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A Scoping Study of Ethics in Artificial
Intelligence Research in Tourism and Hospitality
Pauline A. Milwood1(B), Sarah Hartman-Caverly1, and Wesley S. Roehl2
1Pennsylvania State University—Berks, Reading, PA 19610, USA
{pam325,smh767}@psu.edu
2Temple University, Philadelphia, PA 19121, USA
wesley.roehl@temple.edu
“If artificial intelligence is trained on data from
the real world, who loses out when that data
reflects systemic injustices?” [44]
Abstract. As e-tourism scholars advance innovative research on the use and study
of artificially intelligent systems, it is important to reflect on how well we are
advancing transformative philosophies which ask that emerging fields consider
issues of ethics, power, and bias. We conduct a scoping study of review papers
published between 2015–2021 to understand the extent to which ethical and social
bias issues are identified and treated in AI research in tourism. Results suggest that
the potential for ethical and bias issues in AI in tourism is high, but identification
and treatment of these issues by tourism researchers is weak. We summarize key
implications of this trend and offer suggestions for pursuing a research agenda
which increasingly identifies and treats issues of ethics and bias when advancing
research on artificial intelligence (AI) in tourism.
Keywords: Artificial intelligence (AI) ·Bias ·Ethics ·Review ·Service robots
(SR) ·Scoping study
1 Introduction
E-tourism researchers often “chase down” research deemed “sexy” and innovative, with
no meaningful charting of ethical ontology within the subject or topic areas. There are
benefits to this. For one thing, published scholarship and knowledge dissemination flour-
ishes in real time, as innovative technologies are deployed in industry. Another benefit of
the current publication model of e-tourism research is that knowledge organically devel-
ops and grows without constraints as scholarly search and scientific discovery exploit
new and emerging ontologies. The explosive rate of “smartness” in tourism practice and
the rapid pace of published research justifies deeper and more focused inquiry into the
identification and treatment of ethical, human, and social bias issues in artificial intel-
ligence (AI) in e-tourism research. It is important to note here, that this study does not
Supplementary Information The online version contains supplementary material available at
https://doi.org/10.1007/978-3-031-25752-0_26.
© The Author(s) 2023
B. Ferrer-Rosell et al. (Eds.): ENTER 2023, SPBE, pp. 243–254, 2023.
https://doi.org/10.1007/978-3-031-25752-0_26
244 P. A. Milwood et al.
focus on the nature of artificially intelligent systems, the technology on which it is built
or generated, nor the nature of its use in the production and consumption of tourism.
Rather, the paper focuses on the collection of AI research review papers published
between 2015–2021 to better understand how ethical issues related to AI in tourism are
addressed.
AI research in tourism shows an increasing publication trend in recent years, as
evidenced by publication indices (e.g., Scopus, SSCI). Much of this work has adopted
a post-positivist stance in seeking to engage and make sense of the disruption AI is
having on historically theorized models of tourism. There are specially published spaces
within technology-focused (e.g., Journal of Tourism Technology) and non-technology-
focused (e.g., Journal of Hospitality and Tourism Technology) titles as well as dedicated
published collections (e.g., Annals of Tourism Curated Collection, Robonomics) which
have focused primarily on the impact of AI.
At the same time, the global community has grown increasingly wary of “better,
smarter, faster” innovations. Customers are keenly interested in understanding how per-
sonal data is collected, stored, and used in business processes. Tourism managers and
policy influencers, having observed the historical awakening and present-day ideals for
ethics, environmental sustainability, and corporate social responsibility, should there-
fore seek to understand the critical importance of issues of data privacy, transparency,
and anthropomorphic developments. E-tourism researchers, and specifically those in
AI, should also determine our collective responsibility to not merely chart the rapid
deployment of AI systems in tourism, but lead scholarly discourse on the importance of
maintaining ethics as part of the development of innovative research.
