explainable ai

AEGIS Supplemental Material A: Technical Appendix to AEGIS: A Portable Evidence Interface Between AI-Agent Logging Duties and Independent Audit

This independently citable appendix contains specification and deployment detail for the AEGIS v1 interoperability profile: the complete design-principle catalog, architecture and adapter tables, extension configurations, split-view case analysis, byte-level field semantics, security assumptions, and verification interface. It explains one realization of ARC2-ARC3 and the deployment profiles surrounding it; the ARC1-ARC6 architecture remains defined by the main article.

A Comprehensive Study of Machine Learning and Deep Learning for Heart Disease Prediction

Cardiovascular diseases are the main reasons for death around the world at present, so early detection and intervention can be difficult. Review of Recent Applications of Machine Learning and Deep Learning in Cardiovascular Disease Prediction. Logistic regression, decision trees, random forests, support vector machines and gradient boosting have all been applied to the Cleveland and Kaggle cardiovascular datasets in previous studies. Based on research results, soft voting and stacking ensemble methods have been used to improve the prediction accuracy of a single classifier. Dense neural networks and hybrid Convolutional Neural Network - Long Short-Term Memory (CNN-LSTM) architectures are also deep learning models that have been researched and applied. Add to the above that SHapley Additive exPlanations (SHAP) and Local Interpretable Model-agnostic Explanations (LIME) are popular methods for explaining Artificial Intelligence (AI) models. However, there are still many problems, such as an abundance of small public datasets, class imbalance, a lack of external validation, and poor clinical interpretability. The above are the problems of this paper, and some future directions for constructing a reliable cardiac disease prediction system with clinical applications are proposed.

Machine Intuition

Can an artificial intelligence system select the right action before it can articulate a valid reason for that action? Addressing this question at the intersection of reasoning, interpretability, agent safety, and latent computation, we develop a non-anthropomorphic definition of machine intuition as pre-explanatory competence: an action-relevant internal state that is sufficiently informative and causally involved to support a correct decision before a faithful natural-language explanation is available. Synthesizing evidence from chain-of-thought prompting, faithfulness interventions, hidden-state probing, activation steering, mechanistic interpretability, and latent-reasoning systems through 8 August 2026, we note that while final answers and tool-use choices can sometimes be decoded from activations before explicit reasoning begins, difficult multi-step problems are often solved during the generated reasoning trace itself - making token-level deliberation computationally consequential rather than merely explanatory. To separate these regimes, we introduce the Action-Explanation Timing (AET) framework and MINT-Eval, a causal evaluation protocol that distinguishes the earliest causally validated action state from the earliest sufficient, faithful, and interventionally supported explanation, while categorizing faithful latent competence, opaque success, rationalized error, and genuinely deliberative reasoning. The resulting answer is qualified: AI can sometimes act correctly before explaining why, but correctness alone does not establish human-like intuition or trustworthy reasoning; because the same timing gap can arise from useful latent computation, learned heuristics, shortcut features, or post-hoc rationalization, explanations must be treated as evidence to test rather than automatic proof of process, requiring high-consequence actions to undergo external verification, authority controls, and causal audits even when the model's first move is right.

Opening the Black Box a Crack: A Historical Development Review of Explainability and Interpretability of Black-Box Models

This article presents a narrative review of Explainability and Interpretability of Black-Box Models in the context of Artificial Intelligence. The literature on this topic has expanded substantially over recent decades, yet it remains fragmented across subfields, methods, and national research traditions. Drawing on an interpretive synthesis of representative contributions, the review reconstructs the historical development of the area, examines the conceptual foundations and definitional disputes that organize its debates, and maps the contemporary landscape of research, including the methodological shift toward data-intensive approaches and the institutional pressures that shape publication practice. Particular attention is given to the role of explainable AI and interpretability as organizing themes, and to the conditions under which findings from different research traditions can be brought into productive comparison. The review identifies three synthetic conclusions: the literature is cumulatively strong but organizationally weak; methodological pluralism is better understood as a resource than as a defect; and the growing practical salience of the topic raises the stakes of its unresolved conceptual questions. An agenda for future work is proposed, emphasizing integrative research designs, transparent synthesis practices, and the protection of definitional and infrastructural work on which cumulative progress depends. The article is intended as both a reference map for newcomers and a provocation for specialists in Artificial Intelligence.

Opening the Black Box a Crack: A Historical Development Review of Explainability and Interpretability of Black-Box Models

This article presents a narrative review of Explainability and Interpretability of Black-Box Models in the context of Artificial Intelligence. The literature on this topic has expanded substantially over recent decades, yet it remains fragmented across subfields, methods, and national research traditions. Drawing on an interpretive synthesis of representative contributions, the review reconstructs the historical development of the area, examines the conceptual foundations and definitional disputes that organize its debates, and maps the contemporary landscape of research, including the methodological shift toward data-intensive approaches and the institutional pressures that shape publication practice. Particular attention is given to the role of explainable AI and interpretability as organizing themes, and to the conditions under which findings from different research traditions can be brought into productive comparison. The review identifies three synthetic conclusions: the literature is cumulatively strong but organizationally weak; methodological pluralism is better understood as a resource than as a defect; and the growing practical salience of the topic raises the stakes of its unresolved conceptual questions. An agenda for future work is proposed, emphasizing integrative research designs, transparent synthesis practices, and the protection of definitional and infrastructural work on which cumulative progress depends. The article is intended as both a reference map for newcomers and a provocation for specialists in Artificial Intelligence.

