AI and Ethics
https://doi.org/10.1007/s43681-023-00279-4

ORIGINAL RESEARCH

Personality and demographic correlates of support for regulating
artificial intelligence
Christoph Bartneck1· Kumar Yogeeswaran2· Chris G. Sibley3

Received: 15 November 2022 / Accepted: 20 March 2023
© The Author(s) 2023

Abstract
The arrival of artificial intelligence (AI) in our society has sparked many hopes and fears, with people having diverging viewson the need to strictly regulate AI. The current study investigates how demographic and personality traits are associatedwith a desire to strictly regulate AI using a representative sample of adults from New Zealand (N = 47,951 participants).
Data revealed that support for strict regulation of AI is positively related with agreeableness, neuroticism, and honesty–
humility. However, it is negatively related to openness to experiences. A wide range of demographic factors including
gender, age, ethnicity, religiosity, neighbourhood level economic deprivation, living rural, relationship status, and parental
status were additionally related to support for regulation of AI. However, all these effects were fairly small suggesting thatboth personality and socio-demographic factors contribute to support for regulating AI, but other factors beyond thesecharacteristics should also be considered for understanding people’s support for regulating AI.
Keywords Artificial intelligence · Personality · Demographic · Regulation · Attitude · National survey

1 Introduction
The widespread incorporation of artificial intelligence (AI)in many domains of everyday life has led to much debate
around the need for regulation of this new technology. AI
influences how we interact with conversational agents androbots, how our cars drive, who gets a home loan, and thecost of our insurance premiums [1]. Hagendorff [2] pointed
Christoph Bartneck, Kumar Yogeeswaran and Chris G. Sibley
contributed equally to this work.
* Christoph Bartneck
christoph.bartneck@canterbury.ac.nz
Kumar Yogeeswaran
kumar.yogeeswaran@canterbury.ac.nz

out that the ethical frameworks developed to protect oursociety from privacy breaches, discrimination, and safety
risks have no practical consequences. They are often only
marketing tools and are considered by AI developers often
only as “add-ons”. The regulative power of these frameworks
are also limited, and this is why there are unsurprisingly
concerns about the degree to which AI needs to be strictly
regulated.
What factors relate to people’s support or opposition forregulating AI? Are certain personality traits more related
to support for regulating AI over others? Are certain
demographic characteristics more related to a desire forregulating AI? The current research examines the extent
to which both personality and socio-demographic factors
predict support for the strict regulation of AI.

Chris G. Sibleyc.sibley@auckland.ac.nz

2 Literature review

1Computer Science and Software Engineering, Universityof Canterbury, Private Bag 4800, Christchurch 8140,
New Zealand2

Department of Psychology, University of Canterbury, Private
Bag 4800, Christchurch 8140, New Zealand

3

School of Psychology, University of Auckland, 23 Symonds
Street, Auckland 1010, New Zealand

Recent research has shown that personality traits play arole in how people react to a social robot [3–6]. There isalso considerable research that tries to enable robots andconversational agents to exhibit personality traits [7–9].
However, previous research has not sufficiently explored
how personality and demographic factors relate to broader