This paper takes a transformative approach [1,2] to investigate the following research
question: “to what extent has e-tourism research identified and treated ethics and bias
issues within the topic area of artificial intelligence (AI)?” We employ a scoping study
[3,4], defined as “a form of knowledge synthesis that addresses an exploratory research
question aimed at mapping key concepts, types of evidence, and gaps in research related
to a defined area or field by systematically searching, selecting, and synthesizing existing
knowledge” [5, p. 373] to systematically identify, chart, analyze and summarize iden-
tification and treatment of these issues in e-tourism research. In the following sections,
we review literature and explain the methodological approach of conducting the scoping
review. We then discuss results and close with implications and considerations for future
work.
2 Literature
2.1 Artificial Intelligence
AI comprises training and input data, the algorithmic ‘rules’ by which data is processed
and analyzed and output results [6]. AI is classified as limited or general. Much of the
research in and around tourism has focused primarily on limited AI applications designed
to complete a discrete and defined task [7]. Limited AI applications in tourism include
big data analytics; smart devices in the Internet of Things (IoT); biometrics like speech
and facial recognition; service robots; and blockchain technologies that enable services
like personalized recommendations and smart chat bots; self-driving luggage carts in
A Scoping Study of Ethics in Artificial Intelligence Research 245
airports; smart check-in, venue access, and security; contactless food delivery or house-
keeping services; and identity verification at border crossings [8,9]. Li et al. [8] classify
limited AI in tourism into four categories of AI service encounters: 1) AI-supplemented
which includes use of real-time and historical data from searching, purchasing, and
social media activity to make personalized recommendations, streamline and deliver
contactless services; 2) AI-generated which includes use of biometric facial, speech,
and movement recognition systems to facilitate self-check-in, smart tourism, and health
monitoring; 3) AI-mediated which comprises service robots, virtual and augmented
reality (VR and AR) to enhance virtual booking; and 4) AI-facilitated which includes
customer experience (CX) and customer relationship management (CRM) [8].
General AI describes (semi-)autonomous computation that solves complex prob-
lems. General AI approaches in tourism include the use of machine learning (ML),
neural networks, or deep learning to discern trends in tourist behaviors, perceptions, and
preferences to forecast demand, streamline service delivery, and upsell and cross-sell to
optimize revenue [10,11]. Further developments in biometrics, emotion detection, and
sentiment analysis signal a shift from smart tourism to “neurotourism,” experiences that
are automatically responsive and customized to the unique preferences and even subcon-
scious desires of individual travelers [10]. In their report on post-pandemic travel and
tourism, McKinsey & Company declare that sentiment analysis and predictive analytics
are necessary for prescriptive business models that maximize return-on-investment [12].
2.2 Ethics, Bias, and Artificial Intelligence (AI)
While there is significant debate in the computer science and technology fields on the
need for ethical AI, ethical considerations, including bias, has seen comparatively less
dialogue in smart tourism scholarship. In their systematic review, Xivuri and Twino-
murinzi [6] determined that most research on AI fairness is not sector-specific (62%
of studies examined), while 21% focused on public services sector (criminal justice,
immigration, and government), 11% on the health sector, 2% on the financial sector, and
2% from the communications sector [6, p. 276]. In their 2021 systematic review of AI in
tourism operations, Li et al. [8] acknowledge “the social and ethical issues of AI, such
as ubiquitous surveillance, privacy, and equality, are important but not considered in the
present study” [8,p.8].
Ethical AI encompasses fairness, accountability, privacy, and autonomy. Fairness
considers both individual attitudes toward AI outcomes, and the sociocultural context
in which the system is deployed. “A fair AI system is one that aligns with shared
human values and supports human flourishing” [14, p. 5]. Specific considerations
include reciprocity and the ethics of care, beneficence and non-maleficence, fidelity
and responsibility, integrity, equitable treatment, justice, and explicability [13,14].
