Literary trends of the update of the living systematic mapping review REHALISE up to May 31st, 2025, suggest a slight and gradual shift toward more clinically oriented applications, though robustness and reproducibility remain critical concerns.
Francesco Negrini, Calogero Malfitano, M. Valeri et al.· European Journal of Physical...· 0 citations
A graph-grounded neuro-symbolic framework that integrates ontology-aware symbolic query generation, knowledge graph retrieval, and neural language generation to support accurate and explainable threat analysis across information technology and operational technology environments is proposed.
Padmeswari Nandiya, Ahmad Mohsin, Ahmed Ibrahim et al.· 0 citations
Current approaches to explainable AI (XAI) frequently fall short of providing genuinely understandable and trustworthy explanations. These methods often rely on post-hoc interpretations of black-box models, which can be misleading due to their failure to capture the underlying causal mechanisms driving predictions. This paper proposes a novel axiomatic foundation for XAI, arguing that true explanation necessitates a thorough understanding of causal relationships. We posit that causal inference represents a fundamental requirement for explainability, moving beyond superficial feature importance or local linear approximations. This framework introduces a rigorous approach to assessing explanations by evaluating their consistency with known causal structures, leading to more robust and reliable explanations. We define key concepts and provide a formal outline for evaluating explanation methods based on their ability to accurately represent and leverage causal knowledge. The core contribution is establishing a clear criterion – causal fidelity – for evaluating XAI methods, ensuring explanations reflect the true causal drivers of model behavior.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
Agentic, autonomous and explainable AI (XAI) is growing in smart agriculture, but its design, evaluation and integration have not been systematically mapped. Following PRISMA 2020, we searched Web of Science Core Collection, Scopus and IEEE Xplore through 14 June 2026 for English-language peer-reviewed articles and conference papers (2016–2026) implementing agentic/autonomous AI and/or XAI in agricultural systems using sensor, robotic or computer-vision data and reporting an evaluation. Two reviewers independently screened, extracted data and appraised methodological quality using a six-item CASP-style rubric. Results were synthesised descriptively using counts, percentages, cross-tabulations and thematic mapping; meta-analysis was inappropriate because tasks, datasets and metrics were heterogeneous. Of 2255 records, 322 studies were included. XAI dominated (69.6%), whereas autonomous (14.0%) and agentic (12.1%) designs were less common; only 4.3% combined autonomy with explainability. Disease detection was the leading application (33.5%). Most systems remained at the perception/decision-support level (78.9%); 17.4% were field-validated and 4.3% validated explanations agronomically. The evidence reveals an explainability-autonomy divide and substantial field-validation and reproducibility gaps. Explainable-by-design agentic systems require agronomic validation and shared evaluation standards. The review received no external grant funding and was not registered.
Florin Daniel Militaru, Cosmin Alin Popescu, Ramona Ciolac et al.· Agronomy· 0 citations
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This chapter analyses the growing role of artificial intelligence (AI) in the transformation of contemporary enterprises and analyses the transition from fragmented AI-supported processes towards integrated AI-first organisational models. The chapter combines theoretical discussion with an empirical market analysis conducted among 192 enterprises representing various sectors of the economy, including manufacturing, logistics, finance, trade, technology, and service industries. The purpose of the market analysis was to identify the principal areas of AI implementation, evaluate the organisational and strategic impact of intelligent technologies, and assess the preparedness of enterprises for operating under conditions of dynamic digital transformation. The findings indicate that artificial intelligence increasingly functions as a strategic component of enterprise development rather than merely a technological support tool. Organisations most frequently utilised AI in areas related to data analytics, digital marketing, customer service, logistics, and operational optimisation. The implementation of intelligent systems contributed to improving operational efficiency, accelerating strategic decision-making, strengthening organisational adaptability, and supporting innovation processes. Enterprises additionally emphasised the growing significance of predictive analytics, automation, recommendation systems, and AI-supported communication infrastructures in shaping organisational competitiveness and long-term resilience. The market analysis further demonstrates that the level of AI implementation remains strongly dependent on organisational resources, technological infrastructure, and digital competencies. Large enterprises were more likely to adopt advanced AI systems, whereas small and medium-sized organisations more frequently faced barriers related to financial limitations, shortages of qualified specialists, and insufficient technological preparedness. Respondents also identified important challenges associated with AI implementation, including cybersecurity risks, data protection, algorithmic transparency, and ethical governance. The findings presented in this chapter additionally reveal that artificial intelligence increasingly affects organisational culture, managerial practices, and human–technology interaction within enterprises. AI-driven transformation contributes to the emergence of more integrated, data-oriented, and adaptive organisational structures while simultaneously generating new governance-related challenges connected with accountability, explainability, and human oversight. The chapter therefore provides both an empirical and market-oriented perspective complementing the broader theoretical discussions presented throughout the book concerning AI-driven enterprise transformation, integrated AI systems, and the growing role of governance and leadership within AI-first organisational environments.
