The Role of Artificial Intelligence in Digital Sustainability and Governance Systems
Background. To successfully modernise public governance for the digital era, the strategic adoption of artificial intelligence (AI) is an absolute necessity. The AI, however, cannot deliver sustainable value in a vacuum. Unlocking its true potential depends on institutional readiness, proactive regu...
Gespeichert in:
| Datum: | 2026 |
|---|---|
| Hauptverfasser: | , |
| Format: | Artikel |
| Sprache: | Englisch |
| Veröffentlicht: |
Dr. Viktor Koval
2026
|
| Schlagworte: | |
| Online Zugang: | https://ees-journal.com/index.php/journal/article/view/345 |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| Назва журналу: | Economics Ecology Socium |
| Завантажити файл: |  |
Institution
Economics Ecology Socium| _version_ | 1869472207962374144 |
|---|---|
| author | Keser, Ivana Mudrić, Mihael Mišo |
| author_facet | Keser, Ivana Mudrić, Mihael Mišo |
| author_institution_txt_mv | [
{
"author": "Ivana Keser",
"institution": "Institute for Development and International Relations, Zagreb, Croatia"
},
{
"author": "Mihael Mišo Mudrić",
"institution": "University of Zagreb, Zagreb, Croatia"
}
] |
| author_sort | Keser, Ivana |
| baseUrl_str | https://ees-journal.com/index.php/journal/oai |
| collection | OJS |
| datestamp_date | 2026-06-30T15:36:43Z |
| description | Background. To successfully modernise public governance for the digital era, the strategic adoption of artificial intelligence (AI) is an absolute necessity. The AI, however, cannot deliver sustainable value in a vacuum. Unlocking its true potential depends on institutional readiness, proactive regulatory frameworks, and robust governance capacity to mitigate both operational and societal risks.
Purpose. This study aims to examine how the evolving EU AI Act’s governance framework shapes the concept of Sustainable Digital Governance and to evaluate AI literacy and institutional readiness among Croatian civil servants as critical prerequisites for responsible AI deployment.
Findings. This empirical study utilises survey data collected in mid-2025 from Croatian civil servants to evaluate AI literacy, perceptions, and organisational readiness. While these preliminary findings are subject to certain methodological limitations, they reveal a severe implementation gap between regulatory mandates and institutional capacity. 73.6% of surveyed officials reported no prior exposure to AI-related training or professional discourse, and 80.2% could not identify high-risk AI systems within their operational environments. 62.6% noted a lack of organisational guidance on AI software utilities and their associated risks, leaving only 25.3% who felt capable of executing tasks in compliance with AI Act risk-mitigation standards.
Implications. These insights demonstrate that successful public sector AI governance requires moving beyond mere regulatory compliance to embrace targeted capacity-building, institutional learning, and well-defined national implementation strategies. Ultimately, this study synthesises theoretical perspectives with empirical data to introduce a holistic framework for the sustainable governance of AI systems. It concludes with actionable recommendations designed to guide policymakers and organisational leaders through the regulatory and operational complexities of an AI-driven landscape. |
| doi_str_mv | 10.61954/2616-7107/2026.10.2-6 |
| first_indexed | 2026-07-01T01:00:29Z |
| format | Article |
| fulltext |
Economics Ecology Socium e-ISSN 2786-8958
Volume 10 Issue 2 (2026) ISSN-L 2616-7107
81
Research Article
UDC 004.8:330.341
JEL: O33, Q56, G34, O32, L86
THE ROLE OF ARTIFICIAL INTELLIGENCE IN
DIGITAL SUSTAINABILITY AND GOVERNANCE
SYSTEMS
Ivana Keser *
Institute for Development and
International Relations,
Zagreb, Croatia
ORCID iD: 0000-0002-8994-3384
Mihael Mišo Mudrić
University of Zagreb,
Zagreb, Croatia
ORCID iD: 0000-0001-5364-4385
*Corresponding author
E-mail: ivana.keser@irmo.hr
Background. To successfully modernise public
governance for the digital era, the strategic adoption of
artificial intelligence (AI) is an absolute necessity. The AI,
however, cannot deliver sustainable value in a vacuum.
Unlocking its true potential depends on institutional
readiness, proactive regulatory frameworks, and robust
governance capacity to mitigate both operational and
societal risks.
Purpose. This study aims to examine how the
evolving EU AI Act’s governance framework shapes the
concept of Sustainable Digital Governance and to evaluate
AI literacy and institutional readiness among Croatian civil
servants as critical prerequisites for responsible AI
deployment.
Findings. This empirical study utilises survey data
collected in mid-2025 from Croatian civil servants to
evaluate AI literacy, perceptions, and organisational
readiness. While these preliminary findings are subject to
certain methodological limitations, they reveal a severe
implementation gap between regulatory mandates and
institutional capacity. 73.6% of surveyed officials reported
no prior exposure to AI-related training or professional
discourse, and 80.2% could not identify high-risk AI
systems within their operational environments. 62.6% noted
a lack of organisational guidance on AI software utilities
and their associated risks, leaving only 25.3% who felt
capable of executing tasks in compliance with AI Act risk-
mitigation standards.
Implications. These insights demonstrate that
successful public sector AI governance requires moving
beyond mere regulatory compliance to embrace targeted
capacity-building, institutional learning, and well-defined
national implementation strategies. Ultimately, this study
synthesises theoretical perspectives with empirical data to
introduce a holistic framework for the sustainable
governance of AI systems. It concludes with actionable
recommendations designed to guide policymakers and
organisational leaders through the regulatory and
operational complexities of an AI-driven landscape.
Keywords: Artificial Intelligence, AI Governance, AI
Literacy, Digital Governance, Sustainability.
Received: 15/10/2025
Revised: 14/04/2026
Accepted: 21/05/2026
Published: 30/06/2026
DOI: 10.61954/2616-7107/2026.10.2-6
© Economics Ecology Socium, 2026
CC BY-NC 4.0 license
Economics Ecology Socium e-ISSN 2786-8958
Volume 10 Issue 2 (2026) ISSN-L 2616-7107
82
1. Introduction.
Sustainable development, far from being
an exclusively ecological term, represents a
multidimensional paradigm aimed at
achieving economic, environmental, social,
cultural, and governance objectives in an
intertemporal context, with an emphasis on
meeting the needs of present and future
generations (Keser, 2023). Although
numerous definitions exist, its common thread
is the recognition of the necessity to align
these different dimensions. Haughton (1999)
summarises this complexity, stating that
sustainable development requires “economic
and social systems that foster long-term
ecological stewardship of resources,
recognising the interdependence of social
justice, economic well-being, and ecological
management”. Kemp et al. (2005) emphasise
that the adoption of this holistic approach,
which in recent years has been supplemented
by cultural and governance dimensions, is a
key to understanding the foundations on
which modern development policies must be
built.
