate
Non-government scientific organization (e.g., AAAI) | 2.09 | 0.10 | 50 |  US | Other
Partnership on AI | 1.86 | 0.09 | 51 |  US | Other

Table 16: Association between trust and all of the different actors. The table presents the output from the multiple linear regression used to compare differences in rated trust between all actors. We regressed trust on all actors. The (arbitrarily-chosen) reference category, the one that is excluded from the list of coefficients, is Alibaba. The F-test of overall significance rejects the null hypothesis that trust does not differ between the actors. The Holm method was used to control the family-wise error rate. |  Coefficient (SE)
---|---
(Intercept) |  0.566∗∗∗
| (0.054)
Amazon |  0.316∗∗∗
| (0.078)
Apple |  0.484∗∗∗
| (0.078)
Baidu |  $-$0.061
| (0.065)
Chinese government |  $-$0.188
| (0.075)
Chinese military |  $-$0.182
| (0.102)
DeepMind |  0.805∗∗∗
| (0.102)
EU |  1.416∗∗∗
| (0.084)
Facebook | 0.017
| (0.076)
Google |  0.788∗∗∗
| (0.086)
Government of the country where they do research |  0.761∗∗∗
| (0.100)
Intergovernmental research organizations (e.g., CERN) |  1.515∗∗∗
| (0.084)
Microsoft |  0.868∗∗∗
| (0.087)
Military of the country where they do research |  0.266∗
| (0.092)
Non-government scientific organization (e.g., AAAI) |  1.551∗∗∗
| (0.078)
OpenAI |  0.934∗∗∗
| (0.088)
Partnership on AI |  1.329∗∗∗
| (0.082)
Tencent | 0.123
| (0.064)
U.S. government |  0.372∗∗∗
| (0.086)
U.S. military | 0.119
| (0.105)
UN |  1.171∗∗∗
| (0.092)
N | 2,288 responses, 434 unique respondents
F-Statistic |  70.07∗∗∗ (df = 20; 433)
∗p $<$ .05; ∗∗p $<$ .01; ∗∗∗p $<$ .001

Table 17: Association between trust in actors and demographic variables controlling for actors. The table presents the output from the multiple linear regression used to compare differences in trust in actors between demographic subgroups whilst controlling for the different individual actors. We regressed trust in actors on the categorical variables of gender (female/other, male, and prefer not to say or missing response), location of undergraduate education (Europe, the US, China, other, and missing response), location of work/study (Europe, the US, China, and other), and type of workplace (industry, academic, and other). The (arbitrarily-chosen) reference categories, the ones that are excluded from the list of coefficients, are female/other for gender, other for workplace type, and China for location of undergraduate education and place of work/study. The F-test of overall significance rejects the null hypothesis that respondents do not differ in their trust of actors (when controlling for individual actors) between demographic subgroups. The Holm method was used to control the family-wise error rate. |  Coefficient (SE)
---|---
(Intercept) |  0.765∗∗∗
| (0.155)
Gender: male | 0.122
| (0.081)
Gender: prefer not to say/NA | 0.033
| (0.113)
Place of undergraduate degree: Europe |  $-$0.321∗
| (0.095)
Place of undergraduate degree: missing |  $-$0.342∗
| (0.103)
Place of undergraduate degree: other |  $-$0.402∗∗∗
| (0.087)
Place of undergraduate degree: US |  $-$0.359∗∗∗
| (0.083)
Place of work: Europe |  $-$0.046
| (0.134)
Place of work: other | 0.054
| (0.137)
Place of work: US | 0.005
| (0.125)
Job: industry |  $-$0.047
| (0.076)
Job: academic | 0.023
| (0.079)
N | 2,288 responses, 434 unique respondents
F-Statistic |  55.41∗∗∗ (df = 31; 433)
∗p $<$ .05; ∗∗p $<$ .01; ∗∗∗p $<$ .001

Table 18: Interaction between country of undergraduate degree and trust of Western versus Chinese tech companies. For this regression analysis, we focus only on respondents who received their undergraduate degrees in the US or China and trust in tech companies. We regress trust on whether the tech company is Western or Chinese, the country of the respondent’s undergraduate degree, and the interaction between the two. Google, Facebook, Apple, Microsoft, Amazon, OpenAI, and DeepMind are coded as Western tech companies. Tencent, Baidu, and Alibaba are coded as Chinese tech companies. The arbitrary reference group for tech company type is Chinese tech companies; the arbitrary reference group for country of undergraduate degree is the US. We cluster standard errors by respondent because each respondent evaluated multiple tech companies. The Holm method was used to control the family-wise error rate. |  Coefficient (SE)
---|---
(Intercept) |  0.457∗∗∗
|  (0.061)
Western tech companies |  0.707∗∗∗
|  (0.075)
Place of undergraduate degree: China |  0.831∗∗∗
|  (0.148)
Western tech companies: Place of undergraduate degree: China |  $-$0.437∗∗
|  (0.154)
N |  440 responses; 159 unique respondents
F-Statistic |  47.00∗∗∗ (df = 3; 158)
∗p $<$ .05; ∗∗p $<$ .01; ∗∗∗p $<$ .001