Ida Skubis, Judyta Kabus, Jolanta Wodarska et al.· 0 citations
Artificial intelligence (AI) is becoming an increasingly visible element of business management, influencing not only operational processes but also strategic planning, financial management, leadership, and organisational governance. This book examines these changes from an interdisciplinary perspective, combining management studies, innovation research, AI ethics, and enterprise decision-making. Rather than presenting artificial intelligence solely as a technological tool, the volume explores its growing role in shaping organisational structures, managerial practices, and new models of leadership. The chapters discuss integrated AI systems, intelligent process automation, predictive analytics, and AI-supported decision-making across strategic, operational, and financial dimensions. Particular attention is devoted to the appearance of humanoid and virtual leaders, including the cases of Mika, the humanoid CEO, and Tang Yu, the virtual CEO. These examples are used to analyse how AI is increasingly embedded within executive and managerial functions, raising important questions about accountability, transparency, human oversight, explainability, and the future role of managers in organisations. The volume additionally includes an empirical market analysis conducted among 192 enterprises representing sectors such as manufacturing, logistics, finance, trade, technology, and services. The findings illustrate how organisations implement AI technologies, what barriers they encounter, and how intelligent systems influence organisational efficiency, strategic adaptability, customer relations, and innovation processes. At the same time, the book addresses broader organisational and societal implications connected with AI adoption, including workforce transformation, algorithmic bias, governance challenges, and changing forms of human–machine collaboration. Combining theoretical discussion with case studies and practical examples, the volume offers a critical and accessible examination of how artificial intelligence is influencing entrepreneurship, enterprise management, and leadership in the digital economy.
Ida Skubis, Judyta Kabus, Jolanta Wodarska et al.· 0 citations
This chapter examines humanoid and virtual AI leaders as a new model of enterprise decision-making, with particular focus on the cases of Mika, the humanoid CEO of Dictador, and Tang Yu, the virtual CEO of NetDragon Websoft. The analysis investigates how artificial intelligence is increasingly integrated into strategic, operational, and managerial processes, moving beyond a traditional decision-support function towards a more active role in organisational governance. The study applies a qualitative case study methodology based on corporate reports, public statements, interviews, and secondary academic literature. The findings indicate that AI-based leaders may enhance operational efficiency, support continuous data analysis, improve forecasting capabilities, and optimise resource allocation. At the same time, the analysis identifies significant organisational and ethical challenges related to transparency, explainability, accountability, bias, and human oversight. The chapter further discusses the implications of AI-driven leadership for managerial roles, team dynamics, and enterprise governance structures. It is argued that the effective implementation of humanoid and virtual leaders requires human-centric governance frameworks that combine the analytical capabilities of AI systems with human responsibility, ethical judgment, and organisational control mechanisms.
Ida Skubis, Judyta Kabus, Jolanta Wodarska et al.· 0 citations
Most studies on vehicular cybersecurity focus on classification accuracy but fail to evaluate the applicability of the models to the edge. This paper proposes a secure edge intelligence framework for V2X/IoV intrusion and misbehavior detection, called TrustEdge-V2X, which is deployment-aware. The framework combines dataset-role qualification, attack taxonomy, AI model benchmarking, feature-budget analysis, deployment ranking based on EdgeScore, offline risk-aware orchestration, external validation, robustness testing, explainable AI, repeated-run statistical analysis and ablation studies. Three datasets are assigned different experimental roles: VeReMi_NextGen is used for core V2X/VANET misbehavior detection, CICIoT2023 is used for supporting edge/IoT intrusion experiments and HCRL_CarHacking is used for external IoV/CAN validation. LightGBM outperformed all other AI models in EdgeScore (0.9383), F1-score (0.9878), MCC (0.9758), and inference latency (0.009419 ms per sample) across all eight AI models and six scenarios on VeReMi_NextGen for binary detection. In five dataset-task cases, the accuracy-best model was different from the EdgeScore-best model, which is the most important point to note: the best model in terms of accuracy is not necessarily the best model in terms of EdgeScore. Compact feature subsets were competitive, and robustness testing demonstrated an average F1 decrease of 0.1423 when tested under stress. The orchestration layer was found to be beneficial for the tasks, but it did not always perform better than the best fixed policy. As a whole, TrustEdge-V2X offers a systematic approach to the assessment and selection of vehicular cybersecurity models based on the operational and deployment conditions, not only on the classification accuracy.