Within this framework, the dimension of
sustainable governance is primary in
operationalising the sustainability concept. It
encompasses the application of the principles
of the good governance doctrine, as defined
by the Council of Europe (2007). These
principles include accountability,
transparency, policy coordination, efficiency,
effectiveness, the rule of law, innovation and
openness to change, sustainability and long-
term orientation, and other quality standards.
These principles are not abstract ideals but
concrete standards that enable the
functionality of an integrated approach to
governance, which is a prerequisite for
sustainable development (Keser et al., 2023).
Sustainable governance plays a central
role in digital transition and sustainability,
imposing an integrated approach that unifies
horizontal and vertical levels of coordination
and cooperation. This study aims to apply this
framework to highlight the complex
interrelationships among technology,
sustainability, and governance systems.
Despite the increasing attention to
artificial intelligence (AI) governance and
regulatory frameworks, existing research has
predominantly focused on normative, legal,
and policy-oriented analyses of emerging
regulatory regimes, particularly the European
Union Artificial Intelligence Act (Cancela-
Outeda, 2024; Veale & Borgesius, 2021).
Much of this literature examines the ethical
principles, legal design, and institutional
architecture of AI governance, emphasising
regulatory instruments, risk-based approaches
and the protection of fundamental rights.
Empirical evidence on public
institutions’ capacity and preparedness for
implementation is scarce. As evidenced by
several studies in the field of digital
government (Wirtz et al., 2019; Jørgensen &
Ma, 2025), much emphasis is placed on civil
servants’ levels of digital literacy and on
institutions’ organisational capacity to handle
digital governance transformation.
Conducted research (Madan & Ashok,
2023; OECD, 2025) indicates that the
prerequisites for successful digital (AI)
transformation of the public service sector
depend not only on the regulatory framework,
but, to a great extent, on organisational and
human capacity in the digital arena.
Hence, the results indicate that,
alongside ensuring regulatory compliance
mechanisms, there is an evident need to
enhance AI literacy and competence (skills)
education to meet the requirements of
sustainable digital transformation. Recent
research in digital governance (Janssen et al.,
2020) emphasises data governance and the
responsible use of AI systems when deployed
in the public sector.
The study is grounded in the
fundamental concepts of integrated
governance and policy integration, as well as
the primary drivers of digital transformation.
Based on the integrated governance principles,
governance is perceived as a coordinated and
participatory process that promotes cross-
sectoral cooperation and aligns institutional
with multidimensional sustainability
objectives (Keser, 2023; Peters, 2004).
Economics Ecology Socium e-ISSN 2786-8958
Volume 10 Issue 2 (2026) ISSN-L 2616-7107
83
The integrated governance methods
include policy integration on both the horizontal
and vertical poles, requiring institutional
coordination and policy coherence, and require
stakeholder collaboration and participation in
decision-making across various governance
levels (Domorenok et al., 2021).
The study aims to address the noted
scarcity by providing preliminary empirical
insights from the Croatian public sector. Based
on the national AI literacy and governance
curricula provided by the State School of Public
Administration and Judicial Academy (DSJU,
2026; Duić et al., 2026), the study is based on a
questionnaire addressing various segments of AI
literacy, such as perception of AI, safety and
security of using AI tools, AI literacy levels,
regulatory obligations awareness, and similar.
The purpose of the preliminary findings is
to point to the level of institutional and
regulatory alignment among and within various
public institutions in the Republic of Croatia.
The main research question segments are
divided into three sections. Section 1 analyses
how Croatian civil servants perceive the role of
AI in public administration. Section 2 analyses
to what extent Croatian civil servants
understand the fundamental principles of AI
literacy and to what extent they are aware of
obligations and responsibilities codified in the
European Union’s Artificial Intelligence Act
(AIA). Finally, Section 3 analyses the extent to
which the current public sector’s capacities are
aligned with regulatory requirements regarding
AI systems’ implementation. The overall aim of
both the preliminary and further analyses is to
understand the efforts required to ensure
successful AI implementation through
sustainable digital governance practices.
2. Literature Review.
The overwhelming majority of recent
relevant literature points to AI as the central
driver of the digital transition. While McCarthy
(2007) lays an essential but strictly technical
foundation by defining AI as the science and
engineering of making intelligent machines,
this study deals with what that definition does
not cover: the complex system of governance
and sustainability necessary for the responsible
application of AI systems in society.
Russell and Norvig (2020) modernised
the approach by defining AI as a “rational
agent” that strives to maximise a given
objective, directly connecting with the question
of governance. Consequently, it is crucial to
analyse how to ensure that the objectives of
these AI agents align with complex social,
ethical, and environmental sustainability
standards. In this sense, the role of AI in the
context of sustainability is extremely
significant because its value, as pointed out by
Nishant et al. (2020), lies not only in
optimising resource consumption but also in its
ability to improve effective environmental
management and act as a powerful technology
with the potential to accelerate the green
transition and enhance productivity, efficiency
and innovation.
AI has the potential of creating numerous
environmental and ethical challenges. The core
issue within the digital sustainability paradigm
revolves over the question to what extent
digitalization supports and/or enables
sustainability.
As visible through the example of
energy- and water-hungry data centres
(especially when servicing large language
models (LLM) data training and consumption),
AI or, rather, digital infrastructure as whole,
creates a significant environmental impact
(Nishant et al., 2020; Toderas, 2025). This
contradicts the general strive towards
decarbonisation, and examples such as this
one, that indicate significant dichotomy in
goals and outcomes has been thoroughly
addressed in the relevant literature (Toderas,
2025; van Wynsberghe, 2021).
AI sustainability, hence, requires a
holistic approach whereby sustainability
through AI reveres to the reduction of negative
consequences while, at the same time, focusing
on enhancing productivity, efficiency and
innovation.
The noted direction requires a
multifaceted approach (Nishant et al., 2020)
that simultaneously drives technology,
sustainability, and governance in a unified,
bottom-up approach (individual to state),
reducing reductionism and promoting
sustainable solutions that encompass the
whole.