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AI and Ethics

support for policies that involve the regulation of suchtechnology, or AI [3].
The European Commission launched its first proposal for
an legislative framework for AI in 2021 and it is currently inthe consultation phase [10]. In April 2022, lawmakers in the
USA introduced the Algorithmic Accountability Act of 2022[11]. Academics voiced considerable reservations towards
this bill [12]. A comparison of the proposed Europeanand American AI frameworks is available and came to theconclusion that the American framework is too modest andis expected to have a limited impact [13].
The Organisation for Economic Co-operation andDevelopment (OECD) set standards for practicable andflexible AI standards already in 2019 [14]. They intend
to help member countries put them into practice, but they
are of course not legally binding. In addition, the United
Nations Educational, Scientific and Cultural Organization(UNESCO) voted in 2021 to adopt the Recommendation on
the Ethics of Artificial Intelligence [15].
In the context of the current research (New Zealand),Stats NZ published a report on the use of algorithms[16]. In 2020, an Algorithm Charter was drafted in whichgovernment agencies committed themselves to manage
their use of algorithms in a fair, ethical and transparent
way.1 Besides these initiatives, there are no legally binding
policies on AI in New Zealand yet.
National and international organisations are developing
and implementing AI frameworks. Many recommendations
are already available, but the first legally binding policies are
currently being discussed. While experts in the fields will beaware of these endeavours, they are not likely to have beenstudied by the general public in detail.
Recently, Matthews et al. [17] pointed out that existing
personality models need to be more closely aligned withinteractive AI technology. The attitudes that people holdtowards the need to regulate AI are likely to be influenced
by both personality traits and demographic factors. For
example, Sindermann et al. [18] conducted a study withGerman and Chinese participants and found a positive
correlation between neuroticism and general attitudes
towards AI. While this recent work is valuable, the authors
acknowledged that their study was limited to a conveniencesample and the generalisability to the general populationis, therefore, limited. Moreover, as their work focussed on
general attitudes towards AI rather than policy support for
regulating AI, the relationship between personality andpolicy support for regulating AI is not obvious. Specifically,
people can hold generally positive attitudes towards
technology without necessarily supporting policies that

promote its use, just as people can hold positive attitudes
towards a target while opposing policies relating to it. For
example, research on the principle-implementation gap
demonstrates that people can support a general principle andeven have favourable attitudes towards a specific issue while
simultaneously opposing policies that achieve the same
principle [19]. Therefore, the current research examines how
personality factors are associated with public support for the
regulation of AI.
In addition, we examine how various socio-demographic
factors within the general population relate to public support
for the regulation of AI. In order to fully understand how
demographic factors relate to support for regulating AI, it isnecessary to consider the influence of these factors within
a diverse and wider population. For example, to understandthe role of education in support for regulating AI, it isimportant to sample the entire range of the population withvarying levels of education and not a limited demographic
group such as university students. The same applies tounderstanding the role of socioeconomic background, age,
ethnicity, among other demographic factors. Therefore, itcannot be overstated that to understand reactions to AI,
including public support for regulating it, we must examine
how society at large responds to AI. This means examining
regulation of AI at a population level and not just using
convenience samples of undergraduate students or online
Mechanical Turk samples [20].
The current research advances the above goal byanalysing support for regulating AI within the frameworkof the longitudinal New Zealand Attitudes and Values
Study (NZAVS). The NZAVS2 is a 20-year longitudinalsurvey of a national probability sample of adult New
Zealanders that began in 2009. This questionnaire includes
several hundreds of questions on a variety of topics. Using
the NZAVS allowed us to not only build on a nationally
representative sample, but also to connect attitudes towards
the regulation of AI to a large number of socio-demographic
and personality factors.

1

2
https://​www.​psych.​auckl​and.​ac.​nz/​en/​about/​new-​zeala​nd-​attit​udes-​and-​values-​study.​html.

https://​www.​data.​govt.​nz/​toolk​it/​data-​ethics/​gover​nment-​algor​ithm-​trans​paren​cy-​and-​accou​ntabi​lity/​algor​ithm-​chart​er/.

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3 Methods
3.1 ParticipantsParticipants in this study are annually contacted to complete
the NZAVS survey and were recruited for the study using
the NZ electoral roll which maintains a registry of all adults
who are citizens or permanent residents living in New
Zealand. All adults who are citizens or permanent residents

AI and Ethics

are required by law to register to vote; therefore, using theelectoral roll allows us to recruit from all sections of society.
Details about the NZAVS sampling procedure are available
here [21].
For the current study, data from the 10th wave (Time10) of the NZAVS was utilised as it was the only wave that
included the key measure of interest for the present research.
The Time 10 survey ran from late 2018 to mid 2019 andincluded 47,951 participants (62.6% female, 37.1% male)between the ages of 18–99 years (M = 48.59; SD = 13.86).
Of these participants, 88.6% indicated having European
ancestry, 9.8% reported having Maori ancestry, 2.2% having
Pacific heritage, and another 5.3% reported having Asiandescent (note that participants can report more than one
ancestry, so total adds up to more than 100%).
The NZAVS was approved by The University of Auckland
Human Participants Ethics Committee (Ref Number:
014889). Details about the sampling procedure, measures,and operations of the NZAVS can be found here online.3