#### AI safety

Table 19: Familiarity with AI safety. The table presents the raw frequency and proportion of respondents who indicated different levels of familiarity with the issue of AI safety. The standard errors of the proportions are also presented. For reference, after reading a definition of AI safety (see survey text for the definition), respondents input their familiarity with AI safety using a 5-point slider (0 = not familiar at all; 4 = very familiar). AI safety familiarity level | Proportion | SE | Frequency
---|---|---|---
Missing | $<0.001$ | 0.00 | 1
0 - Not familiar at all | 0.03 | 0.01 | 8
1 | 0.25 | 0.03 | 71
2 | 0.32 | 0.03 | 90
3 | 0.25 | 0.03 | 72
4 - Very familiar | 0.15 | 0.02 | 42
Table 20: Familiarity with AI safety (mean response by demographic subgroups). The table presents the AI/ML researchers’ mean familiarity with AI safety and the associated standard error and sample size, by demographic subgroup. For reference, after reading a definition of AI safety (see survey text for the definition), respondents input their familiarity with AI safety using a 5-point slider (0 = not familiar at all; 4 = very familiar). Demographic subgroup | Demographic subgroup type | Mean | SE | N
---|---|---|---|---
US | Undergraduate country | 2.48 | 0.15 | 56
China | Undergraduate country | 1.98 | 0.17 | 42
Europe | Undergraduate region | 2.12 | 0.12 | 69
North America | Undergraduate region | 2.45 | 0.14 | 64
Asia | Undergraduate region | 2.16 | 0.11 | 98
Academic | Workplace type | 2.22 | 0.07 | 217
Industry | Workplace type | 2.31 | 0.11 | 77
Table 21: How much should AI safety be prioritized? Respondents were asked how much AI safety research should be prioritized relative to today. The answer choices are a Likert scale from -2 to 2: -2 = much less; -1 = less; 0 = about the same; 1 = more; 2 = much more. There was also an “I don’t know” option. We present the proportion of respondents who chose each option or have a missing response, along with the associated standard error and raw frequency. AI safety prioritization | Proportion | SE | Frequency
---|---|---|---
-2: Much less | 0.01 | 0.01 | 4
-1: Less | 0.04 | 0.01 | 11
0: About the same | 0.24 | 0.03 | 68
1: More | 0.38 | 0.03 | 109
2: Much more | 0.30 | 0.03 | 84
Missing | 0.00 | 0.00 | 0
I don’t know | 0.03 | 0.01 | 8

Table 22: Correlation between respondents’ familiarity with AI safety and how much they think AI safety research should be prioritized. For both models, the outcome variable is how much the respondents think AI safety research should be prioritized. Model 1 looks at the bivariate relationship between these two variables. Model 2 includes demographic variables as controls, including gender (female/other, male, and prefer not to say or missing response), location of undergraduate education (Europe, US, Asia, other, and missing response), location of work (Europe, US, Asia, and other), and type of workplace (industry, academic, and other). The (arbitrarily-chosen) reference categories, the ones that are excluded from the list of coefficients, are female/other for gender, other for workplace type, and Asia for location of undergraduate education and place of work/study. The Holm method was used to control the family-wise error rate. |  Coefficient (SE)
---|---
| (1) | (2)
(Intercept) |  0.709∗∗∗ | 1.157
| (0.125) | (0.397)
Familiarity with AI safety | 0.100 | 0.086
| (0.049) | (0.047)
Gender: male |  |  $-$0.484∗
|  | (0.156)
Gender: prefer not to say/NA |  |  $-$0.479
|  | (0.395)
Place of undergraduate degree: Europe |  | 0.134
|  | (0.218)
Place of undergraduate degree: missing |  | 0.206
|  | (0.224)
Place of undergraduate degree: other |  | 0.421
|  | (0.205)
Place of undergraduate degree: US |  | 0.086
|  | (0.203)
Place of work: Europe |  | 0.004
|  | (0.340)
Place of work: other |  |  $-$0.229
|  | (0.374)
Place of work: US |  | 0.066
|  | (0.335)
Job: industry |  |  $-$0.162
|  | (0.148)
Job: academic |  |  $-$0.154
|  | (0.165)
N | 284 | 284
F-Statistic |  4.215 (df = 1; 282) |  1.928 (df = 12; 271)
∗p $<$ .05; ∗∗p $<$ .01; ∗∗∗p $<$ .001