Economics Ecology Socium e-ISSN 2786-8958
Volume 10 Issue 2 (2026) ISSN-L 2616-7107
84
The governance framework that enables
noted concepts is driven by regulatory
frameworks such as the European Union’s
(EU) Artificial Intelligence Act (AIA),
preceded by the Ethics Guidelines for
Trustworthy AI (European Commission, 2019).
Simultaneously, an important holistic
framework for sustainable governance is
visible in the OECD Council Recommendation
on Artificial Intelligence (OECD, 2019), which
explicitly calls for the development of AI
systems that are transparent and accountable, in
accordance with sustainable development
principles and protection of human rights.
In more general terms, digital
transformation requires practical and
responsible AI application. This requires new
governance models that do not stem from
technology itself, but from strategic and ethical
consideration of the societal and systemic
implications of AI applications. To that end,
the present study highlights the triangular
interrelationship among governance,
sustainability, and technology. It provides
indications on how this methodology can be
implemented in the Croatian public
administration, having in mind the current state
of system readiness and human resources
capacity to adopt the technology sustainably
and responsibly.
3. Methodology.
The research methodology combines the
normative framework qualitative analysis
(focus on the Artificial Intelligence Act
(Regulation (EU) 2024/1689 laying down
harmonised rules on artificial intelligence
(European Parliament and Council of the
European Union, 2024b) (AI Act)), and
empirical research by means of an anonymous
survey questionnaire for Croatian civil servants
(perceived levels of AI literacy, institutional
readiness, and regulatory awareness).
The first section provides a comprehensive
analysis of the applicable normative framework
(as noted, the focus is placed on the AI Act). The
section examines and interprets the core legal
provisions and principles of sustainable digital
governance relevant to future compliance
obligations of the public sector.
The subsequent empirical section utilizes
preliminary data from an anonymous survey
conducted in 2025 among Croatian civil
servants enrolled in the State School for Public
Administration’s AI literacy training program
(various specialized educational programmes,
from basic AI fundamentals, to advanced AI
compliance for local and regional governance).
By presenting initial findings from this research,
the study identifies emerging trends and critical
challenges regarding AI literacy, regulatory
awareness, and the institutional readiness
required to effectively govern AI systems within
daily administrative operations in various levels
of public administration.
The analytical sample comprises 91
respondents, whose profile is defined by
functional relevance (persons designated by
various public administration bodies to enroll
into various educational programs) rather than
socio-demographic characteristics. All
participants are civil servants whose
professional responsibilities are utilizing digital
systems, database management, information
processing, or decision-support tools in their
everyday activities. Representing a diverse
range of public-sector institutions and
administrative levels (over 80 different
institutions have participated in the educational
programs), this sample provides a focused and
functionally pertinent perspective on the
preliminary insights within the modern public
administration landscape.
The study does not provide a granular
breakdown of respondents based on age, gender,
seniority, or specific affiliation, primarily to
ensure anonymity of the research design by
excluding personal or institutional identifiers.
Participation in the questionnaire was entirely
voluntary and conducted under strict anonymity,
ensuring no personal data was collected. All
respondents were fully informed of the study's
purpose and the fact that their data would be
utilized exclusively in an aggregated format for
scientific research.
To ensure a comprehensive assessment of
both technical competence and regulatory
readiness, the survey instrument was designed
as a multi-format framework, the details of
which are presented in Table 1.
Economics Ecology Socium e-ISSN 2786-8958
Volume 10 Issue 2 (2026) ISSN-L 2616-7107
85
Table 1. Survey Instrument Methodology AIA Awareness.
Section Focus Areas Question Formats
AI literacy Familiarity, competence, operational risks
in daily workflows
Dichotomous: training history, IT
involvement, and institutional policies
AIA
awareness
Regulatory obligations (AI Act) and
governance expectations
Likert Scales: perception of AI tool
importance, risk impact, and data processing
functions
Contextual
insights
Identification of specific digital tools and
descriptions of high-risk operational scenarios
Open-ended: contextualization of quantitative
responses to identify governance gaps
Given the non-probabilistic sampling, the
empirical analysis is primarily descriptive.
Dichotomous items were evaluated using
frequencies and percentages, while Likert-scale
responses were examined through measures of
central tendency and dispersion to identify
dominant patterns. Responses to open-ended
questions underwent qualitative content
analysis, in which brief textual entries were
coded for recurring themes and grouped into
categories such as AI usage, governance risks,
regulatory awareness, and institutional capacity
gaps. This thematic grouping served to
complement the quantitative data, providing
contextual insight into how respondents
perceive high-risk AI scenarios, operational
risks, and organisational shortcomings.
The reliance on a non-probabilistic
sample from a specific training program
precludes generalising the findings to the entire
Croatian civil service (the present study aims to
provide indications rather than final findings).
Second, the respondents represent a
functionally specialised subgroup whose
attitudes and digital engagement may differ
significantly from those of the broader public
administration workforce (despite the wide
vertical and horizontal spread of attendees).
Third, the findings are based on self-
reported perceptions rather than objective
assessments of AI literacy or legal compliance
(the educational programme still does not issue
certification; only attendance confirmation is
provided).
Finally, due to the fact that the present
analysis is preliminary and restricted to a
single section of the questionnaire, the analysis
does not attempt to provide an overall scope of
interpretation. Rather, this study is indicative
and aimed at identifying emerging trends and
governance gaps.
Projected continuation of this analysis
aims to expand the sample size (we continue to
conduct questionnaires and plan to include
semi-structure interview qualitative analysis
formats), include a more diverse range of
administrative bodies, and incorporate
comparative methods to enhance analytical
robustness and external validity.
4. The EU AI Act as a Catalyst for
Governance Reform.
The evolution of the normative
framework, particularly through the AI Act,
marks a paradigm shift in sustainable digital
governance. Rather than being a mere set of
restrictions, AI Act establishes legal
foundations and compliance requirements that
shape the deployment of AI tools across the
public sector. This study provides a general
overview of the key provisions of the AI Act,
focusing on how regulatory obligations and
governance expectations require a structured
approach to AI integration.
A central pillar of this new governance
model is the management of high-risk AI
systems. AI Act mandates rigorous compliance
requirements, including implementation of
principles that ensure transparency and
accountability (Mudrić, 2025). By defining the
parameters for legal foundations, AI Act
compels public sector institutions to align their
technological deployment with broader societal
and administrative standards. AI risk
management, especially for high-risk AI
systems, is a focus of technical standards
harmonisation, and relevant international and
national organisations, such as ISO and NIST,
have already published standards for AI risk
management (ISO/IEC 42001:2023, NIST AI
RMF). The European Standardization
Framework is still under development.