The introduction of automation poses a risk of economic dislocation and consid-
eration for social responsibility within the tourism sector [8]. Saul and Etemad-Sajadi
[11] observe that “seasonal, casual and some operational staff in the hospitality industry
could be most impacted over time by the rise of artificial intelligence.” The Organization
for Economic Cooperation and Development (OECD) prioritizes “inclusive growth, sus-
tainable development, and wellbeing” along with “human-centered values and fairness”
in its “Recommendation of the Council on Artificial Intelligence” [15]. The European
246 P. A. Milwood et al.
Commission’s Ethics Guidelines for Trustworthy AI articulates a framework based on
four ethical principles: respect for human autonomy, prevention of harm, fairness, and
explicability. Prevention of harm includes awareness of “where AI systems can cause
or exacerbate adverse impacts due to asymmetries of power or information,” while fair-
ness describes conditions “free from unfair bias, discrimination, and stigmatization” [16,
p. 12].As a component of ethical AI, algorithmic bias describes “systematically unfair
outcomes that can arbitrarily put a particular individual or group at an advantage or dis-
advantage over another” [7, p. 2). AI bias is significant because it is felt on a wider scale
than human bias, which tends to be more localized in its impact [17]; thus, “seemingly
small error rates can still have a negative impact on a substantial number of individuals”
[18]. Examples include the failure of facial recognition software to correctly identify
Black and East Asian individuals as well as women and gender minorities; the dispropor-
tionate assignment of negative emotions to Black men in biometric sentiment analysis;
discriminatory and exclusionary ad placements in search engines and social media plat-
forms; bias in professional recruitment and candidate ranking in human resources; and
racial bias in medical automation [7,18,19]. AI bias can arise from the training or input
data, in which members of minoritized groups may be underrepresented (un-visible)
or overrepresented (hypervisible) in the data to their disadvantage [14,17], dynamics
which Dancy & Saucier [20] characterize as “predatory inclusion” and “unwanted expo-
sure” (2022), as well as from the design, development, features, processes, or outputs of
the algorithmic model [7,14]. Bias can also be introduced indirectly when input data is
sufficiently correlated with a protected class or characteristic to act as a surrogate for that
attribute, such as the Federal Trade Commission’s warning that use of postal codes to
determine financial creditworthiness can result in illegal racial and ethnic discrimination
[21].
Additional AI ethical considerations include data protection, privacy, autonomy,
trust, safety, and artificial intimacy. Akter et al. [7] found that the exploitation of search,
browse, and purchase history to shape consumer behavior and decision-making can
contribute to trust declines and reputational damage. Additionally, dynamic pricing and
price personalization can evolve into actual or perceived price discrimination [7]. The
Public Voice [45] name data quality, public safety, cybersecurity, and prohibitions on
secret profiling and unitary scoring (so-called “social credit scores”) among its “Univer-
sal Guidelines for Artificial Intelligence”, while OECD includes robustness, security,
and safety in its “Recommendation of the Council on Artificial Intelligence” [15]. Arti-
ficial intimacy describes AI applications designed to mimic social interactions, which
can leverage emotional states and perceived closeness to influence customer behavior
[22]. For a human-centered enterprise like travel and tourism, with all the complexity
and idiosyncrasy that implies, Strauß [23] warns that “AI has a transformative capacity
where ‘natural’ aspects of society are at a risk of becoming reduced to machine-readable,
datafied models that fit the logics of the artificial” (p. 4).
A Scoping Study of Ethics in Artificial Intelligence Research 247
3 Methods
3.1 Data Scoping Steps
By undertaking a scoping study of published review papers, we methodologically asso-
ciate this study with what Arksey & O’Malley [3] and Pham et al. [5] refer to as “a
scoping review of scoping reviews”. While similar to systematic reviews, the aim of this
study was narrower in nature. Specifically, we aimed to map the literature in tourism to
better understand identification and treatment of ethics in AI research.
In response to the guiding research question, peer-reviewed articles which met the
following criteria were selected for the study: published 1) in English, 2) in tourism-
focused research journals, 3) between 2015 and 2021. Electronic database searches
include CAB, ProQuest, and Business Source Premier (BSP). Search terms across all
databases included “tourism” OR “hospitality” AND “artificial intelligence.” Initial and
updated searches were conducted in tourism and business databases, journals, and con-
ference proceedings resulting in 33,848 and 15,414 articles, respectively. Further filtering
for “type =peer reviewed”, “language =English” and “time =2015–2021” resulted in
2,030 articles.