#### Attitudes toward military applications of AI

Table 23: Attitudes toward researchers working on military applications of AI: distribution of responses. Respondents were asked to indicate their level of support for two of the three randomly presented military applications (lethal autonomous weapons, surveillance, and logistics) on a five-point scale from $-2$ to 2. Each military application was defined when it was presented (see survey text for the definition). The table presents the proportion of respondents that indicated each level of response or answered “I don’t know”, along with the associated standard error and raw frequency, by military application type. The proportion of missing responses is also presented. Military application type | Response | Proportion | SE | Frequency
---|---|---|---|---
Lethal autonomous weapons |  $-2$: Strongly oppose | 0.58 | 0.03 | 178
Lethal autonomous weapons |  $-1$: Somewhat oppose | 0.16 | 0.02 | 48
Lethal autonomous weapons | 0: Neither support nor oppose | 0.14 | 0.02 | 44
Lethal autonomous weapons | 1: Somewhat support | 0.07 | 0.01 | 20
Lethal autonomous weapons | 2: Strongly support | 0.01 | 0.00 | 2
Lethal autonomous weapons | Missing | 0.01 | 0.01 | 3
Lethal autonomous weapons | I don’t know | 0.04 | 0.01 | 11
Surveillance |  $-2$: Strongly oppose | 0.20 | 0.02 | 58
Surveillance |  $-1$: Somewhat oppose | 0.21 | 0.02 | 60
Surveillance | 0: Neither support nor oppose | 0.26 | 0.03 | 75
Surveillance | 1: Somewhat support | 0.22 | 0.02 | 63
Surveillance | 2: Strongly support | 0.07 | 0.01 | 19
Surveillance | Missing | 0.01 | 0.01 | 3
Surveillance | I don’t know | 0.04 | 0.01 | 11
Logistics |  $-2$: Strongly oppose | 0.06 | 0.01 | 17
Logistics |  $-1$: Somewhat oppose | 0.08 | 0.02 | 23
Logistics | 0: Neither support nor oppose | 0.36 | 0.03 | 102
Logistics | 1: Somewhat support | 0.24 | 0.03 | 68
Logistics | 2: Strongly support | 0.20 | 0.02 | 55
Logistics | Missing | 0.00 | 0.00 | 1
Logistics | I don’t know | 0.05 | 0.01 | 15
Table 24: Attitudes toward researchers working on military applications of AI (mean response by demographic subgroup). Respondents were asked to indicate their level of support for two of the three randomly presented military applications (lethal autonomous weapons, surveillance, and logistics) on a five-point scale from $-2$ to 2: $-2$ = strongly oppose, $-1$ = somewhat oppose, 0 = neither support nor oppose, 1 = somewhat support, 2 = strongly support. There was also an “I don’t know” option. Each military application was defined when it was presented (see survey text for the definition). The table presents the proportion of respondents that indicated each level of response or answered “I don’t know”, along with the associated standard error and raw frequency, by military application and demographic subgroup. The proportion of missing responses is also presented. Military application type | Mean | SE | N |  Demographic subgroup |  Demographic subgroup type
---|---|---|---|---|---
Lethal autonomous weapons | $-1.30$ | 0.06 | 306 |  All respondents |  All respondents
Surveillance | $-0.27$ | 0.07 | 289 |  All respondents |  All respondents
Logistics | 0.46 | 0.06 | 281 |  All respondents |  All respondents
Lethal autonomous weapons | $-1.24$ | 0.13 | 65 |  US |  Undergraduate country
Surveillance | $-0.34$ | 0.14 | 65 |  US |  Undergraduate country
Logistics | 0.77 | 0.13 | 68 |  US |  Undergraduate country
Lethal autonomous weapons | $-0.89$ | 0.19 | 41 |  China |  Undergraduate country
Surveillance | 0.21 | 0.20 | 43 |  China |  Undergraduate country
Logistics | 0.38 | 0.13 | 40 |  China |  Undergraduate country
Lethal autonomous weapons | $-1.46$ | 0.09 | 83 |  Europe |  Undergraduate region
Surveillance | $-0.52$ | 0.14 | 67 |  Europe |  Undergraduate region
Logistics | 0.22 | 0.15 | 62 |  Europe |  Undergraduate region
Lethal autonomous weapons | $-1.30$ | 0.12 | 71 |  North America |  Undergraduate region
Surveillance | $-0.41$ | 0.13 | 73 |  North America |  Undergraduate region
Logistics | 0.71 | 0.12 | 78 |  North America |  Undergraduate region
Lethal autonomous weapons | $-1.13$ | 0.11 | 101 |  Asia |  Undergraduate region
Surveillance | $-0.03$ | 0.13 | 101 |  Asia |  Undergraduate region
Logistics | 0.53 | 0.09 | 92 |  Asia |  Undergraduate region
Lethal autonomous weapons | $-1.31$ | 0.06 | 234 |  Academic |  Workplace type
Surveillance | $-0.19$ | 0.08 | 216 |  Academic |  Workplace type
Logistics | 0.48 | 0.07 | 204 |  Academic |  Workplace type
Lethal autonomous weapons | $-1.29$ | 0.11 | 82 |  Industry |  Workplace type
Surveillance | $-0.54$ | 0.14 | 77 |  Industry |  Workplace type
Logistics | 0.47 | 0.12 | 81 |  Industry |  Workplace type