Economics Ecology Socium e-ISSN 2786-8958
Volume 10 Issue 2 (2026) ISSN-L 2616-7107
86
The human-centric approach is further
reinforced by the requirement to enable
meaningful human supervision. To ensure
technical competence and regulatory readiness,
governance frameworks must prioritise
technical robustness and the ability of human
operators to intervene in AI-driven processes.
This “human-in-the-loop” (HITL) requirement
is not just a technical safeguard but a legal
necessity designed to mitigate operational risks
and organisational shortcomings. This is
perhaps the most difficult requirement to meet,
given the general constraints on human
resources and the emerging challenges of fully
understanding the complexities of data training
and AI decision-making (Mudrić, 2025).
To be effective, HITL mechanisms must
position human judgment, ethical reasoning, and
personal accountability at the heart of the
decision-making chain. This mandate extends
far beyond a simple “emergency stop” function;
it necessitates the creation of intuitive interfaces
and specialised workflows that empower human
supervisors to interpret, challenge, and override
AI-generated outputs. As noted by Kapoor
(2025), the human role is undergoing a
fundamental shift from passive observation to
active collaboration, involving a continuous
cycle of evaluation, steering, and validation of
an AI system’s performance. The iterative
nature of HITL ensures that AI systems remain
resilient, equitable, and ethically sound
throughout their operational lifecycle. By
adopting this dynamic oversight, organisations
can address the critical need to maintain human
agency over sophisticated technologies, thereby
reinforcing public confidence in their use.
In tandem with oversight, the AI Act
enforces strict transparency and explainability
standards. Providers of high-risk systems are
legally required to provide comprehensive
insights into the logic, capabilities, and inherent
limitations of a model. This regulatory pressure
effectively mandates the implementation of
Explainable AI (XAI) techniques to demystify
“black box” processes. However, as Clement et
al. (2023) emphasise, explainability is not a
peripheral technical add-on, but rather a
composite ingredient that must be woven into
the very fabric of the software development
lifecycle.
Such a foundation of responsible
governance facilitates internal audits, helps
identify algorithmic bias, and provides the
clarity needed for stakeholders to contest
decisions meaningfully. By institutionalising
these requirements, the AI Act transforms AI
governance from a discretionary best practice
into a mandatory legal standard. This shift
elevates AI strategy to a board-level priority,
compelling directors and managers to take an
active role in managing AI risk and AI
compliance. This new regulatory environment
is also catalysing the rise of specialised
executive leadership, such as the Chief AI
Officer (CAIO), dedicated to harmonising
these complex governance duties across the
entire organisation.
4.1. Sustainable Digital Governance.
The AI Act provides the fundamentals of
responsible AI governance. However, in a
broader European regulatory ecosystem, the AI
Act must be reviewed in light of the impact of
organisational activities that incorporate AI
digital transformation on society and the
environment. In this context, the concept of
Corporate Digital Responsibility (CDR) and the
legal mandates introduced by by Directive (EU)
2024/1760, known as the Corporate
Sustainability Due Diligence (CSDDD)
(European Parliament and Council of the
European Union, 2024b) are critical. CDR
offers a proactive ethical framework that serves
as a moral compass (Lobschat et al., 2021),
guiding an organisation’s digital impact through
principles of fairness, societal well-being, and
trust (AI Ethics Guidelines). While the AI Act
mandates specific technical and procedural
safeguards, CDR points to the underlying values
and strategic intent behind the deployment of
technology (Herden et al., 2021). Hence,
evaluating AI Act in synergy with CSDDD
reveals a necessary convergence. While the AI
Act focuses on safety and fundamental rights
related to the technology itself, CSDDD
obligates large entities to identify, prevent, and
mitigate adverse environmental and human
rights impacts across their entire value chain.
When an AI system is integrated into
corporate or administrative processes, it
becomes a permanent link in the chain.
Economics Ecology Socium e-ISSN 2786-8958
Volume 10 Issue 2 (2026) ISSN-L 2616-7107
87
For instance, a biased algorithm utilised
in supply chain management could lead to
discriminatory practices that fall directly under
the CSDDD’s obligations.
The noted intersection creates regulatory
pressure compelling organisations to adopt the
“Sustainable Digital Governance” framework.
This approach aligns with the academic call for
“Sustainable AI” (van Wynsberghe, 2021),
which requires simultaneous consideration of
AI’s environmental sustainability and its
potential to advance sustainability goals. The
described mechanism requires a holistic
approach in which IT and sustainability
departments must collaborate to manage AI
Act compliance and CSDDD reporting.
Equally, governing bodies must manage the
entire socio-technical system, assessing both
the intrinsic properties of AI tools and their
extrinsic impacts on society through a unified
governance lens.
4.2. Fundamental Transformation of
the Public Sector.
A participatory approach involving all
key stakeholders is necessary to ensure
transparency and accountability within the AI
ecosystem. The governing structure, civil
society, the private sector, and academia must
be “at the same table” to jointly discuss
governance mechanisms that will minimise
risks and harness the full potential of the
technology.
Such a governance model requires an
integrated approach based on horizontal and
vertical cooperation and a high level of
participation from formal and informal actors
(Tasan-Kok & Vranken, 2011; Schwedler,
2011; Keser et al., 2023) to ensure
transparency, accountability, and explainability
for the AI ecosystem. AI has the potential to
improve the efficiency of public
administration.
The adoption of AI in the public sector
faces specific challenges that require a special
approach, different from that in the private
sector. Zuiderwijk et al. (2021) pay attention to
the governance and regulatory implications of
AI, while highlighting the lack of research
addressing this topic.
Nishant et al. (2020) pointed out that AI
increases efficiency by automating repetitive
tasks, uncovers key insights by analysing vast
amounts of unstructured data, and solves the
complex problems by integrating resources.
Theoretically, AI can “liberate” public
servants from routine tasks, allowing them to
focus on high-value work that demands human
empathy, perception and critical judgment.
Predictive analytics further empowers
governance systems to allocate resources more
effectively, ranging from traffic flow
optimisation in smart cities to energy balancing
in electricity markets to forecasting demand for
social services. For citizens, AI-powered tools
such as chatbots and virtual assistants provide
24/7 access to information, significantly
reducing wait times and improving engagement
(Androutsopoulou et al., 2019). In this sense,
digital transformation provides an opportunity
to fundamentally redesign the relationship
between the state and its citizens, making it
more accessible, transparent, and user-centric.
