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Systems
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Ethics and Governance of Artificial Intelligence (AI) Systems
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Ethics and Governance of Artificial Intelligence (AI) Systems

A special issue of Systems (ISSN 2079-8954). This special issue belongs to the section "Artificial Intelligence and Digital Systems Engineering".

Deadline for manuscript submissions: 31 March 2027 | Viewed by 7184

Editor

Dr. Haris Alibašić

E-Mail Website
Guest Editor
Department of Business Administration, University of West Florida, Pensacola, FL 32514, USA
Interests: hybrid intelligence governance; artificial intelligence ethics and governance; algorithmic accountability and transparency; quadruple bottom line (QBL) sustainability framework; digital governance and public sector AI; organizational resilience (GEOR framework); post-conflict governance and institutional development

Special Issue Information

Dear Colleagues,

Artificial intelligence systems are rapidly transforming operations across public and private sectors, fundamentally altering decision-making, resource allocation, and service delivery. As AI becomes embedded in critical infrastructure, financial systems, emergency response, and strategic planning, robust ethical frameworks and effective governance mechanisms are urgently needed.

This Special Issue explores comprehensive ethical and governance challenges in AI implementation, examining how organizations develop frameworks to ensure AI systems operate responsibly, transparently, and effectively. We seek contributions addressing algorithmic accountability, data security, system reliability, and the balance between automation and human oversight. Research areas include ethical frameworks for AI deployment, governance models and regulatory approaches, transparency in machine learning systems, cybersecurity considerations, risk assessment frameworks, and best practices for the responsible implementation of AI.

We invite scholars to submit original research articles, case studies, and reviews that advance understanding of how ethical principles can be operationalized in AI systems. This research aims to provide practical guidance to organizations navigating AI deployment challenges while maintaining public trust and regulatory compliance, ultimately promoting responsible innovation that benefits both organizations and the public.

In this Special Issue, original research articles, case studies, and reviews are welcome. Research areas may include (but are not limited to) the following topics:

  • Ethical frameworks for AI system design and deployment in public or private sectors;
  • Algorithmic accuracy, reliability, and accountability mechanisms;
  • AI governance models, regulatory approaches, and policy frameworks;
  • Case studies of ethical and governance challenges in AI implementation;
  • Data security, privacy protection, and system integrity in AI applications;
  • Cybersecurity considerations for AI systems;
  • Transparency and explainability in machine learning systems;
  • Human-AI interaction, governance structures, and operational considerations;
  • Performance standards, quality assurance, and validation methods for AI systems;
  • Risk assessment and management frameworks for AI deployment;
  • Organizational structures and procedures for AI oversight;
  • Legal and regulatory compliance in AI operations;
  • Decision-making protocols for AI-augmented systems;
  • Audit and monitoring mechanisms for AI systems;
  • Best practices for responsible AI implementation.

I look forward to receiving your contributions.

Dr. Haris Alibašić
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Systems is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • AI governance
  • algorithmic accountability
  • ethical AI frameworks
  • cybersecurity in AI systems
  • transparency and explainability
  • responsible AI deployment
  • data security and privacy
  • human-AI interaction
  • regulatory compliance
  • operational integrity

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (6 papers)

