Jul 2026· Knowledge and Decision Systems with Applications· Vol 2, pp. 617-631· 0 citations
TL;DR
Research indicates that proper application of MCDM techniques can relate AI outputs to real-life decision-making by structuring, enforcing transparency, and justifying AI-scoring results for use within an MCDA (Multi-Criteria Decision Analysis) framework, as demonstrated in complex use cases such as transportation planning.
Abstract
Artificial Intelligence (AI) often suffers from a "science-to-service gap," where high-performing models fail to translate into effective real-world decision-making. This systematic literature review investigates this divide, identifying three critical barriers: inadequate technical reasoning, organizational resistance, and stringent regulatory compliance. To bridge this gap, we propose a holistic analytical framework anchored in three interconnected pillars: the human–AI relationship, predicated on mutual trust and complementarity; organizational preparedness, necessitating comprehensive cultural transformation and workforce reskilling; and ethical regulation, prioritizing process transparency and robust accountability. Our findings reveal that successful AI integration extends beyond technical optimization, requiring cross-disciplinary strategies such as participative design and collaborative human–AI audits. By synthesizing these dimensions, this study provides a strategic roadmap for enterprises to navigate systemic challenges, fostering a transition from theoretical AI potential to actionable, empowered, and human-centric decision-making systems in complex operational environments. Research indicates that proper application of MCDM techniques can relate AI outputs to real-life decision-making by structuring, enforcing transparency, and justifying AI-scoring results for use within an MCDA (Multi-Criteria Decision Analysis) framework, as demonstrated in complex use cases such as transportation planning.
This study aims to identify the key challenges and development prospects of AI implementation to enhance business efficiency and ensure sustainable development, providing strategic guidance for organizations to balance technological innovation with responsibility and risk management.
Giedrius Čyras, Vita Marytė Janušauskienė· International Scientific Con...· 1 citation
Artificial intelligence (AI), as a key component of contemporary digital technologies, has emerged as a transformative technology across various sectors, including international relations and diplomacy. As AI technologies continue to evolve within the broader digital transformation of international affairs, they are increasingly influencing diplomatic activities and shaping how governments and international organizations respond to global challenges. This study addresses the following research question: “How does AI shape global diplomacy, and what are its implications for global governance and sustainable development?” To answer this question, the study employs a PRISMA-guided systematic literature review of peer-reviewed studies. The findings suggest that AI enhances diplomatic processes by supporting data-driven decision-making, improving negotiation strategies through predictive analytics, and strengthening crisis response capabilities. However, significant challenges remain, including algorithmic bias, cybersecurity threats, ethical concerns, and the limitations of AI in replicating the human judgment and cultural sensitivity that are essential to effective diplomacy. The review further indicates that responsible AI integration requires appropriate governance frameworks, international cooperation, and continued human oversight. By synthesizing the existing literature, this study contributes to a broader understanding of AI’s evolving role within the digital transformation of global diplomacy and its implications for global governance and sustainable international cooperation.
The study argues that collaborative intelligence should be viewed as an organizational capability rather than merely a technological outcome, requiring deliberate management of human judgment, ethical responsibility, and organizational design.
M. R· International Journal of Phi...· 0 citations
This paper examines the European Union Artificial Intelligence Act (AI Act) as a strategic regulatory response to the rapid and pervasive diffusion of artificial intelligence technologies. Starting from a conceptual framing of AI as an instrument of augmented intelligence rooted in bounded rationality, the contribution highlights how contemporary AI systems adopt satisficing logics through heuristics and large-scale data processing rather than pursuing optimal solutions. The analysis situates the AI Act within the broader EU digital strategy and discusses its risk-based regulatory architecture, which classifies AI systems according to the severity and likelihood of potential harm to fundamental rights, safety, and democratic values. Particular attention is devoted to high-risk AI systems, whose stringent compliance obligations raise significant legal, technical, and economic challenges, especially for SMEs and start-ups. While acknowledging the risks of regulatory rigidity, innovation slowdown, and market entry barriers, the paper also emphasizes the strategic opportunities generated by the AI Act. These include the consolidation of a trustworthy, human-centric AI ecosystem, the strengthening of the Digital Single Market, and the potential emergence of a global regulatory benchmark through the so-called “Brussels effect.” Ultimately, the AI Act is interpreted not merely as a compliance burden, but as a long-term investment capable of transforming regulation into a competitive advantage for the European system.
Enrico Maggiora, Claudia Iacobino· Journal of Emerging Perspect...· 0 citations
This Viewpoint argues that prevailing ethics-based and compliance-oriented approaches to artificial intelligence (AI) in health are insufficient for the dynamic, context-dependent realities of contemporary AI systems. It proposes a shift toward collaborative stewardship, a model that emphasizes shared responsibility, continuous learning and meaningful stakeholder participation across the full lifecycle of AI in health.
The analysis draws on a structured synthesis of peer-reviewed studies, major international policy documents and interdisciplinary scholarship published between 2021 and 2025. Using this evidence base, the paper introduces the C-STEER framework, which outlines practical components of collaborative stewardship and maps them to key stages of the AI lifecycle.
The synthesis reveals that static ethical principles and top-down regulatory models frequently fail to account for real-world variability, equity concerns and the evolving behavior of systems. Governance approaches that combine legal, technical, organizational and participatory mechanisms, supported by continuous monitoring and local adaptation, are better positioned to build trust, enhance accountability and promote equitable outcomes.
By defining collaborative stewardship and presenting the C-STEER framework, this Viewpoint moves beyond compliance-driven governance and offers a practical, context-responsive model for responsible AI integration in health systems.
M. Sokhanvar· International Journal of Hea...· 0 citations
GenAI's current business value is concentrated in augmenting, rather than automating, decision-making, with the strongest evidence for productivity gains among relatively lower-skilled or lower-performing decision-makers, and the mapping of AI capability boundaries within specific decision domains as the central future research prospect.
Rajidi Rammohan Reddy, Vinodray Thumar, Amar Jyoti Borah et al.· International journal of com...· 0 citations
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