Simultaneously, the public sector faces a
distinct and expansive set of implementation
obstacles. This is primarily visible through the
noted lack of internal technical expertise and the
ethical risks associated with algorithmic bias. A
major challenge lies in the management and
procurement of AI systems. Public bodies often
lack the technical proficiency to evaluate
complex tools effectively, creating knowledge
asymmetry with private-sector vendors. This
can result in vendor lock-in or the acquisition of
non-compliant technology. While the AI Act
imposes significant obligations on public bodies
as “deployers” of high-risk systems, the
guidelines for translating these legal mandates
into specific procurement requirements remain
underdeveloped (or, as preliminary research
indicates, non-existent).
Ethical risks are particularly acute in the
public sector, where administrative decisions
profoundly impact individual rights and societal
well-being (Wirtz et al., 2022; Kuziemski &
Misuraca, 2020). The use of biased algorithms
in sensitive areas, such as social welfare,
criminal justice, or employment, can perpetuate
existing social inequalities and erode public
trust (Zuiderwijk et al., 2021).
Economics Ecology Socium e-ISSN 2786-8958
Volume 10 Issue 2 (2026) ISSN-L 2616-7107
88
The rapid digitalisation of public services
risks widening the digital divide, potentially
marginalising citizens who lack the necessary
skills or access to navigate new platforms.
Addressing these challenges requires a
commitment to human-centric design, robust
ethical oversight, and substantial investment in
public-sector capacity building, particularly
through education.
5. Results
The synthesis of the normative analysis
and preliminary empirical findings reveals a
substantial misalignment between the
governance mandates of the European
regulatory environment and the current
operational readiness of the surveyed Croatian
civil servants.
Empirical data from 91 respondents
indicates a significant deficiency in
foundational engagement with artificial
intelligence, as 73.6% reported no prior
participation in relevant educational programs
or professional gatherings (including basic
educational programs on the use of standard
office tools).
The noted high numbers directly
correlate with a narrow perception of
institutional risk regarding the use of AI
systems and tools. Only 19.8% of respondents
identified their organisational activities as
involving high-risk AI systems, while the vast
majority (80.2%) failed to recognise such
systems within their professional scope (or
their organisation at large). Despite this low
recognition rate, the qualitative justifications
provided by the minority suggest that actual
operational exposure is far more extensive than
subjective awareness indicates. These
participants substantiated their claims by citing
engagement with state information
infrastructure, e-services, legal case analysis,
and the management of critical databases
(including sensitive personal data such as
health records and Personal Identification
Numbers (OIB)).
Furthermore, they identified high-risk
contexts in financial operations, such as loan
approvals and grant allocations, as well as the
handling of classified datasets and business
secrets.
An analysis of AI literacy and
organisational capacity further underscores the
implementation gap identified in this study’s
theoretical framework. Institutional efforts to
mitigate digital risks appear insufficient, with
62.6% of respondents stating that their
organisations have not provided training on
essential software or the associated digital
risks. This lack of support manifests in low
self-assessed competence regarding the
obligations imposed by the AI Act.
Specifically, only 25.3% of participants felt
capable of performing tasks in compliance with
AI Act requirements, such as risk recognition
and incident mitigation.
The remaining 74.7% expressed a
profound need for targeted education, citing a
lack of theoretical and practical knowledge,
total unfamiliarity with the EU regulatory
framework, and the absence of a clear national
legislative roadmap as primary barriers to
professional readiness. Table 2 synthesises
these quantitative distributions and qualitative
insights to provide a comprehensive overview
of these findings.
These findings suggest that, while the AI
Act conceptualises the public sector as a
natural deployer of high-risk systems (given
the high-risk determinations in Annexes I and
III of the AI Act), the individuals responsible
for operationalising this oversight generally
lack the necessary AI literacy and institutional
support. The data confirms that current
perceptions of AI remain largely detached from
the rigorous governance expectations
established by the EU.
Consequently, a staged research design is
required to further refine these measurement
instruments and facilitate a more systematic
evaluation of reliability and validity in
subsequent phases.
Economics Ecology Socium e-ISSN 2786-8958
Volume 10 Issue 2 (2026) ISSN-L 2616-7107
89
Table 2. Distribution of AI Awareness and Literacy Indicators among Croatian Civil Servants
(N=91).
Thematic
Section
Survey
Indicator /
Research
Question
Response
(Yes)
Response
(No)
Positive
%
Negative
%
Contextual Findings
AIA
Awareness
Participation in
AI-related
educational
programs or
discourse
24 67 26.4% 73.6%
Systematic lack of foundational
information and professional
engagement with AI terms,
concepts, and use-case
Identification of
High-Risk AI
systems in
professional
context
18 73 19.8% 80.2%
State info-infrastructure, e-
services, legal case solutions,
databases/registries, OIB, health
records, loan approvals, financial
transactions, guarantees, SME
grants, employee data, and
business/state secrets, etc.
AI Literacy
Institutional
training on
software use and
associated
operational risks
34 57 37.4% 62.6%
Organisational deficit in
providing risk-based training for
essential digital tools and
software systems
Perceived
capability to
fulfil AIA
regulatory and
risk-mitigation
obligations
23 68 25.3% 74.7%
Need for training on negative
consequences and risk
recognition; absence of
theoretical/practical knowledge;
lack of familiarity with the AIA
and national strategic
frameworks
6. Discussion.
The empirical findings reveal a
substantive "implementation gap" between the
EU's normative ambitions and the current
operational readiness of the Croatian public
administration. Within the context of this study,
this gap signifies a misalignment between the
governance expectations established by the AI
Act (encompassing risk identification,
accountability, human oversight, and
organisational preparedness) and the low levels
of AI literacy, regulatory awareness, and
institutional support reported by respondents.
From a sustainable governance perspective, this
indicates that the principles of accountability,
transparency, and innovation have not yet been
sufficiently operationalised (Schwedler, 2011;
Tasan-Kik & Vranken, 2011). Consequently,
the findings reinforce the view that digital
transformation is not merely a technical
transition, but a strategic and ethical evolution
contingent upon the public sector's capacity to
critically evaluate the implications of AI
deployment (Nishant et al., 2020).