3.2 Measures
3.2.1 AI regulation
Using a 7-point measure from 1 = Strongly Oppose to 7
= Strongly Support, participants indicated their support or
opposition to the following policy item about AI regulation:“Strict regulation limiting the development and use of
Artificial Intelligence”
No further explanation of what is meant by AI was provided
to the participants. Hence, participants indicated theirsupport or opposition to the regulation of AI in whatever
way they understood AI similar to people’s voting on
many issues. AI is a general term that does encompass ahuge variety of software methods and techniques. Whileparticipants might not always use a technically accurate
definition of AI, it does capture the overall sentiment thatpeople have towards AI. The survey also did not specify any
specific legal AI regulations as discussed above.
3.2.2 Personality measures
Participants completed 24 items assessing personality acrosssix dimensions (4 items for each dimension) including
neuroticism, extraversion, agreeableness, conscientiousness,
openness to experience, and honesty–humility. These items
were taken from the mini-IPIP6 (International Personality
Item Pool) that provides a shortened set of items of the Big

3
https://​www.​psych.​auckl​and.​ac.​nz/​en/​about/​new-​zeala​nd-​attit​udes-​and-​values-​study/​nzavs-​tech-​docs.​html.

Five personality model, and an additional sixth factor of
personality, Honesty–Humility [22, 23]. This additional
factor of personality was added based on the establishmentof the HEXACO model [24] that revealed an additional facetof personality beyond the traditional Big Five factors in theform of Honesty–Humility in what is often referred to as the
Big Six model of personality [25]. The specific measure of
personality used in this study has been successfully validated
in previous work in New Zealand where the current researchis conducted [25, 26]. The full personality inventory usedin the NZAVS has been published [25]. More specificinformation about the inventory is available [27].
Using a 7-point scale (1 = very inaccurate and 7 = very
accurate), participants indicated the extent to which theyfelt the items that followed described themselves. These
items captured the 6 dimensions of personality including:Honesty–Humility (sample item: “I deserve more thingsin life”; reverse coded), Neuroticism (sample item: “I havefrequent mood swings”), Extraversion (sample item: “Italk to a lot of different people at parties”), Agreeableness(sample item: “I am not interested in other people’s
problems”; reverse coded), Conscientiousness (sample item:“I like order”), and Openness to experience (sample item:“I have a vivid imagination”). Details including all items
relating to the six facets of personality are available in [25].
3.2.3 Demographic variables
Various demographic details about participants wererecorded during the survey including participants’ age,gender, ethnicity, relationship status, employment status,
whether one was a parent or not, whether one lived in arural or urban setting, whether one was religious or not, andwhether one was born in New Zealand or not. In addition,
we also recorded the socioeconomic deprivation of one’s
neighbourhood using the NZdep4 index that provides anindex on the extent to which one’s local community isdeprived (1 = least deprived areas; 10 = most deprived
areas) [28]. We chose to rely on regional deprivation as anindex of socioeconomic standing rather than measures of
personal income because personal income can misinform
our conclusions as someone can have a partner or parents
with wealth and high income while reporting low personal
income of their own. Instead, the economic deprivation
measure used here provides an index of whether one lives ina relatively affluent or deprived area. This specific measurehas been widely used in previous work, including thoseusing the NZAVS.

4

https://​www.​otago.​ac.​nz/​welli​ngton/​depar​tments/​publi​cheal​th/​resea​
rch/​hirp/​otago​020194.​html.

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AI and Ethics
Table 1  Full model of
personality and demographic
factors predicting support for
strict regulation of AI

Predictor

B

Std. error

Beta

p value

Honesty–Humility
Neuroticism
Extraversion
AgreeablenessConscientiousness
Openness
GenderAge
Ethnicity (1 = European; 0 = non-European)
Religious (1 = Yes, 0 = No)
Parent (1 = Yes, 0 = No)
Relationship status (1=In a relationship, 0=single)
Location (0 = Rural, 1 = Urban)
Born in NZ (0 = No, 1 = Yes)NZ Dep
Education level
Job security