Following Pham et al. [5], we conducted an initial title and abstract review of these
articles. Next, we decided to refine the search strategy by applying the search criteria
to the abstract field to increase the relevance of the corpus of literature that would
be retained for the study. Two investigators independently reviewed abstracts and full
titles to determine final inclusion. Discussions were held to address challenges and
uncertainties related to articles to be included in the study. Review of these articles for
relevance and accessibility led to the retention of 170 articles for manual screening.
Two investigators manually screened these abstracts for relevance (i.e., review-type
articles), duplicity, and accessibility. This was done until 100% agreement on the final
pool of articles was achieved between the investigators. A pool of n =27 articles which
met all study criteria for relevance, publication period, language, accessibility, and review
method was retained for charting analysis. Scoping identification, search, selection, and
screening strategy steps are depicted in the first column and halfway down the middle
column in Fig. 1 (see Supplementary Material).
3.2 Data Charting
Data charting was an iterative process which involved cross-charting between investiga-
tors to address discrepancies and divergencies in charting activities. Two investigators
were responsible for charting the data to determine deficiencies in AI-focused tourism
literature as it relates to empirically reflecting and treating underlying issues of ethics and
bias. To determine this, investigators used thematic analysis to identify and document
ethical and social bias implications of the AI technologies, techniques, or applications
discussed in each article. This was followed by documenting the explicit discussion of
ethics, and bias reduction specifically, in each article. Thus, the ethical and social bias
implications documented in each article may be either explicit or inferred by us during
charting analysis; while the treatment of ethics and bias was sought to be explicit in
the article. The presence of an ethical or social bias implication that is not met with
248 P. A. Milwood et al.
considered discussion elucidates the gap in ethical consideration which we seek to dis-
cover and document in this scoping review. Discussion and debriefs were held before
and after the first round of charting to determine variable parameters which would con-
stitute relevance to the research question. To ensure validity of the final sample, charting
entries were cross-checked by two investigators if there was uncertainty or challenge
during charting (e.g., a viewpoint paper did not review literature; identical but differing
chronology of authors revealed duplication; and use of primary data related to customer
experience with service robots). This process of cross-charting resulted in the deletion of
five articles from the sample pool resulting in a final sample of n =23 articles. Summary
charting notes are provided in the Appendix (see Supplementary Material).
3.3 Data Overview
Descriptive characteristics for the pool of (n =23) review articles are shown in Table
1 (see Supplementary Material). One (1) review article was published in 2017 and one
(1) in 2018, three (3) in 2019, eight (8) in 2020, and seven (7) in 2021. Based on author
descriptor, most papers employed a literature review methodology or review derivative
(e.g., comprehensive research review, systematic review), bibliometric analysis or criti-
cal analysis. Other articles reviewed trade literature and/or industry trends, while others
adopted industry use-case analyses. Also included in the pool of review articles were
viewpoint or perspective papers. The papers reviewed research on service automation,
big data and artificial intelligence (BDAI), service robots (SR) and service automation,
and artificial intelligence-enabled internet of things (AI-enabled IoT). The articles spread
across eleven tourism and hospitality-focused journals with first author institution affil-
iation spanning North America and The Caribbean, The United Kingdom, South and
Southeast Asia, Western and Southeastern Europe, and the South Pacific.
4 Results and Discussion
From preliminary analysis of results of this scoping study on identification and treatment
of ethics and bias issues in AI research in tourism and hospitality, we identify five
categories across articles reviewed: 1) privacy and bias, 2) protection and transparency,
3) (de)humanization and sustainability, 4) inclusion and safety, and 5) policy and legal.
These issues, their treatment in reviewed literature, and implications for research and
practice are summarized below.