Table 2 further elucidates this
discrepancy. Although few respondents
explicitly identified high-risk AI systems in
their professional environments, their open-
ended responses detailed activities involving
state information infrastructure, e-services,
registries, legal case processing, and financial
decision-making. This suggests that the core
issue is not an absence of high-risk applications
(present in the public sector for a long time
already), but rather a limited capacity to
recognise them within the regulatory categories
introduced by the AI Act (the ability to align the
normative framework with the actual
operational systems in use). This divide between
operational exposure and regulatory awareness
means public servants may manage AI systems
with significant legal and societal consequences
without realising when specific AI governance
obligations are triggered. Several drivers of this
gap can be inferred from the data. First, there is
a foundational educational deficit, as most
participants reported no prior exposure to AI-
related training or professional discourse.
Economics Ecology Socium e-ISSN 2786-8958
Volume 10 Issue 2 (2026) ISSN-L 2616-7107
90
Second, the results suggest weak
organisational integration of AI governance,
with a notable absence of institutional training
regarding software use and operational risks.
Third, the low self-assessed capability to fulfil
AI Act obligations indicates that legal
requirements have not yet permeated daily
administrative workflows.
Research in public administration
emphasises that without integrating AI risk
management into governance systems and
strengthening internal capacity, the systematic
identification and mitigation of risks across the
AI lifecycle remains unattainable (Wirtz et al.,
2020). This limitation similarly hinders the
practical application of human oversight and
explainability, both of which presuppose that
officials can competently question AI systems’
outputs (Russell & Norvig, 2020). These
findings align with broader research suggesting
that insufficient internal knowledge weakens
public institutions during procurement and
oversight (Zuiderwijk et al., 2021). Ultimately,
from the perspective of Sustainable Digital
Governance, effective AI risk management
requires a holistic approach to the socio-
technical system rather than a narrow focus on
formal compliance (van Wynsberghe, 2021).
Bridging this implementation gap demands
both the legal alignment with the AI Act and
systematic investment in AI literacy and
internal governance capacity to ensure a
responsible digital transformation.
7. Conclusions.
Merging the AI Act with the CSDDD
establishes the groundwork for what this study
defines as Sustainable Digital Governance. The
research indicates that for AI to drive digital
sustainability, governance mechanisms must
transcend bureaucratic limits. They must shift
toward an integrated approach that
simultaneously manages technical risks and
social consequences. By bridging the
transparency and safety requirements of the AI
Act with the accountability and value chain
obligations of the CSDDD, this proposed
framework ensures that AI deployment becomes
a pillar of the ethical integrity and resilience of
digital ecosystems.
The primary takeaway of this research is
that modernising governance for the digital era
depends entirely on aligning regulatory
standards with actual operational capacities.
Moving toward a sustainable digital future
demands a transition from reactive, "check-the-
box" compliance to a proactive, risk-centred
strategy.
However, the data reveal that an
implementation gap currently hinders this
transition. Overcoming this hurdle requires a
combination of specialised educational
initiatives and a robust national strategic
roadmap. The public sector can act only as a
competent deployer and guardian of AI
technologies by prioritising institutional
readiness and widespread AI literacy.
While these findings offer significant
insights, several methodological constraints
exist. Because the empirical data rely on a non-
probabilistic sample of 91 civil servants,
potential sampling bias limits the direct
generalisability of these results to the broader
Croatian administration or other national or
international jurisdictions.
Additionally, the study's focus on Croatia
reflects a specific institutional and cultural
environment that may not be found in other EU
Member States. Consequently, future research
should expand its geographic reach and employ
larger, statistically representative samples to
facilitate cross-border comparisons. Upcoming
research phases will utilise qualitative
interviews to triangulate these findings further
and provide a more nuanced understanding of
the institutional barriers to advancing AI
literacy.
Conflict of Interest Statement.
The authors declare that there is no
conflict of interest.
Funding Disclosure.
This research received no external
funding.
AI Use Statement.
The authors declare that no generative AI
tools were used in the preparation of this work.
Economics Ecology Socium e-ISSN 2786-8958
Volume 10 Issue 2 (2026) ISSN-L 2616-7107
91
REFERENCES
Androutsopoulou, A., Karacapilidis, N., Loukis, E., & Charalabidis, Y. (2019). Transforming the
communication between citizens and government through AI-guided chatbots. Government
Information Quarterly, 36(2), 358–367. https://doi.org/10.1016/j.giq.2018.10.001
Cancela-Outeda, C. (2024). The EU’s AI act: A framework for collaborative governance. Internet
of Things, 27, 101291. https://doi.org/10.1016/j.iot.2024.101291
Clement, T., Kemmerzell, N., Abdelaal, M., & Amberg, M. (2023). XAIR: A Systematic
Metareview of Explainable AI (XAI) Aligned to the Software Development Process. Machine
Learning and Knowledge Extraction, 5(1), 78-108. https://doi.org/10.3390/make5010006
Council of Europe. (2007). Good local and regional governance-The European challenge (the
Valencia Declaration); MCL-15(2007)5 final. Valencia: Conference of European Ministers
Responsible for Local and Regional Government. https://www.ftr-bg.org/wp-
content/uploads/2019/10/Valencia_Declaration_MCL-1520075final_EN.pdf
Domorenok, E., Graziano, P., & Polverari, L. (2021). Introduction: Policy integration and
institutional capacity: Theoretical, conceptual and empirical challenges. Policy and Society,
40(1), 1–18. https://doi.org/10.1080/14494035.2021.1902058
DSJU. (2026). Državna škola za javnu upravu. Osnove AI pismenosti: Razumijevanje sustava
umjetne inteligencije [Online live seminar]. https://www.dsju.hr/program-
kategorije/?courseid=922&td=1&ts=6&catid=49&ucionica=1&online_uzivo=1
Duić, D., Vidaković, I., & Mudrić, M. M. (2026). Pravni okvir umjetne inteligencije [Obrazovni
materijali]. Pravosudna akademija. https://epak.gov.hr/course/info.php?id=2311&lang=en
European Commission. (2019). Ethics guidelines for trustworthy artificial intelligence. High-Level
Expert Group on Artificial Intelligence. https://digital-strategy.ec.europa.eu/en/library/ethics-
guidelines-trustworthy-ai
European Parliament and Council of the European Union. (2024a). Directive (EU) 2024/1760 of the
European Parliament and of the Council of 13 June 2024 on corporate sustainability due
diligence and amending Directive (EU) 2019/1937 and Regulation (EU) 2023/2859. Official
Journal of the European Union. https://eur-lex.europa.eu/eli/dir/2024/1760/oj
European Parliament and Council of the European Union. (2024b). Regulation (EU) 2024/1689
laying down harmonised rules on artificial intelligence (Artificial Intelligence Act). Official
Journal of the European Union, L 2024/1689. https://eur-lex.europa.eu/eli/reg/2024/1689/oj
Gasser, U., & Almeida, V. A. F. (2017). A layered model for AI governance. IEEE Internet
Computing, 21(6), 58–62. https://doi.org/10.1109/MIC.2017.4180835
Haughton, G. (1999). Environmental justice and the sustainable city. Journal of Planning Education
and Research, 18, 233–243. https://doi.org/10.1177/0739456X9901800305
Herden, C. J., Alliu, E., Cakici, A., Cormier, T., Deguelle, C., Gambhir, S., Griffiths, C., Gupta, S.,
Kamani, S. R., Kiratli, Y.-S., Kispataki, M., Lange, G., Moles de Matos, L., Tripero Moreno,
L., Betancourt Nunez, H. A., Pilla, V., Raj, B., Roe, J., Skoda, M., Song, Y., Ummadi, P.K.,
Edinger-Schons, L. M. (2021). “Corporate Digital Responsibility”: New corporate
responsibilities in the digital age. Sustainability Management Forum, 29(1), 13–29.
