explainable ai

Editorial: Robotics in the performance, safety and learning of surgery - what next?

uptake of generative AI may have influenced health administration, workflow, electronic charting, disease interrogation for information and learning, utility of AI or the notion of autonomous microsurgery is still somewhat remote. 4 Is it thus time to revisit the role of machine advances with digital innovation, AI and automation in robotic system for surgery? And how it influences performance, safety and learning to envision a quantifiable and standardizeable paradigm in surgical care? With principles of industry 4.0 reshaping the world, can OR and surgery be the next frontier …, albeit one that demands a pragmatic, scientific and clinical consideration across surgical subspecialties 4 . The contributions in this journal issue attempt to highlight this evolving landscape.Minimally invasive procedures being a key motivation and driver in robotassisted surgery, Zhiyuan et al., demonstrate through a case-control series using the TiRobot ForcePro Superior system, that the robotic screw implantation was associated with significantly reduced intraoperative blood loss, shortened incision length, alleviated pain, and better recovery of shoulder joint function. Relative to workflow and integration of robotics in the OR, accuracy of screw placement, OR time, length of hospital stay and post-op complications were comparable between robot-assisted and conventional surgery.Challenging the perception that robotic systems lack of flexibility, Fritsch and Overschmidt present an algorithmic framework for real-time configuration to a target pose for a hyper-redundant robotic end-effector. In an era where mathematical modelling and simulation offer close real-world representations, this novel inverse kinematic model suggests a potential pathway towards scalable multi-joint and multipurpose dextrous robotic endeffector.Virtual reality (VR) simulations in robotics continue to be an area of interest, with its importance explored and often established in the learning/training of surgery in riskfree environment. Kawahima and colleagues, use an early non-inferiority of headmounted VR simulation for robot-assisted suturing task, as opposed to conventional console based simulation. With early signal suggesting a faster learning towards proficiency amongst VR simulation group, further work and validation will help establish its significance and potential for efficient training paradigms.In the constrained anatomy of dental procedures, where high-volume care expected, Thieringer et al. explore the utility of digital planning systems tailored to patient-specific problem. While ongoing advances in digital infrastructure of robotic platforms with iterative improvement may enable real-world integration, this work highlights the inherent challenges in translating concept to routine and implications on workflow.In neurosurgery, accurate target localization is fundamental, influencing surgical planning and execution. Using established neuronavigation software within a miniature robotic unit, Stealth AutoGuide TM (Medtronic USA), Bath et al. examine the value of learning curve and workflow optimization in stereotactic biopsy procedures. The findings show promising levels of surgeon-independent accuracy and relatively seamless integration into the OR workflow, including procedural safety for biopsy. These are indicative of continued maturation of procedure-specific robotic units with built in navigation capability.Discussion of surgical robotics would be incomplete without reference to endoscopy, both for its minimal-invasiveness, and the potential for recreating an algorithmic advantage over traditional systems. Through interchangeable articulated robotic end-effectors in endoscopic trans-nasal approach, Dmitrikakis and colleagues overcome limitations of current conventional endoscopic toolset. Although pre-clinical, the work demonstrating improved operative access and surgeon dexterity, marks a viable proposition in extending robotic systems to include endoscopy.While this small collection highlights the work of our peers and robot enthusiasts driving the march of robotics into surgery, these and multiple devices including surgical team, add to the ever expanding data-rich environment of OR -underutilized for digital innovation and seemingly closed door for health data safety and compliance. Drawing inspiration from aerospace industry where machine precision, digital inter-connectivity, quantification, standardization, and automation are deeply embedded, surgery continues to be reliant on human operator. Despite text-book knowledge and prolonged training for proficiency, variability is an unavoidable reality.Robotics and AI offers an opportunity to address this variability. Present day robotics not only lack finesse and flexibility required for microsurgery, but also a structured and integrated digital intelligence necessary to mimic human expertise, experience and judgement. When merged with the memory, high-dimensional computation and predictive algorithms of AI, an ideal human-robot formation is conceivable. Autonomous microsurgery would then be an achievable proposition.For this to be realized, robust and scalable digital infrastructure is imperative, incorporating transparent AI, explainability, traceability and validity of digital signatures within the system. Post-quantum level cybersecurity offer a promise within the digital interplay of machine to machine authentication. 5 At the same time, the rapid release of new algorithms and open-source platforms suggest that increasingly adaptive, agile and interconnected robotic systems are within reach -a necessary equalizer and next disruptor in the OR. When controlled for cost, affordability and scalability, a broader adoption of robotics is inevitable -a paradigm for quantifiable and standardizable surgery, a necessary antidote for human variability.In the complex and dynamic landscape of surgery, robotics, its knowledge and predictive autonomy, may well help level the playing field, while empowering new discovery and innovations in perpetuity.

