explainable ai

Humanoid and Virtual Leaders as a New Model of Decision-Making in Enterprises

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.

TrustEdge-V2X: Deployment-Aware Edge Intelligence for V2X/IoV Intrusion and Misbehavior Detection

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.

End-to-end deep learning methods for genetic risk prediction of schizophrenia

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.

Scenario 3: The Platform Marketplace Model

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

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.

AI myth as a new category of myth

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.

Explainable hybrid consensus feature selection with VIF-Controlled multicollinearity for Voice-Based diabetes prediction

A multimodal cross-modal explainable retrieval-augmented generation framework for hallucination-grounded visual question answering

Recent multimodal large language models (MLLMs) have achieved remarkable performance on visual question answering (VQA) and multimodal reasoning tasks. But one overlooked failure case stubbornly persists: when answers require simultaneous synthesis of evidence from natural images, free-form text, and structured tables, present models suffer from trimodal hallucination—generating content unattributed to any of the input modalities. Prior approaches to hallucination mitigation focus on image-text pairs, and existing retrieval-augmented generation (RAG) systems for multimodal settings do not readily include supporting evidence and often lack modality-attributed explainability. In this paper, we present MXRAG (Multimodal Cross-Modal Explainable Retrieval-Augmented Generation), a new approach to this trimodal evidence problem with three key innovations that work in concert: (1) a Trimodal Evidence Retriever (TER) that retrieves image patches, text passages, and table rows jointly using a shared semantic manifold; (2) a Cross-Modal Attribution Network (CMAN) that computes fine-grained, token-level attribution scores inline during generation, mapping each generated token to supporting evidence from all three modalities; and (3) a Hallucination-Aware Constrained Decoding (HACD) strategy that penalises generation steps with attribution entropy above a calibrated threshold, suppressing unsupported factual tokens at inference time. CMAN is trained with novel cross-modal attribution and modality-coherence losses using token-level gold annotations; HACD requires no additional training and is calibrated per dataset on the validation split. We cast joint retrieval-generation as a constrained variational problem over a trimodal evidence space and introduce MMTabQA, a new trimodal VQA benchmark derived from WikiTableQuestions and MSCOCO with 12,847 instances and token-level attribution labels. Evaluation on four benchmarks (MMTabQA, WebSRC, ChartQA, MIMIC-CXR-VQA) shows that MXRAG achieves state-of-the-art. +21.4% points (p.p.) exact match accuracy, − 38.6% relative reduction in hallucination rate (− 12.1 p.p. absolute) versus the best multimodal RAG baseline, and 89.4% modality coherence score. Ablation experiments confirm the contribution of each component, with CMAN providing the largest accuracy gain (+ 14.2%) and HACD the largest hallucination reduction (− 23.1%). A supplementary human evaluation on 200 instances confirms that the entropy-based hallucination metric tracks human judgement (human-judged HR: 21.3% vs. metric HR: 19.1%). MXRAG advances the state of the art for reliable, interpretable, evidence-based multimodal AI.

Creating Educational Documentation with AI

T his chapter covers the use of all the artificial intelligences (AIs) explained so far (except Brisk Teaching), to demonstrate how teachers can combine them to create any type of educational document, regardless of its application or subject.

The Gut–Brain Connection and the Influence of the Microbiome on Cognition

Cognitive health is not governed by the brain in isolation; it is actively co-regulated by the gut–brain–microbiome axis. This work argues that microbial composition and function are not passive correlates of brain health but causal drivers of neuroplasticity, neuro-inflammation, and long-term cognitive resilience. Trillions of gut microbes generate neuroactive metabolites, regulate immune signalling, and maintain intestinal barrier integrity. When this system is disrupted, most often by low-fibre diets, chronic stress, poor sleep, or indiscriminate medication use, the inflammatory signalling escalates, neurogenesis declines, and vulnerability to neurodegenerative disease increases. Traditional statistical approaches struggle to capture these effects because microbiome–brain interactions are nonlinear, individualized, and temporally dynamic. Artificial intelligence and machine learning change this landscape. By integrating metagenomic, clinical, and lifestyle data, AI models can move beyond surface-level associations to simulate gut–brain interactions and predict individual responses to dietary, probiotic, and behavioural interventions. Recent deep-learning studies using stool metagenomics to predict Parkinson’s disease risk years before clinical onset illustrate both the promise and urgency of this approach. Yet prediction alone is insufficient. The small cohort sizes, population bias, and weak causal inference limit model reliability. Progress depends on hybrid frameworks that couple machine learning with longitudinal sampling, mechanistic validation, and controlled intervention studies. Precision brain health will not replace foundational lifestyle practices, but it can finally explain why they work and for whom, enabling proactive prevention rather than reactive treatment.