https://doi.org/10.1007/s00550-020-00509-x
Janssen, M., Brous, P., Estevez, E., Barbosa, L. S., & Janowski, T. (2020). Data governance:
Organizing data for trustworthy artificial intelligence. Government Information Quarterly,
37(3), Article 101493. https://doi.org/10.1016/j.giq.2020.101493
Jørgensen, B. N., & Ma, Z. G. (2025). Impact of EU regulations on AI adoption in smart city
solutions: A review of regulatory barriers, technological challenges, and societal benefits.
Information, 16(7), 568. https://doi.org/10.3390/info16070568
Economics Ecology Socium e-ISSN 2786-8958
Volume 10 Issue 2 (2026) ISSN-L 2616-7107
92
Kapoor, M. (2025). The evolving role of human-in-the-Loop evaluations in advanced AI systems.
European Journal of Computer Science and Information Technology, 13(9), 115–126.
https://doi.org/10.37745/ejcsit.2013/vol13n9115126
Kemp, R., Parto, S., & Gibson, R. B. (2005). Governance for sustainable development: Moving
from theory to practice. International Journal of Sustainable Development, 8 (1-2), 12–30.
https://doi.org/10.1504/IJSD.2005.007372
Keser, I. (2023). Interdependence and Complementarity of a Multi-Dimensional Concept of
Sustainable Development and the Integrated Approach to Urban Governance—Case Study
City of Zagreb. Sustainability, 15(12), 9213. https://doi.org/10.3390/su15129213
Keser, I., Puljiz, J., & Maleković, S. (2023). Potencijal i izazovi integriranog urbanog upravljanja.
Hrvatska i komparativna javna uprava, 23 (4), 593–617.
https://doi.org/10.31297/hkju.23.4.1
Kuziemski, M., & Misuraca, G. (2020). AI governance in the public sector: Three tales from the
frontiers of automated decision-making in democratic settings. Telecommunications Policy,
44, 101976. https://doi.org/10.1016/j.telpol.2020.101976
Lobschat, L., Mueller, B., Eggers, F., Brandimarte, L., Diefenbach, S., Kroschke, M., & Wirtz, J.
(2021). Corporate digital responsibility. Journal of Business Research, 122, 875–888.
https://doi.org/10.1016/j.jbusres.2019.10.006
Madan, R., & Ashok, M. (2023). AI adoption and diffusion in public administration: A systematic
literature review and future research agenda. Government Information Quarterly, 40(1),
101774. https://doi.org/10.1016/j.giq.2022.101774
McCarthy, J. (2007). What is artificial intelligence? Stanford: Stanford University, Computer
Science Department. http://jmc.stanford.edu/artificial-intelligence/
Mudrić, M. (2025). Pravni okvir regulacije umjetne inteligencije, drugi dio. Informator.
https://informator.hr/strucni-clanci/pravni-okvir-reguliranja-umjetne-inteligencije-ii
Nishant, R., Kennedy, M., & Corbett, J. (2020). Artificial intelligence for sustainability:
Challenges, opportunities, and a research agenda. International Journal of Information
Management, 53, 102104. https://doi.org/10.1016/j.ijinfomgt.2020.102104
OECD. (2019). Recommendation of the Council on Artificial Intelligence. Paris: OECD
Publishing.
OECD. (2025). Governing with Artificial Intelligence: How AI is accelerating the digital
government journey. Paris: OECD Publishing.
Russell, S. J., & Norvig, P. (2020). Artificial intelligence: A modern approach (4th ed.). London:
Pearson.
Schwedler, H. W. (2011). Integrated urban governance, the way forward. Berlin: Metropolis;
Senate Department for Urban Development.
Tasan-Kok, T., & Vranken, J. (2011). Handbook for multilevel urban governance in Europe:
Analysing participatory instruments for an integrated urban development. Rotterdam:
EUKN.
Toderas, M. (2025). Artificial Intelligence for Sustainability: A Systematic Review and Critical
Analysis of AI Applications, Challenges, and Future Directions. Sustainability, 17(17),
8049. https://doi.org/10.3390/su17178049
van Wynsberghe, A. (2021). Sustainable AI: AI for sustainability and the sustainability of AI. AI
Ethics, 1, 213–218. https://doi.org/10.1007/s43681-021-00043-6
Veale, M., & Zuiderveen Borgesius, F. (2021). Demystifying the draft EU Artificial Intelligence
Act. Computer Law Review International, 22(4), 97–112. https://doi.org/10.9785/cri-2021-
220402
Economics Ecology Socium e-ISSN 2786-8958
Volume 10 Issue 2 (2026) ISSN-L 2616-7107
93
Wirtz, B. W., Weyerer, J. C., & Geyer, C. (2019). Artificial intelligence and the public sector—
applications and challenges. International Journal of Public Administration, 42(7), 596–615.