0.031
0.034
− 0.0020.097
0.024− 0.074− 0.3640.01− 0.1410.228
0.145− 0.084− 0.209
0.174
0.036
0.001
− 0.015

0.008
0.008
0.008
0.01
0.008
0.008
0.019
0.001
0.029
0.018
0.022
0.022
0.022
0.021
0.003
0.001
0.006

0.022
0.024
−0.002
0.058
0.015
−0.05
− 0.1080.078− 0.0260.067
0.041− 0.021− 0.05
0.044
0.059
0.006
− 0.014< 0.001< 0.001
0.772< 0.001
0.004< 0.001< 0.001< 0.001< 0.001< 0.001< 0.001< 0.001< 0.001<0.001< 0.001
0.287
0.008

In addition, we also assessed participant’s level of
education using the NZ Qualifications Framework thatranks education from Levels 1–10 (1 = lowest educational
qualifications and 10 = highest educational qualifications).5
And finally, we also included a measure of job security totest whether those who feel especially insecure in their job
were more likely to support strict regulation of AI. Using a
7-point scale where 1 = strongly disagree and 7 = strongly
agree, participants responded to the item: “How secure do
you feel in your current job?”

3.3 Procedure
Participants completed a series of measures in the annual
survey. The items used in the current paper were intermixed
within this larger survey that typically takes approximately
one hour to complete.
As we rely on data from a large national sample, theanalyses reported will use a stricter criteria for evaluating
statistical significance by only considering results wherep < 0.001 as statistically significant.

4 Results
Using multiple regression analyses, we examined therelationship between personality and demographic
variables with support for strict regulation of AI. Table 1

5https://​www.​nzqa.​govt.​nz/​assets/​Study​ing-​in-​NZ/​New-​Zeala​nd-​
Quali​ficat​ion-​Frame​work/​requi​remen​ts-​nzqf.​pdf.

13

reports the full details of the regression analysis including
all personality and demographic factors within the samemodel suggesting that the results presented reflect the uniquevariance accounted by each personality and demographic
factor described while statistically controlling for all otherfactors.
To unpack the results, however, first we focus on
the relationship between personality and support
for strict regulations of AI. Data revealed thata g r e e a b l e n e s s ( 𝛽 = 0.058, p < 0.001 ) , h o n e s t y /humility ( 𝛽 = 0.022;p < 0.001 ), and neuroticism( 𝛽 = 0.024, p < 0.001) are positively related to support
for strict regulation of AI. By contrast, openness toexperience is negatively related to support for strictr e g u l a t i o n o f A I ( 𝛽 = −0.050, p < 0.001 ) . B ot h
extraversion (𝛽 = −0.002, p = 0.77) and conscientiousness(𝛽 = 0.015, p = 0.004) were not related to support for strict
regulation of AI.
Among demog raphic factors, being female( 𝛽 = −0.108, p < 0.001 ), older ( 𝛽 = 0.078, p < 0.001 ),non-European( 𝛽 = −0.026, p < 0.001 ) ,r e l i g i o u s ( 𝛽 = 0.067, p < 0.001 ) , b e i n g s i n g l e( 𝛽 = −0.021, p < 0.001), a parent ( 𝛽 = 0.041, p < 0.001),
living rural ( 𝛽 = −0.050, p < 0.001), being born in NZ( 𝛽 = 0.044, p < 0.001), and living in a more economicallydeprived region ( 𝛽 = 0.059, p < 0.001) were all related tosupport for strict regulation of AI. However, how secure
one felt in their current job ( 𝛽 = −0.014, p = 0.008 ) andone’s level of education ( 𝛽 = 0.006, p = 0.29 ) were notsignificantly related to their support for the strict regulationof AI. As mentioned above, these results represent theunique relationship between each of these personality andAI and Ethics

demographic factors with support for AI regulation while
statistically controlling for any shared variance between
other factors with the same outcome presented in the model.