Generative Systems Theory

Generative Systems Theory is a foundational inquiry and metaphysics concerning systems theory and complexity science. It focuses on how concepts regarded as basic elements—such as nodes, relations, compatibility, attractors, information, and so on—come into being in the first place, and on what grounds they can be said to exist. If we do not simply assume that they already exist, can they instead be derived from fewer premises and more parsimonious conditions? It is also committed to integrating different, scattered domains into a single generative genealogy: how influence generates constraints; how constraints generate interactions and coupling; how uneven influence generates differences in state; how states and constraints generate evolutionary trajectories; how evolutionary trajectories generate compatibility and attractors; how compatibility and attractors serve as preconditions for nodes and systems; how nodes are represented; what hidden coupling conditions lie behind representation; how synchronicity should be explained; what distinguishes a system from a node; whether relations can be divided into fundamentally different types at the most basic level; why some systems possess robustness; into how many types robustness can be further classified; how a form of information that does not depend on bits can be derived and defined purely from systemic logic; how cognitive systems maximize information; what the most fundamental difference is between living systems and other systems; why gene-centered theories in biology are difficult to sustain; what two opposite extremes animals and artificial intelligence occupy, and why humans lie in the intermediate zone; what the core cognitive functions of human beings are besides embodiment; how the most distinctive and difficult-to-articulate human cognitive functions can be connected with neural networks; what common information-theoretic foundation underlies theories such as Archetype, predictive processing, and generative grammar; how that information-theoretic foundation can be used to derive the optimal forms of human–computer interaction and human–machine symbiosis; how modern Pythagoreanism relates to academic institutions and historical change; and, rather than dividing explanations into top-down and bottom-up, what kind of general explanation can come closer to the underlying logic of predictive processing, and so on and so forth. All of these are derived from the foundational theory of Generative Systems Theory. The theoretical extensions beyond the core framework are as follows: Reinterpret “prediction” from a specific cognitive function into a universal mechanism of state-space convergence. Derive the cognitive system’s “sample space” from state-space theory, and use it to provide a unified explanation of memory, prediction, perception, intuition, and archetypes. Derive self-reference paradoxes from node robustness, and transform the problem of self-reference from a logical problem into a problem of generative conditions. Further derive a theory of judgment concerning subjectivity, objectivity, and authenticity from the problem of self-reference. Place logic, reason, and the a priori within an evolutionary genealogy, and propose the concept of “relative first-order status.” Distinguish output diversity from semantic freedom, and propose “cognitive friction” as a metric for evaluating the compatibility of human–AI coupling. Reinterpret the broad problem of AI overfitting through the concept of “natural attractors.” Propose “constraint isomorphism,” freeing understanding from content similarity and representational replication. Reinterpret “structure” from traditional positive-space morphology as negative-space constraints within state space.

AI-driven drug discovery and personalized medicine: integrating quantum computing and next-generation technologies

What the Karpowicz Theorem Does Not Prove: A Three-Resource Theory of the LLM Einstein Test

Two AI systems can reach the same scientific conclusion for different reasons: one may generate the candidate sooner, another may check it more cheaply, and a third may obtain decisive evidence earlier. An end-to-end score records success while hiding which interface made success possible, which resource an intervention changed, and what the result warrants. This paper develops an interface-sensitive three-resource theory for the Einstein Test for large language models. A running laboratory example follows a team choosing between faster candidate generation and earlier access to a distinguishing experiment. Generation effort, computational verification and empirical time form separate coordinates. A finite-budget theorem gives sufficient conditions that connect them: positive target support, consistent witness-producing experiments, bounded screening and complete verification. A complementary theorem composes resource floors for a specified serial procedure. The empirical analysis distinguishes strict refutation from sequential statistical acceptance and allows instruments and experimental opportunities to change the completion frontier. Computational recognition depends on representation: broad recursively axiomatised classes admit undecidability reductions, while suitable real-closed-field representations permit decision procedures. The worked example gives a quantitative success guarantee and shows that generator and instrument improvements alter different costs. Historical cases explain how to choose a data cutoff and acceptance rule without treating an observed discovery interval as a universal lower bound. Publicly deposited on 13 May 2026, the account predates several later 2026 studies that independently foreground these interfaces. The framework states the interfaces required for success, the resource changed by an intervention, and the evidential conclusion supported by the result.