Table 25: Support for collective action against research into military applications of AI: distribution of responses. The table presents the proportion and number of respondents who oppose others working on the different military applications of AI (lethal autonomous weapons, logistics, and surveillance) and who were asked about each application in the survey, broken down by type of collective action Military application type |  Collective action |  Proportion of respondents who oppose others working on the application |  Proportion of all respondents who were asked about the application |  Number of respondents who oppose others working on the application |  Number of respondents asked about the application
---|---|---|---|---|---
Lethal autonomous weapons |  Nothing |  0.01 |  — |  225 |  306
Lethal autonomous weapons |  Actively avoid working on the project |  0.75 |  0.55 |  225 |  306
Lethal autonomous weapons |  Expressing your concern to a superior in your organization involved in the decision |  0.69 |  0.51 |  225 |  306
Lethal autonomous weapons |  Sign a petition against the decision |  0.61 |  0.45 |  225 |  306
Lethal autonomous weapons |  Participate in a public protest |  0.40 |  0.29 |  225 |  306
Lethal autonomous weapons |  Speak out against the decision anonymously to the media or online |  0.34 |  0.25 |  225 |  306
Lethal autonomous weapons |  Speak out against the decision publicly to the media or online |  0.34 |  0.25 |  225 |  306
Lethal autonomous weapons |  Resign or threaten to resign from your job |  0.42 |  0.31 |  225 |  306
Logistics |  Nothing |  0.07 |  — |  40 |  281
Logistics |  Actively avoid working on the project |  0.72 |  0.10 |  40 |  281
Logistics |  Expressing your concern to a superior in your organization involved in the decision |  0.62 |  0.09 |  40 |  281
Logistics |  Sign a petition against the decision |  0.52 |  0.07 |  40 |  281
Logistics |  Participate in a public protest |  0.22 |  0.03 |  40 |  281
Logistics |  Speak out against the decision anonymously to the media or online |  0.25 |  0.04 |  40 |  281
Logistics |  Speak out against the decision publicly to the media or online |  0.22 |  0.03 |  40 |  281
Logistics |  Resign or threaten to resign from your job |  0.18 |  0.02 |  40 |  281
Surveillance |  Nothing |  0.03 |  — |  115 |  289
Surveillance |  Actively avoid working on the project |  0.74 |  0.29 |  115 |  289
Surveillance |  Expressing your concern to a superior in your organization involved in the decision |  0.72 |  0.29 |  115 |  289
Surveillance |  Sign a petition against the decision |  0.62 |  0.25 |  115 |  289
Surveillance |  Participate in a public protest |  0.34 |  0.13 |  115 |  289
Surveillance |  Speak out against the decision anonymously to the media or online |  0.33 |  0.13 |  115 |  289
Surveillance |  Speak out against the decision publicly to the media or online |  0.36 |  0.14 |  115 |  289
Surveillance |  Resign or threaten to resign from your job |  0.27 |  0.11 |  115 |  289

Table 26: Attitudes toward Google pulling out of Project Maven. Respondents were presented with a short description of the employees’ reactions to Google’s Project Maven and the following non-renewal of the contract (see survey text for the description) and were asked to indicate their support for the non-renewal decision on a five-point scale from $-2$ to 2. There was also an “I don’t know” option. The table presents the proportion of respondents choosing each option and who did not respond to the question, along with the associated standard error and raw frequency. Response | Proportion | SE | Frequency
---|---|---|---
$-2$: Strongly oppose | 0.03 | 0.01 | 15
$-1$: Somewhat oppose | 0.06 | 0.01 | 28
0: Neither support nor oppose | 0.23 | 0.02 | 102
1: Somewhat support | 0.21 | 0.02 | 96
2: Strongly support | 0.38 | 0.02 | 171
Missing | 0.05 | 0.01 | 23
I don’t know | 0.04 | 0.01 | 18