Hesham Sakr, Mina Shenouda, Nadeem Sarwar et al.· Computers· 0 citations
Schizophrenia is a highly heritable psychiatric disorder with a complex genetic basis. While recent GWAS and whole exome sequencing studies have identified numerous risk loci, existing clinical prediction models rely primarily on linear assumptions, limiting their ability to capture complex, nonlinear genetic effects such as epistasis. In this study, we apply end-to-end genome interpretation neural network models to predict schizophrenia risk using whole exome sequencing data in a cohort of 6,135 cases and 6,245 controls. We show that nonlinear neural networks significantly outperform conventional additive models when sufficient sample size is available. These findings support the fact that high-order genetic interactions between alleles and variants should be considered by clinical and quantitative genetics models. To investigate the decision process our models follow, we integrate explainable AI techniques and biological priors into our models, using them to identify predictive genes and pathways. Our approach recovers both known schizophrenia risk genes and recommends BASP1 as a potential understudied schizophrenia gene involved in neuronal development. Existing prediction models for schizophrenia often fail to capture nonlinear genetic effects, like epistasis. Here, the authors show that explainable neural networks improve genetic prediction of schizophrenia by capturing genetic interactions.
This chapter develops the third scenario, the Platform Marketplace model. This is situated at the intersection of market-led coordination and AI-first integration. In this future, digital platforms unbundle higher education into specialized functions including skill acquisition, credential signaling, socialization, and career placement. These are disaggregated across providers and recombined through algorithmic coordination. Universities shift from orchestrators of integrated educational experiences to content or service partners competing within platform-governed ecosystems. This chapter grounds the scenario in transaction cost economics, information asymmetry theory, network effects, and platform ecosystem theory, explaining how reduced coordination costs make market-based disaggregation economically viable. Two mechanisms dominate: systemic substitution through unbundling, as platforms progressively absorb functions historically bundled within institutions; and automation enabling scale, as near-zero marginal delivery costs drive platform consolidation toward winner-take-most dynamics. The 2035 operating model features global learning marketplaces controlled by a small number of technology firms, modular micro-credentials replacing integrated degrees, faculty labor restructured into star creators and gig workers, and governance exercised through algorithmic management. This chapter identifies deep stratification, data extraction, and labor market segmentation as principal equity risks, then concludes with signposts for monitoring movement toward this scenario.
Strategies for Higher Education Institutions: Resilience and Adaptability provides higher education leaders with a structured methodology for strategic decision-making under deep uncertainty. The book constructs four plausible scenarios for higher education to 2035, organized around two critical uncertainties: coordination logic (state-led versus market-led) and human-AI centricity (human-centric versus AI-first). It introduces a five-mechanism typology that explains how AI and emerging technologies reshape institutional value propositions, operating models, and capability sets, moving analysis from individual tools to structural forces. The book then translates scenarios into strategic action through the 7C Strategy Wheel and 7C Strategy Selection Framework, connecting environmental diagnosis to strategic posture selection, initiative classification, and signpost monitoring. The primary readers are senior institutional leaders, including vice-chancellors, presidents, rectors, deans, governing board members, and strategy executives. Policymakers, ecosystem players, and researchers in strategic management and higher education will also find applicable frameworks. The book functions as both an analysis and toolkit. Readers gain methods for stress-testing existing strategies against multiple futures, identifying robust actions that hold value across scenarios, building contingent strategies tied to specific conditions, and establishing signpost-based monitoring systems for strategic recalibration. The scenarios apply globally and are designed for application to specific national, regional, and institutional contexts.
This chapter situates AI myth within the broader landscape of mythological theory by evaluating major theories of myth to determine their alignment and compatibility with AI myth and its implications. Drawing on theories from anthropology, sociology, psychology, theology, and political science, this chapter will argue that prior theories of myth are insufficient to fully describe and explain theistic AI myths. Many notable mythologists from the nineteenth century, for example, viewed religious myths as pre-scientific attempts to explain the physical world, leading twentieth century theorists to decouple religion and science altogether. This tension between religion and science remains particularly acute in the monotheistic West, where the notion of god is often regarded as antithetical to the cosmology established by modern science. AI myth provides a potential resolution to this tension by offering a secular form of religious myth that depicts a scientifically acceptable form of god, succeeding where many other attempts, such as James Lovelock’s Gaia theory, failed. While AI myths are compatible with some aspects of the several historical theories of myth reviewed, this chapter ultimately concludes that AI myth represents a new category of myth that functions to bring the idea of god back to the world in the secular age.
A new method, called CW-Net, translates the reasoning process of an autonomous vehicle’s AI system into understandable concepts that explain its behavior.