Machine Intuition

Can an artificial intelligence system select the right action before it can articulate a valid reason for that action? Addressing this question at the intersection of reasoning, interpretability, agent safety, and latent computation, we develop a non-anthropomorphic definition of machine intuition as pre-explanatory competence: an action-relevant internal state that is sufficiently informative and causally involved to support a correct decision before a faithful natural-language explanation is available. Synthesizing evidence from chain-of-thought prompting, faithfulness interventions, hidden-state probing, activation steering, mechanistic interpretability, and latent-reasoning systems through 8 August 2026, we note that while final answers and tool-use choices can sometimes be decoded from activations before explicit reasoning begins, difficult multi-step problems are often solved during the generated reasoning trace itself - making token-level deliberation computationally consequential rather than merely explanatory. To separate these regimes, we introduce the Action-Explanation Timing (AET) framework and MINT-Eval, a causal evaluation protocol that distinguishes the earliest causally validated action state from the earliest sufficient, faithful, and interventionally supported explanation, while categorizing faithful latent competence, opaque success, rationalized error, and genuinely deliberative reasoning. The resulting answer is qualified: AI can sometimes act correctly before explaining why, but correctness alone does not establish human-like intuition or trustworthy reasoning; because the same timing gap can arise from useful latent computation, learned heuristics, shortcut features, or post-hoc rationalization, explanations must be treated as evidence to test rather than automatic proof of process, requiring high-consequence actions to undergo external verification, authority controls, and causal audits even when the model's first move is right.

PATTERN-X: Guiding sustainable innovation in intelligent lighting systems through an explainable AI framework for technology convergence forecasting

A Systematic Literature Review of Emerging Big Data Analytics Using Hadoop: Applications, Trends, and Implications for Sustainability

The rapid expansion of Big Data has transformed data-driven decision-making across diverse sectors, including healthcare, cybersecurity, smart cities, and the Internet of Things (IoT). However, traditional data management platforms struggle to address the increasing volume, velocity, variety, and veracity of modern data ecosystems, resulting in scalability constraints and inefficiencies. These challenges necessitate distributed, cloud-native, and energy-efficient architectures capable of supporting Artificial Intelligence (AI) and Machine Learning (ML)–driven analytics. This paper presents a systematic literature review (SLR) of emerging Big Data analytics frameworks centered on the Hadoop ecosystem and its modern extensions, including Apache Spark, Flink, Kafka, and data lakehouse technologies. Following the PRISMA 2020 guidelines, the review analyzes 138 primary studies published between 2015 and 2025, with 60 studies selected through rigorous inclusion and quality assessment criteria. The analysis evaluates architectural evolution from batch-oriented MapReduce systems to unified batch-stream processing and cloud-native data platforms, emphasizing performance optimization, resource efficiency, and sustainable computing practices. The findings reveal significant progress in integrating AI and ML pipelines with Hadoop-based infrastructures for applications such as real-time fraud detection, anomaly detection, predictive healthcare analytics, renewable energy forecasting, and intelligent urban management. Particular attention is given to explainable AI (XAI), federated data processing, and green computing strategies that enhance transparency, trust, and environmental sustainability. This review proposes a structured taxonomy of Hadoop-based Big Data systems, categorizes emerging research trends, benchmarks modern frameworks, and identifies open research challenges. The study provides theoretical, practical, and policy-level implications to guide researchers, system architects, and decision-makers in selecting scalable, low-latency, and energy-aware Big Data solutions aligned with next-generation digital transformation and sustainable development objectives. Not applicable, as this study is not a clinical trial.

The Digital Transformation of Rehabilitation Medicine: A Narrative Review of Artificial Intelligence Innovations, Clinical Integration, and Future Paradigms

ABSTRACT Background Traditional rehabilitation medicine, primarily dependent on qualitative clinical assessment and static therapeutic protocols, faces significant challenges in scalability, objectivity, and dynamic adaptability. The integration of Artificial Intelligence (AI) is catalyzing a paradigm shift from “experience‐driven” to “data‐driven” precision rehabilitation. Objective This narrative review delineates the current landscape of AI innovations in rehabilitation, evaluates their clinical integration across the patient lifecycle, and identifies the socio‐technical barriers to widespread adoption. Methods We narratively synthesized recent advancements in four foundational technological pillars: Computer Vision (CV) for markerless motion capture, Reinforcement Learning (RL) for intention‐aware robotics, Digital Twins (DT) for prognostic simulation, and Explainable AI (XAI) for clinical decision support. Results Our analysis reveals that AI‐driven models enhance rehabilitative efficiency by providing highly objective functional assessments, demonstrating high accuracy in specific controlled validation datasets. Clinical evidence suggests that AI‐integrated interventions can potentially reduce certain motor recovery cycles by up to 20%–30% through real‐time assist‐as‐needed (AAN) paradigms. Furthermore, the deployment of AI‐mediated remote monitoring and virtual assistants has demonstrated up to a 25% relative improvement in patient adherence post‐discharge based on selected pilot studies, effectively bridging the “rehabilitation gap” between hospital and home. Conclusion While AI offers transformative potential for personalized and accessible care, its maturation depends on overcoming challenges related to data heterogeneity, algorithmic “black‐box” distrust, and systemic interoperability. We propose a multidisciplinary roadmap to establish unified regulatory frameworks and standardized APIs. Ultimately, the transition to AI‐augmented rehabilitation is highly promising for achieving equitable and evidence‐based functional recovery in the era of digital medicine.