5 Discussion
The current research sought to better understand how
personality and demographic factors are associated withsupport for the strict regulation of AI using a large national
sample.
As personality was a central focus of this work, wewill start by unpacking the different findings relating topersonality and support for the strict regulation of AI.
First, the positive correlation between neuroticism andsupport for the regulation of AI can be understood by apotential fear towards AI. As neuroticism is defined byemotional instability and proneness to negative emotions
such as anxiety, depression, and irritation [29, 30], ourresults fit with what one would expect as a reaction tonovel technology. Our findings are also in line the resultsfrom [18], which reveal a positive correlation between
neuroticism and fear towards AI. It is important to pointout that [18] distinguished between acceptance of AI andfear towards AI. Both concepts were included in theirquestionnaire, enabling participants to express positive
and negative attitudes at the same time. It is conceivablethat people might have positive attitudes towards robots fordifferent reasons than having negative attitudes.
Second, the positive correlation between agreeablenessand honesty–humility with support for strict regulation of
AI could best be understood together. Honesty–humility
is defined by an avoidance of manipulating others for
personal gain, a disinterest in lavish wealth and luxuries,
and a lack of entitlement to elevated social status [25],while agreeableness is associated with a tendency to
be warm, cooperative, and caring for others [29, 30]. A
combination of these may indicate that these correlations
could reflect a desire to regulate AI to guarantee fairness
out of concern for others.
Third, our study found negative correlation between
openness to experience with the support for strict
regulation of AI. In the literature, openness is characterisedby a tendency to prefer novelty, creativity, and a broadrather than narrow range of interests [17, 30]. Therefore, itis perhaps unsurprising that openness is negatively related
to support for strict regulation of AI. This result is also
inline with the positive correlation of acceptance of AI
with openness to experience in the Chinese sample of [18].
AI is certainly a novelty and has the potential to open
new interaction methods between people and technology.
People who are feel no desire to expose themselves tounconventional new technologies are more likely to also
support the strict regulation of such new technology.
Among demographic factors, being female, nonEuropean, living rural, and living in an economicallydeprived region were all related to support for strict
regulation of AI. These factors may collectively suggestthat people from more disadvantaged backgrounds may beespecially concerned about a lack of regulation around AI,
especially as these effects emerge even while controllingfor other factors such as education and job security. In
addition, older people, those single, religious, and a parent
were also related to support for the strict regulation of AI.
It is important to point out that these correlations
between personality traits and attitudes towards AI are
significant despite the statistical model taking a large
number of demographics into account. This means thatthese relationships emerge over and above the influence ofvaried demographic differences within the population. With
that said, the magnitude of these effects are fairly small.
Specifically, all personality factors collectively explained
just 1.6% of the total variance in people’s support for
regulating AI, and therefore, these do not represent major
drivers of people’s support for regulating AI. However, ourentire model including all personality and demographic
factors collectively explain just over 5% of the total
variance in people’s support for the strict regulation of AI
suggesting that there are other important factors beyond
these personality and demographic factors that explain alarge amount of people’s support for regulating AI. Future
research should delve further into better understanding thedrivers of people’s support for regulating AI.

5.1 Implications
The results of our study help us to better understand several
characteristics of people who support the strict regulation of
AI. By addressing their concerns, we can potentially increase
support for the adoption of AI in domains where it has thegreatest potential to benefit humanity. Developers need to
be aware of the role that personality traits and demographic
factors play when users directly interact with AI systems.
We also need to discuss the need to regulate AI from ausers’ perspective. There are several reasons why people
might want to regulate AI. For example, people who feelthat they may be disadvantaged by AI systems are likely
to call for their regulation. For example, our analysis
shows that people living is economically deprived areas
are significantly more likely to support the strict regulationof AI. This may be because economically disadvantaged
individuals in society tend to work in jobs that are especiallyvulnerable to automation. This fear is not new and has beenwith us since the industrial revolution, and automation willcontinue to target repetitive manual labour. These changes