4.1 Privacy and Bias
Ethical issues related to guest privacy were mentioned in several papers, and include
behavioral tracking of guests [24,25], use of sensitive guest data, privacy literacy, inti-
mate privacy [26], employee privacy, voice-recognition algorithms, and other “creepy”
human surveillance which could be deemed to violate the sense of trust between guests
and hosts. Bowen and Whalen [24] suggest an article, “Disclosing personal information
via hotel apps: A privacy calculus perspective” for “further reading”. Hajal and Rowson
[25] go further. They acknowledge that ethical concerns around the implementation of
A Scoping Study of Ethics in Artificial Intelligence Research 249
AI-driven technology “could be considered a threat to personal privacy and data rights”
[25, p. 55], given the inaccuracy shown in recent studies, when it comes to identifying
African American and Asian faces in comparison to Caucasian faces. Other examples
include the ability of IoT to keep companies connected to driverless vehicles post-sale
and hotels and restaurants’ ability to keep connected to guests beyond the initial stay or
visit, for target marketing purposes. Research has found that behavioral tracking data
may be used to enhance guest experience [24] and authors generally agree on AI as a
source of social progress [25]. However, the use of sensitive guest data for targeted mar-
keting raises issues of which data guests agree to disclose, and their control over when
and how that data will be used. In keeping with guidelines for the respect for privacy and
data governance [16], AI researchers should engage in applied research collaborative
partnerships to address data privacy, trust of AI and implications for guest experiences.
As well, we should support the establishment of normative practices for industry which
promote sustainable target marketing, data privacy, protection and transparency.
4.2 Protection and Transparency
Protection and transparency involve aspects of artificial intimacy, data protection, own-
ership, consent [28], trustworthiness [29], and guest and employee protections. There is
an urgent need for accountability and transparency in the secure use of sensitive biomet-
ric and psychographic data which feeds autonomous services and the IoT. Still, fulsome
treatment of data protection and transparency issues have been relegated to futuristic
possibilities in published research, leaving data ownership and safekeeping of sensitive
data under-research. While Samara et al. [30] acknowledge the EU’s recently introduced
General Data Protection Regulation and the impact it is expected to have on the way
BDAI is conducted, McCartney and McCartney [28] highlight several deficiencies. They
acknowledge neglect of the topic of data protection and protocols; the need for the hos-
pitality and tourism industry to consider legislative oversight for data protection; and
weak protections for humans in the face of continued SR integration. They call for an SR
hospitality research agenda to address emerging risks and security concerns to include
data protection and protocols. Cobanoglu and Demicco [32] found that hotels bypass
critical cybersecurity protections for computers and software, thereby exposing guest
and employee data to cyber-related risk.
4.3 (De)humanization and Sustainability
(De)humanization and sustainability issues relate to employee protections and include
socio-economic dislocation [33,34] and environmental displacement [10]. The underly-
ing value of AI rests in its anthropomorphic capabilities to perform humanlike tasks based
on algorithmic programming. Several researchers highlight risks facing human workers
whose talents are being supplemented by the very AI systems replicated from human
design (e.g., Chipotle’s Chippy). While AI researchers agree on the benefits to guest
experiences, these models can lead to dehumanization [25,36] of the workforce arising
from emotional exhaustion among service employees [37] stemming from use of SR
employees with whom absenteeism, workplace conflict, and performance-based com-
pensation issues do not arise; and who are able to perform at higher levels of efficiency.
250 P. A. Milwood et al.
The result is social and economic dislocation due to replacement of low-skill and low-
wage positions [35]. Environmental displacement concerns have also been raised based
on the use of AR and VR technologies to facilitate immersive experiences for guests.
On the one hand, environmental and socio-cultural sustainability can be enhanced [10].
On the other hand, cultural and heritage erosion, human-nature (dis)engagement, and
unsustainable tourism communities emerge and should be researched.
4.4 Inclusion and Safety
Ethical issues of social inclusion and safety relate to linguistic and human bias towards
anthropomorphic robots [28], algorithmic bias [30], gender bias [38], and biases related
to e-human resource management [24]. For example, algorithmic bias related to AI lin-
guistics [39,40] can serve to exclude traditionally marginalized groups such as Black,
Indigenous, and People of Color (BIPOC). AI recommender and information systems
for example, are programmed mostly by non-BIPOC groups who unknowingly create
algorithms reflective of social biases and inequities in society. Bhushan [31] encourages
diversity in AI development teams as a means of controlling this form of bias. This is espe-
cially important given that much AI development has occurred in westernized, “white
male” contexts, privileging this group over comparatively underserved gendered and
minoritized groups when it comes to use of AI systems for voice- and face-recognition.