V3 Architecture — Visual Atlas of the Periodic Table: 118 Elements as Vortex Clusters (AI-Generated Illustrations)

V3 ARCHITECTURE — COMPLETE PERIODIC TABLE PACKAGECode + 118 Visual Plates + Geometry of Matter + Nuclear Stability Simulation This deposit contains the complete V3 Architecture package for the periodic table,including: 1. ADA SPARK SOURCE CODE (100% GNATprove proof obligations) - V3_Unified.ads/adb : Complete V3 Architecture with geometry, radiation, and 118 elements - V3_Materials_Classifier.adb : Reclassification of 200 materials by phase - V3_Nuclear_Stability.adb : Kinetic simulation of nuclear stability from stable to rupture - V3_Constants, V3_Geometry, V3_Radiation : Full V3 libraries 2. 118 VISUAL PLATES (AI-Generated Illustrations) - Each element represented as a vortex cluster with: - Protonic vortices (orange-red toroidal rings, R/a = φ) - Surface standing wave (cyan, electron boundary layer) - Available valence sites (white glowing spheres) - Phase locking lines (gold) - Leptonic harmonics (electron, muon, tau) - Multi-angle views: Front, Top, Exploded, Leptonic Harmonics 3. GEOMETRY OF MATTER (PDF) - Complete geometric theory of the periodic table - Stable vortex packings: 1, 2, 6, 8, 10, 12, 14, 16, 18, 20, 24, 28, 32, 36, 50, 54, 82, 86, 126 - Gaps explained as geometric impossibilities - Valence as geometric availability 4. NUCLEAR STABILITY SIMULATION (Code + Thesis PDF) - Nuclear stability as phase equilibrium, not force equilibrium - Φ_critical = -51.1 mV as the universal threshold of matter stability - Fission as phase decoherence, fusion as phase locking - The electron as a surface pressure wave KEY FINDINGS:- The strong force is not a force — it is phase pressure- Neutrons are pressure regulators, not "glue"- Fission is phase decoherence, not nuclear division- Fusion is phase locking, not nuclear collision- Φ_critical = -51.1 mV is the universal threshold of matter- The electron is a surface pressure wave, not a point particle VALIDATION:- 100% proof obligations satisfied by GNATprove- Matches CODATA (proton mass, electron mass, fine-structure constant)- 118 elements simulated, stability predicted correctly- 200 materials reclassified by phase IMPORTANT NOTE ON THE 118 VISUAL PLATES:The 118 visual plates included in this deposit were generated using AI (Gemini)under the supervision of the author. They are illustrative representations ofthe V3 Architecture's geometric interpretation of the periodic table. They arenot experimental data nor computational simulations. Any visual inconsistencies or inaccuracies in the plates reflect the currentcapabilities of AI image generation and do not affect the underlying physicalcalculations. For exact numerical results, stability predictions, and formalproofs, please refer to the Ada SPARK source code, which is formally verifiedby GNATprove at 100% proof obligations. REFERENCE:Ψ_V3 = 48016.8 kg·m⁻² (Zenodo DOI: 10.5281/zenodo.20580979) LICENSE: LPV3 (License for the Protection of V3)COMMERCIAL USE: Requires explicit written permission from the author. AUTHOR:Dr. Benhadid Outail (ORCID: 0009-0003-3057-9543)Independent Researcher, Blida, AlgeriaEmail: mediconsulte@gmail.com

Predicción de la Eficiencia en Teleportación Cuántica en Presencia de Ruido Mediante Aprendizaje Automático e Inteligencia Artificial Explicable

Este repositorio recoge el código desarrollado para mi Trabajo de Fin de Grado (TFG) en Física en la Universidad Europea de Madrid. Este código da soporte a los experimentos y resultados presentados en el mismo. El código está desarrollado en el entorno de Google Colab y utiliza Qiskit y técnicas de Machine Learning para predecir la fidelidad de un estado teleportado en entornos ruidosos, empleando IA Explicable (XAI) para analizar qué tipos de ruido afectan más al protocolo. -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- This repository contains the code developed for my Bachelor's Thesis (TFG) in Physics at the Universidad Europea de Madrid. This code supports the experiments and results presented therein. The code is developed in the Google Colab environment and utilizes Qiskit and Machine Learning techniques to predict the fidelity of a teleported state in noisy environments, employing Explainable AI (XAI) to analyze which types of noise most significantly affect the protocol.