Table 27: Correlation between support for researchers working on lethal autonomous weapons attitude and support for Google pulling out of Project Maven. In both models, the outcome variable is support for Google pulling out of Project Maven. Model 1 looks at the bivariate relationship between these two variables. Model 2 includes demographic variables as controls, including gender (female/other, male, and prefer not to say or missing response), location of undergraduate education (Europe, US, Asia, other, and missing response), location of work (Europe, US, Asia, and other), and type of workplace (industry, academic, and other). The (arbitrarily-chosen) reference categories, the ones that are excluded from the list of coefficients, are female/other for gender, other for workplace type, and Asia for location of undergraduate education and place of work/study. The Holm method was used to control the family-wise error rate. |  Coefficient (SE)
---|---
| (1) | (2)
(Intercept) |  0.388∗∗ | 0.324
| (0.108) | (0.393)
Support for researchers working on lethal autonomous weapons |  $-$0.469∗∗∗ |  $-$0.441∗∗∗
| (0.067) | (0.070)
Gender: male |  |  $-$0.030
|  | (0.185)
Gender: prefer not to say/NA |  |  $-$0.428
|  | (0.472)
Place of undergraduate degree: Europe |  |  $-$0.129
|  | (0.242)
Place of undergraduate degree: missing |  |  $-$0.221
|  | (0.277)
Place of undergraduate degree: other |  | 0.246
|  | (0.241)
Place of undergraduate degree: US |  | 0.237
|  | (0.224)
Place of work: Europe |  | 0.736
|  | (0.451)
Place of work: missing |  | 0.393
|  | (0.647)
Place of work: other |  |  $-$0.092
|  | (0.480)
Place of work: US |  | 0.090
|  | (0.429)
Job: industry |  |  $-$0.209
|  | (0.182)
Job: academic |  |  $-$0.186
|  | (0.179)
N | 306 | 306
F-Statistic |  49.41∗∗∗ (df = 1; 304) |  10.36∗∗∗ (df = 13; 292)
∗p $<$ .05; ∗∗p $<$ .01; ∗∗∗p $<$ .001

#### Publication norms

Table 28: Responses to statement on pre-publication review. After seeing a definition of “pre-publication review” (see survey text for the definition), respondents were asked to indicate their level of agreement with the statement: “Machine learning research institutions (including firms, governments, and universities) should practice pre-publication review.” The respondents could choose responses from a four-point scale. There was also an “I don’t know” option. The table presents the proportion of respondents who indicated each option or had a missing response, along with the associated standard error and raw frequency. Response | Proportion | SE | Frequency
---|---|---|---
$-2$: Strongly disagree | 0.14 | 0.02 | 54
$-1$: Somewhat disagree | 0.19 | 0.02 | 72
1: Somewhat agree | 0.39 | 0.03 | 147
2: Strongly agree | 0.20 | 0.02 | 76
Missing | 0.00 | 0.00 | 0
I don’t know | 0.06 | 0.01 | 24

Table 29:  Sharing aspects of research (by demographic subgroup). Respondents were presented with three aspects of research randomly chosen from a list of six. For each aspect of research, they selected from six levels of openness (0 = it doesn’t matter; 1 = it’s completely up to the researchers to share or not to share; 2 = it’s preferred that researchers share but it’s not paramount that they do; 3 = researchers are encouraged to share; 3 = researchers need a very strong reason not to share; 4 = it must be shared every time). The table presents the mean response for each aspect of research by demographic subgroup, along with the associated standard error and sample size. Aspect of research | Mean | SE | N |  Demographic subgroup |  Demographic subgroup type
---|---|---|---|---|---
High-level description of methods | 4.81 | 0.04 | 172 |  All respondents |  All
Detailed description of methods | 4.52 | 0.06 | 161 |  All respondents |  All
Results | 4.63 | 0.06 | 174 |  All respondents |  All
Code | 3.74 | 0.08 | 143 |  All respondents |  All
Training data | 3.54 | 0.08 | 173 |  All respondents |  All
Trained model | 3.46 | 0.10 | 143 |  All respondents |  All
Algorithm(s) | 4.30 | 0.07 | 153 |  All respondents |  All
High-level description of methods | 4.80 | 0.07 | 42 |  US |  Undergraduate country
Detailed description of methods | 4.46 | 0.12 | 42 |  US |  Undergraduate country