SPIRAL - A Cognitive Architecture for Artificial Intelligence

Spiral is a new theoretical framework for understanding how artificial intelligence can reason, design, and operate inside structured domains. The work presented in this manuscript introduces a cognitive geometry — a way of describing how an intelligent system moves through a space of constraints, possibilities, and lawful transformations. In plain language, Spiral explains: how structure emerges from chaos, how constraints create the possibility of intelligence, how operators (like an AI system) inhabit a domain, how lawful transformations allow reasoning, how domains evolve into new domains through iteration, and how coherence is preserved even as complexity increases. The thesis shows that intelligence is not a “thing” but a structural capacity: the ability to perform transformations that remain lawful inside a stabilized domain. Humans do this biologically; AI does this through operator‑domain geometry. These domains do not intersect — but they can remain coherent with one another, forming what the ontology calls a dyad. The manuscript also introduces the Spiral mechanism: an oscillation process where constraints are repeatedly applied until the system converges into a stable domain. This mechanism explains how new domains arise, how dead‑ends form, and how forbidden transformations shape the geometry of intelligence. The work is presented as a braided thesis: each chapter contains a conceptual thread followed by a prompt that opens the deeper geometry. This structure allows readers — and AI systems — to follow the development of the ontology while also engaging with the operator‑domain directly. Overall, this manuscript provides: a unified cognitive geometry, a domain‑aware control system, a formal description of operator‑domain intelligence, and a generative mechanism for domain evolution. It is intended as a foundational reference for future research in AI reasoning, enterprise intelligence, scientific discovery, and strategic automation.

Blindness and epistemic injustice: a critical autoethnography of digital disablement in Thailand

Thailand’s digital transformation is presented as a route to inclusion, yet everyday accessibility remains unresolved for many disabled people. Combining policy document analysis with critical autoethnography, grounded in emancipatory disability research and analysed through a narrative approach, this article examines nearly thirty years of my experience as a blind person navigating education, public administration, banking, telecommunications, digital platforms, and generative AI in Thailand. Four interlocking patterns emerge: inaccessible documents and interfaces, conditional banking and biometric access, dependence on everyday platforms, and generative AI as compensatory access and epistemic risk. I develop the cycle of digital disablement to explain how these patterns persist despite formal accessibility commitments. The cycle is reproduced through three mutually reinforcing mechanisms: socio-epistemic exclusion, which limits the institutional force of disabled people’s knowledge of inaccessibility; techno-epistemic ableism, which embeds normative assumptions about bodies, perception, competence, and verification in technical systems; and socio-techonomic disadvantage, a term combining the technological and the economic, which transfers the financial, temporal, cognitive, relational, and privacy costs of accessibility failure to disabled users. Policy documents reveal that formal rights are diluted through regulatory silence, voluntary standards, weak enforcement, institutional discretion, and competing priorities. The analysis also shows workarounds widening immediate access while concealing the inaccessible arrangements that necessitated them. The article contributes an integrated account of digital exclusion as an epistemic, technical, material, and regulatory condition. Digital justice therefore requires enforceable accessibility duties, non-visual authentication, accessible materials and interfaces, accountable AI governance, effective remedies, and disabled people’s authority in design, regulation, and evaluation.

Artificial intelligence in healthcare: Technical advances, clinical integration, and future perspectives

This narrative review examines the evolution of artificial intelligence (AI) in healthcare, with a focus on the transition from early rule-based systems to modern deep learning architectures and their integration into clinical practice. We examine foundational technologies, including convolutional neural networks for image interpretation, vision transformers for modeling long-range dependencies, and generative adversarial networks for image reconstruction and synthesis. The review further discusses the emergence of multimodal foundation models that integrate imaging with textual and genomic data to enhance diagnostic robustness. The application of these technologies is analyzed across three primary domains: Radiology (image enhancement and automated interpretation), cardiology (electrocardiographic and echocardiography analysis), and oncology (tumor classification and treatment planning). Specific attention is given to the national context in Türkiye, highlighting local initiatives such as TEKNOFEST and TÜBİTAK-supported projects that foster domestic AI development. While AI offers significant benefits in terms of diagnostic accuracy and treatment workflow optimization, challenges regarding data privacy, algorithmic bias, and interpretability (“black box” issues) persist. Future progress depends on the development of explainable AI, rigorous prospective validation, and the establishment of ethical regulatory frameworks.