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20 pages, 319 KB  
Article
Untangling the Algorithmic Leviathan: Palantir, the Post-Factual Polity, and the Infrastructural Crisis of AI Governance in Public Administration
by Haris Alibašić
Systems 2026, 14(7), 879; https://doi.org/10.3390/systems14070879 - 22 Jul 2026
Viewed by 583
Abstract
This paper extends the post-factual polity framework into AI infrastructure and public administration systems theory. It asks how proprietary analytical platforms alter the state’s capacity to produce, audit, and contest the categories through which risk, threat, eligibility, fraud, and deviance become actionable. Using [...] Read more.
This paper extends the post-factual polity framework into AI infrastructure and public administration systems theory. It asks how proprietary analytical platforms alter the state’s capacity to produce, audit, and contest the categories through which risk, threat, eligibility, fraud, and deviance become actionable. Using a structured documentary case analysis of Palantir Technologies across United States agencies and allied jurisdictions, the study applies three diagnostic markers—categorical opacity, contestation displacement, and substitutive dependency—to examine the migration of sovereign classification into vendor-controlled infrastructure. The research gap was identified through an integrative review of public administration, AI governance, algorithmic accountability, systems theory, surveillance studies, and Palantir scholarship. The analysis distinguishes AI epistemic capture from ordinary IT vendor lock-in: the former concerns not merely technical dependence or high exit costs but the loss of public capacity to define and contest consequential administrative categories. The paper argues that administrative law, procurement reform, and algorithmic impact assessment remain necessary but insufficient when agencies lack substitutive capacity. It specifies untangling as a systems-level task involving capacity reconstruction, categorical repatriation, contractual restructuring, and procurement reorientation. Hybrid intelligence is advanced as a post-untangling architecture that embeds machine processing within contestable, accountable, and legally governed human judgment. The contribution is diagnostic, methodological, and design-oriented for AI systems governance. Full article
(This article belongs to the Special Issue Ethics and Governance of Artificial Intelligence (AI) Systems)
27 pages, 1637 KB  
Article
Operationalising Human-Centred AI Governance Under the EU AI Act: A Governance Framework for Human Oversight and Data Accountability
by Hyun-Kyung Lee, Cheolhee Yoon and Bong Gyou Lee
Systems 2026, 14(7), 849; https://doi.org/10.3390/systems14070849 - 17 Jul 2026
Viewed by 649
Abstract
Artificial intelligence (AI) is increasingly embedded in high-stakes socio-technical systems, intensifying concerns about autonomy, accountability, data rights, and fundamental-rights protection. This article develops an exploratory, expert-informed Human-Centred AI (HCAI) pre-design governance framework that translates selected risk-based obligations of the EU Artificial Intelligence Act [...] Read more.
Artificial intelligence (AI) is increasingly embedded in high-stakes socio-technical systems, intensifying concerns about autonomy, accountability, data rights, and fundamental-rights protection. This article develops an exploratory, expert-informed Human-Centred AI (HCAI) pre-design governance framework that translates selected risk-based obligations of the EU Artificial Intelligence Act into early organisational decisions about human oversight, data accountability, documentation, and bounded algorithmic autonomy. Using a sequential mixed-methods design, the study combines an Analytic Hierarchy Process (AHP) survey of 28 experts with think-aloud interviews with 15 of those respondents. The AHP results show that, among the governance criteria included in the model, AI design objectives received the highest upper-level priority and human oversight and control received the highest global priority, followed by personal information protection, design ethics, intellectual property rights protection, and limits of algorithmic autonomy. The interviews explain these priorities by showing that experts framed trustworthy AI governance as a problem of controllability, responsibility allocation, traceable data use, rights protection, and verifiable human intervention rather than model performance alone. The study contributes by defining pre-design governance as a bounded initial consideration-stage decision structure, combining AHP-based priority evidence with qualitative justification logic, and proposing a preliminary governance package of decision points, minimum evidence artefacts, and illustrative operational check criteria. The package is not presented as a validated legal compliance model; instead, it provides an expert-informed translation pathway for future organisational, sector-specific, and empirical validation. Full article
(This article belongs to the Special Issue Ethics and Governance of Artificial Intelligence (AI) Systems)
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26 pages, 958 KB  
Article
Systems Governance for Trustworthy AI: A Framework for Environmental Accountability
by Fatemeh Ahmadi Zeleti
Systems 2026, 14(5), 485; https://doi.org/10.3390/systems14050485 - 30 Apr 2026
Viewed by 878
Abstract
Artificial Intelligence systems increasingly shape environmental decision making, infrastructure planning, and resource use across public and urban domains. However, prevailing AI trust and governance mechanisms, including labels, certifications, and assurance schemes, remain primarily focused on ethical and legal accountability, with limited operational attention [...] Read more.
Artificial Intelligence systems increasingly shape environmental decision making, infrastructure planning, and resource use across public and urban domains. However, prevailing AI trust and governance mechanisms, including labels, certifications, and assurance schemes, remain primarily focused on ethical and legal accountability, with limited operational attention to environmental sustainability. This paper reconceptualises AI trust mechanisms as socio-technical governance infrastructures that can support both ethical assurance and environmental accountability. Drawing on a comparative qualitative analysis of nine AI trust initiatives, the study develops a three-dimensional analytical framework embedding Environmental Performance Indicators across three governance dimensions: trust-building effectiveness, governance readiness, and sustainable adoption. Applying a systems governance lens, the framework examines how governance instruments structure information flows, institutional practices, and lifecycle feedback relevant to environmental performance. It is analytically illustrated through two urban mobility cases, Helsinki’s Whim application and Barcelona’s smart mobility system, to examine how governance conditions enable or constrain the integration of Environmental Performance Indicators in practice. Findings show that current trust mechanisms lack measurable and publicly visible environmental criteria, indicating a gap between AI assurance and environmental governance. The study contributes a systems-oriented framework for evaluating AI trust mechanisms as governance instruments capable of supporting environmental accountability. While exploratory and based on secondary data, the results indicate that future AI trust mechanisms must incorporate measurable sustainability indicators to support eco-efficient and accountable digital transformation. Full article
(This article belongs to the Special Issue Ethics and Governance of Artificial Intelligence (AI) Systems)
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28 pages, 2250 KB  
Article
Occupational Gender Bias in Chinese Generative AI Models: Cross-Model Evidence of Stereotypical Amplification and Systematic Underrepresentation
by Yunhong Liu, Aijun Lin, Sui Peng and Zelong Cai
Systems 2026, 14(3), 286; https://doi.org/10.3390/systems14030286 - 9 Mar 2026
Viewed by 1504
Abstract
Occupational gender stereotypes are widely embedded in social cognition and increasingly reproduced through generative artificial intelligence (AI). Two mainstream Chinese generative AI models (DeepSeek V3 and Qwen 2.5) were audited by eliciting occupation–gender pronoun associations for 72 census-anchored occupations using a standardized questionnaire [...] Read more.
Occupational gender stereotypes are widely embedded in social cognition and increasingly reproduced through generative artificial intelligence (AI). Two mainstream Chinese generative AI models (DeepSeek V3 and Qwen 2.5) were audited by eliciting occupation–gender pronoun associations for 72 census-anchored occupations using a standardized questionnaire and an automated testing pipeline. Each occupation was queried in 1000 independent rounds, yielding 2,880,000 item-level observations. The results show that, for both models, the fitted relationship between census female shares and model-implied female pronoun associations follows an S-shaped pattern. This pattern is consistent with a dominance-amplifying mapping that pushes male-dominated occupations toward lower female attribution and female-dominated occupations toward higher female attribution. Meanwhile, women’s overall visibility is consistently shifted downward: when the census benchmark is 50% female, the predicted female proportion remains below parity at 48% in DeepSeek and 43% in Qwen. Cross-model comparisons reveal substantial heterogeneity in bias profiles: DeepSeek primarily compresses female attribution in male-dominated occupations, whereas Qwen amplifies female dominance in occupations where women already predominate. Overall, these findings characterize a multi-layered output-level bias pattern combining structural amplification with a system-wide downward shift in women’s aggregate visibility. Full article
(This article belongs to the Special Issue Ethics and Governance of Artificial Intelligence (AI) Systems)
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15 pages, 712 KB  
Article
Stage-Aware Governance of Large Language Models: Managing Uncertainty and Human Oversight in AI-Assisted Literature Review Systems
by Junic Kim and Haeyong Shin
Systems 2026, 14(2), 153; https://doi.org/10.3390/systems14020153 - 31 Jan 2026
Cited by 2 | Viewed by 1453
Abstract
This study proposes a stage-aware governance framework for large language models (LLMs) that structures human oversight and accountability across different decision stages in AI-assisted literature review systems. Large language models (LLMs) are increasingly embedded in systematic review workflows, yet how human oversight and [...] Read more.
This study proposes a stage-aware governance framework for large language models (LLMs) that structures human oversight and accountability across different decision stages in AI-assisted literature review systems. Large language models (LLMs) are increasingly embedded in systematic review workflows, yet how human oversight and accountability should be structured across different decision stages remains unclear. This study evaluates three LLMs in a controlled two-stage literature review workflow—title-and-abstract screening and eligibility assessment—using identical evidence inputs and fixed inclusion criteria, with outputs benchmarked against expert consensus under fully reproducible conditions with standardized prompts and comprehensive logging. While LLMs closely matched expert decisions during screening (precision 0.83–0.91; F1 up to 0.89; Cohen’s κ 0.65–0.85), performance degraded substantially at the eligibility stage (F1 0.58–0.65; κ 0.52–0.62), indicating increased epistemic uncertainty when fine-grained criteria must be inferred from abstract-level information. Importantly, disagreements clustered in borderline cases rather than random error, supporting a stage-aware governance approach in which LLMs automate high-throughput screening while inter-model disagreement is operationalized as an actionable uncertainty signal that triggers human oversight in more consequential decision stages. These findings highlight the need for explicit oversight thresholds, responsibility allocation, and auditability in the responsible deployment of AI-assisted decision systems for evidence synthesis. Full article
(This article belongs to the Special Issue Ethics and Governance of Artificial Intelligence (AI) Systems)
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Other