13AI and Ethics

in our economy are ongoing and its easy to understand why
people are afraid to lose their jobs. Still, the productivity
gains might be worthwhile, and government programmes,
including higher education, are necessary in the transition.
However, as the results of our study did not show asignificant relationship between perceived job security andsupport for the strict regulation of AI once we accounted forregional deprivation, education, age, and other demographic
factors that may relate to this, it may be that people living ineconomically deprived areas are especially concerned about
how such technology can impact their relatively precarious
lives regardless of their current job security, while people inwealthier communities may feel more immune to the risksof AI.
One important consideration of this research is thatwhile we examine support for regulation among a large
national sample, we do not focus on how people’s varied
technological understanding and expertise can impact on
their perceptions of AI and support for the regulation of AI.
For example, engineers and scientists might be aware of thelimitations of AI and hence support the strict regulation of
AI since they can see the associated dangers. This supportof regulations is different from the more diffuse fear towards
AI that can be observed from those in less technologicaloccupations in society. AI is a complex collection oftechnologies that only few fully understand. Still, regulationof AI does not automatically mean that this will hold backa prosperous future. Other areas in our society, in particular
medicine and air traffic, is highly regulated. Their strict rules
are necessary to protect people in a highly dynamic anddangerous environments. Engineers and scientists should,
therefore, not be discouraged by a societal need to regulateAI, but consider it a challenge to create systems that benefit
all of society and address public concerns about this novel
technology.

5.2 Limitations
While this study utilises a representative sample of adults,
it is still limited to New Zealand, a small country in thesouth Pacific. While New Zealand has much cultural overlap
with other English speaking nations such as Australia, theUK, Canada, and the USA, further studies are necessary
to be understand the generalisability of these findings toother contexts, especially those outside the Anglo westernworld. In addition, as New Zealand was ranked 23rd in the
global AI Government Readiness Ranking [31], future work
would greatly benefit from examining the personality anddemographic correlates of public support for regulating
AI in countries that rank both higher on the AI readinessindex as well as in countries that rank lower on the sameindex. It may be that in countries that rank higher on the

13

AI readiness index, people’s support for regulating AI ismore informed by ongoing changes in society and a more
concrete understanding of what the technology could mean
thereby having a differing relationship with demographic
and personality factors. However, future work is needed tobetter understand this possibility.
Another limitation of the current research is that thisstudy did not define in detail what is meant by “artificial
intelligence”. Therefore, it is likely that the attitudes that theparticipants had are based on their general understanding of
AI. This understanding might be more based on portraits
in popular culture and the news than a full technicalunderstanding. In this regard, the current work encountersseveral challenges when discussing the view of societyon the need to regulate AI. First, AI is a diffuse term that
describes a large set of technologies, including neuralnetworks, machine learning, and algorithms. Second, only
experts are typically able to fully understand how thesetechnologies work and what are its limitations. Based onthese epistemological challenges, it is difficult to focus
the views of the general public to the exact technicalspecifications of AI systems. To gain a more general
understanding of the attitudes that the public has on theregulations of AI, therefore, requires the acceptance of thevarious interpretations that the members of the public eachhold. [18] considers this as a “general attitude as the basisof more specific attitudes towards certain AI services”.
They used the Attitude Towards Artificial Intelligence Scale(ATAI) to gain insights into the fears and hopes that people
hold towards AI in general [32]. Future research would
benefit from examining the wider population’s attitudes andsupport for the use of AI in more nuanced ways than relying
on such a blunt instrument as used here.
And finally, given the high cost of conducting national
probability sampling, and the need to reduce participant
fatigue by keeping survey length short, it was only possible
to include a single item to measure support for AI regulation.
However, despite these limitations, the current research
provides a useful starting point for future research byexamining the relationship between various personality
and socio-demographic factors on public support for the
regulation of AI.
Funding Open Access funding enabled and organized by CAUL
and its Member Institutions. The New Zealand Attitudes and Values
Study is funded by a grant from the Templeton Religion Trust(TRT-2021-10418).
Open Access This article is licensed under a Creative Commons
Attribution 4.0 International License, which permits use, sharing,
adaptation, distribution and reproduction in any medium or format,
as long as you give appropriate credit to the original author(s) and thesource, provide a link to the Creative Commons licence, and indicate

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if changes were made. The images or other third party material in thisarticle are included in the article's Creative Commons licence, unlessindicated otherwise in a credit line to the material. If material is notincluded in the article's Creative Commons licence and your intended
use is not permitted by statutory regulation or exceeds the permitteduse, you will need to obtain permission directly from the copyrightholder. To view a copy of this licence, visit http://​creat​iveco​mmons.​
org/​licen​ses/​by/4.​0/.

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