https://doi.org/10.1080/01900692.2018.1498103
Wirtz, B. W., Weyerer, J. C., & Sturm, B. J. (2020). The dark sides of artificial intelligence: An
integrated AI governance framework for public administration. International Journal of
Public Administration, 43, 818–829. https://doi.org/10.1080/01900692.2020.1749851
Zuiderwijk, A., Chen, Y., & Salem, F. (2021). Implications of the use of artificial intelligence in
public governance: A systematic literature review and a research agenda. Government
Information Quarterly, 38, 101577. https://doi.org/10.1016/j.giq.2021.101577
|
| id | oai:ojs2.www.ees-journal.com:article-345 |
| institution | Economics Ecology Socium |
| keywords_txt_mv | keywords |
| language | English |
| last_indexed | 2026-07-01T01:00:29Z |
| publishDate | 2026 |
| publisher | Dr. Viktor Koval |
| record_format | ojs |
| resource_txt_mv | ees-journalcom/cd/e052a59631d06f43c1816484cd5569cd.pdf |
| spelling | oai:ojs2.www.ees-journal.com:article-3452026-06-30T15:36:43Z The Role of Artificial Intelligence in Digital Sustainability and Governance Systems The Role of Artificial Intelligence in Digital Sustainability and Governance Systems Keser, Ivana Mudrić, Mihael Mišo Artificial Intelligence, AI Governance, AI Literacy, Digital Governance, Sustainability. Artificial Intelligence, AI Governance, AI Literacy, Digital Governance, Sustainability. Background. To successfully modernise public governance for the digital era, the strategic adoption of artificial intelligence (AI) is an absolute necessity. The AI, however, cannot deliver sustainable value in a vacuum. Unlocking its true potential depends on institutional readiness, proactive regulatory frameworks, and robust governance capacity to mitigate both operational and societal risks. Purpose. This study aims to examine how the evolving EU AI Act’s governance framework shapes the concept of Sustainable Digital Governance and to evaluate AI literacy and institutional readiness among Croatian civil servants as critical prerequisites for responsible AI deployment. Findings. This empirical study utilises survey data collected in mid-2025 from Croatian civil servants to evaluate AI literacy, perceptions, and organisational readiness. While these preliminary findings are subject to certain methodological limitations, they reveal a severe implementation gap between regulatory mandates and institutional capacity. 73.6% of surveyed officials reported no prior exposure to AI-related training or professional discourse, and 80.2% could not identify high-risk AI systems within their operational environments. 62.6% noted a lack of organisational guidance on AI software utilities and their associated risks, leaving only 25.3% who felt capable of executing tasks in compliance with AI Act risk-mitigation standards. Implications. These insights demonstrate that successful public sector AI governance requires moving beyond mere regulatory compliance to embrace targeted capacity-building, institutional learning, and well-defined national implementation strategies. Ultimately, this study synthesises theoretical perspectives with empirical data to introduce a holistic framework for the sustainable governance of AI systems. It concludes with actionable recommendations designed to guide policymakers and organisational leaders through the regulatory and operational complexities of an AI-driven landscape. Background. To successfully modernise public governance for the digital era, the strategic adoption of artificial intelligence (AI) is an absolute necessity. The AI, however, cannot deliver sustainable value in a vacuum. Unlocking its true potential depends on institutional readiness, proactive regulatory frameworks, and robust governance capacity to mitigate both operational and societal risks. Purpose. This study aims to examine how the evolving EU AI Act’s governance framework shapes the concept of Sustainable Digital Governance and to evaluate AI literacy and institutional readiness among Croatian civil servants as critical prerequisites for responsible AI deployment. Findings. This empirical study utilises survey data collected in mid-2025 from Croatian civil servants to evaluate AI literacy, perceptions, and organisational readiness. While these preliminary findings are subject to certain methodological limitations, they reveal a severe implementation gap between regulatory mandates and institutional capacity. 73.6% of surveyed officials reported no prior exposure to AI-related training or professional discourse, and 80.2% could not identify high-risk AI systems within their operational environments. 62.6% noted a lack of organisational guidance on AI software utilities and their associated risks, leaving only 25.3% who felt capable of executing tasks in compliance with AI Act risk-mitigation standards. Implications. These insights demonstrate that successful public sector AI governance requires moving beyond mere regulatory compliance to embrace targeted capacity-building, institutional learning, and well-defined national implementation strategies. Ultimately, this study synthesises theoretical perspectives with empirical data to introduce a holistic framework for the sustainable governance of AI systems. It concludes with actionable recommendations designed to guide policymakers and organisational leaders through the regulatory and operational complexities of an AI-driven landscape. Dr. Viktor Koval 2026-06-30 Article Article Peer-reviewed Article application/pdf https://ees-journal.com/index.php/journal/article/view/345 10.61954/2616-7107/2026.10.2-6 Economics Ecology Socium; Vol. 10 No. 2 (2026): Economics Ecology Socium; 81-93 Економіка Екологія Соціум; Том 10 № 2 (2026): Economics Ecology Socium; 81-93 2616-7107 2616-7107 10.61954/2616-7107/2026.10.2 en https://ees-journal.com/index.php/journal/article/view/345/297 Copyright (c) 2026 Economics Ecology Socium https://creativecommons.org/licenses/by-nc/4.0 |
| spellingShingle | Artificial Intelligence AI Governance AI Literacy Digital Governance Sustainability. Keser, Ivana Mudrić, Mihael Mišo The Role of Artificial Intelligence in Digital Sustainability and Governance Systems |
| title | The Role of Artificial Intelligence in Digital Sustainability and Governance Systems |
| title_alt | The Role of Artificial Intelligence in Digital Sustainability and Governance Systems |
| title_full | The Role of Artificial Intelligence in Digital Sustainability and Governance Systems |
| title_fullStr | The Role of Artificial Intelligence in Digital Sustainability and Governance Systems |
| title_full_unstemmed | The Role of Artificial Intelligence in Digital Sustainability and Governance Systems |
| title_short | The Role of Artificial Intelligence in Digital Sustainability and Governance Systems |
| title_sort | role of artificial intelligence in digital sustainability and governance systems |
| topic | Artificial Intelligence AI Governance AI Literacy Digital Governance Sustainability. |
| topic_facet | Artificial Intelligence AI Governance AI Literacy Digital Governance Sustainability. Artificial Intelligence AI Governance AI Literacy Digital Governance Sustainability. |
| url | https://ees-journal.com/index.php/journal/article/view/345 |
| work_keys_str_mv | AT keserivana theroleofartificialintelligenceindigitalsustainabilityandgovernancesystems AT mudricmihaelmiso theroleofartificialintelligenceindigitalsustainabilityandgovernancesystems AT keserivana roleofartificialintelligenceindigitalsustainabilityandgovernancesystems AT mudricmihaelmiso roleofartificialintelligenceindigitalsustainabilityandgovernancesystems |