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30 pages, 3460 KB  
Systematic Review
Large Language Models for Interpretive Support in Digital Government Service Systems: A Systematic Review
by Xueyu Zhang, Zezhong Ma, Yuancheng Ma and Illisriyani Ismail
Systems 2026, 14(7), 823; https://doi.org/10.3390/systems14070823 - 10 Jul 2026
Viewed by 456
Abstract
Large language models (LLMs) are increasingly discussed in digital government research, but existing studies remain fragmented across application opportunities, technical performance, and governance risks, with limited synthesis of how they support service provision within digital government service processes. Using the concept of interpretive [...] Read more.
Large language models (LLMs) are increasingly discussed in digital government research, but existing studies remain fragmented across application opportunities, technical performance, and governance risks, with limited synthesis of how they support service provision within digital government service processes. Using the concept of interpretive support, this study examines: (1) what forms of interpretive support LLMs provide; (2) what task- and service-level effects are reported; and (3) what governance conditions shape responsible integration. Drawing on a socio-technical systems perspective, the study conducts a PRISMA-informed systematic review and thematic synthesis of 60 studies from the Web of Science Core Collection and Scopus. The findings show a shift from stand-alone question answering to broader forms of interpretive support, including rule explanation, service navigation, complaint interpretation, issue routing, and back-office knowledge structuring. The strongest evidence concerns efficiency, responsiveness, and accessibility, whereas claims about trust, accountability, and wider public value remain less well supported. The review concludes that public value is conditional rather than automatic, depending on reliability, legal boundaries, responsibility allocation, data security and privacy, and organizational capacity. Future research should examine whether these service-level gains persist in routine service environments and how governance mechanisms affect service quality, equity, and accountability. Full article
(This article belongs to the Special Issue Ethics and Governance of Artificial Intelligence (AI) Systems)
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