Methods research dedicated to the measurement and management of diversity, equity, and
inclusion in AI-use in tourism should address accessibility across stakeholder groups
and accessibility across spatial-temporal dimensions. This would mean that fair and
inclusive AI access form parts of key destination performance measures.
4.5 Policy and Legal
Policy and legal issues include capabilities and responsibilities of service robots, issues
of risk and compliance [28], and rights of both SRs and humans interacting in the
tourism space. Issues of digital and material inequalities in guest interactions with SRs
and sexbots [35] suggest need for laws to adapt and protect both humans and robots, as
well as to protect human sex workers from their algorithmic competition. McCartney
and McCartney [28] identify the need to address ethical issues of trust and emotional
attachment between SRs and children in their care. Also relevant are issues of power,
discrimination, equality and justice between SRs and employees whose positions and
roles become intelligently automated [39]. Li et al. [42] acknowledge social and ethical
issues of AI, such as ubiquitous surveillance, privacy, and equality as important consid-
erations for future research. Gaur et al. [40] state that it is essential to study the ethics
involved in adopting AI and robotics in the hospitality industry, while Lv et al. [43] call
for greater ethical care in the use of big data artificial intelligence (BDAI) in analyt-
ics and forecasting methods. These authors acknowledging that while existing research
mainly focuses on the bright sides that big data brings into hospitality and tourism, the
dark sides remain largely unexplored and could be investigated by future research. In
response, Cain et al. [35] encourage the need for regulatory and legal frameworks in
AI. These positions invariably reflect the overall neglected treatment of policy and legal
issues in AI studies.
A Scoping Study of Ethics in Artificial Intelligence Research 251
5 Conclusions and Future Work
The hospitality and tourism sector cannot afford to ignore ethical issues in AI. As Akter
et al. [7] point out, there is convincing consensus among scholars that the future source
of competitive advantage of a firm is dependent on the extent to which it can safely
and securely deploy bias-free AI solutions to deliver real-time decisions and solve crit-
ical business problems. Tourism managers and policy influencers who ignore or fail to
effectively incorporate AI ethics in the deployment of innovative technologies risk unfa-
vorable scrutiny from a global community now keenly interested in safe and secure AI.
Experienced and emerging AI researchers are encouraged to forge ahead of the ethics
curve and lead critical discourse on the ethics of AI-use in tourism, even as we continue
to expand cutting-edge AI research.
This study provides support for the shared responsibility we have as tourism
researchers and practitioners to ensure that AI research and use are as ethically encom-
passing as they are novel. By placing attention on the occurrence and treatment of AI
ethics issues in tourism scholarship, this study calls on e-tourism researchers to be more
deliberate in addressing AI ethics in scholarly work; on conference organizers and journal
editors to create meaningful spaces for AI ethics discourse; and on industry stakeholders
to actively engage strategies to mitigate impact of unethical AI practices in designing
service experiences. Furthermore, scanning the journal titles from our set of twenty
identified articles suggests that the conversation about AI, ethics, and tourism appears
to be occurring in our niche journals, rather than in the mainstream of our literature.
Limitations of this scoping study create opportunities for future work. For example,
use of additional databases may have revealed other relevant articles. The dramatic level
of shrinkage in the number of articles, from thousands down to twenty, suggests the
need to further refine search terms, since the terms that were used were identifying both
those articles relevant to this study’s goals as well as many articles that mentioned AI
and AI-related terms in passing. Finally, investigating treatment of ethics within specific
application areas in tourism and hospitality (e.g., big data artificial intelligence, service
robots) presents another future research direction to determine whether AI scholarship
in tourism tracks with other service research fields.
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