Opening the Black Box a Crack: A Interdisciplinary Mapping 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.

Explainable AI-driven edge–cloud framework for cluster-based predictive cyber threat detection in IIoT-enabled internet of vehicles

A robust and explainable cybersecurity framework for IoV in IIoT Cyber-Physical Systems (CPS) is proposed, in which implementation and validation using the RT-IoT2022 (Real-Time Internet of Things) dataset is performed.

Trusting AI

Abstract Here is a very popular view on what user rational trust in AI requires: the Explanation View of AI Trust, whereby user rational trust in AI requires an explanation of why the AI has reached the conclusion it has. The authors of this chapter think that the Explanation View of AI Trust is wrong. It is not true of trust in general that rational trust (even typically) requires understanding why, and it is not the case that AI communication generates any special normative requirement that there should be an explanation why that grounds rational trust. This doesn’t mean that the authors think there is nothing to be gained by explainable AI (XAI)—they prefer explainability, all else being equal! But understanding how to increase trust (when appropriate) in AI requires the right diagnosis. In order to understand how to increase trust in AI, the authors think it’s better to focus not on AI explainability but instead on AI trustworthiness. That is, in this chapter, they defend what they call the Simple View of AI Trust, whereby user rational trust in AI requires AI trustworthiness.

The Role of E-CRM, AI Chatbot Usage and E-Service Quality on Digital Transformation in Financial Industry

Abstract: Digital transformation has emerged as an important strategy for financial institutions to enhance operational efficiency and enhance customer experience in the digital business environment. This study aims to examine the relationships of Electronic Customer Relationship Management (E-CRM), Artificial Intelligence (AI) chatbot usage, and e-service quality with digital transformation in the financial industry. A quantitative research approach was conducted using data collected from 280 users of digital financial services in Jakarta using purposive sampling. Data analysis was conducted using Partial Least Squares Structural Equation Modeling (PLS-SEM) with SmartPLS 4.0. The results indicate that E-CRM, AI chatbot usage, and e-service quality each have a positive and significant association with digital transformation. E-CRM shows the strongest association with digital transformation among the three examined factors, indicating that effective digital customer relationship management plays a more prominent role in supporting digital transformation than the other examined factors. The proposed model explains 42.4% of the variance in digital transformation (R² = 0.424), indicating a moderate level of explanatory power. This study adds to existing research by integrating E-CRM, AI chatbot usage, and e-service quality into a unified framework for explaining digital transformation in the financial industry. The findings also provide practical implications for financial institutions by emphasizing the importance of strengthening customer relationship management, improving AI-enabled customer services, and maintaining high-quality digital services to support digital transformation initiatives. Keywords: Digital Transformation, E-CRM, AI Chatbot, E-Service Quality, Financial Services

Staged Actions and Provable Human Oversight in Agentic Enterprise AI

Abstract. As enterprise AI moves from answering questions to taking actions, the question of human oversight sharpens. Regulation for higher-risk uses requires that systems be overseen by people who can understand, intervene in and if necessary halt them. In deployed practice this duty is often satisfied by assertion: a policy states that a human is in the loop, and the organisation is asked to trust that the loop was honoured. This paper argues that for regulated use an agent's privileged actions should be staged for human clearance, and, more particularly, that each clearance decision should itself be recorded, so that oversight leaves a trace rather than remaining an operational claim. We describe a stage, clear and seal-before-execution flow in which an agent that proposes a privileged action does not perform it directly: the action is placed in a staging state, presented to an authorised person for a decision, and executed only after that decision has been sealed into a tamper-evident record. Crucially the record captures the decision, who made it, when, and what was shown, not merely the action that followed. We relate this to the human-oversight duties of the EU AI Act and explain how sealing the decision rather than only the action turns oversight from an assurance into evidence. We are candid about the limits: a record shows that a decision was made and shown, not that it was sound, and staging does not by itself defeat inattentive approval.

Human-Centered Educational Leadership for Sustainable Development in the Age of Artificial Intelligence

This chapter develops a human-centered, leadership-driven framework explaining how education contributes to sustainability in AI-augmented societies. It addresses three research questions on leadership as a driver of sustainability, its mechanisms of influence, and its role in employability, inclusion, and resilience. Using a conceptual methodology and literature synthesis, it draws on Human Capital Theory, the Capability Approach, and leadership theories. Findings show that transformational, instructional, and adaptive leadership jointly shape education through ethical resource allocation, curriculum, capacity building, stakeholder engagement, and apprenticeship systems that bridge education and the labor market through experiential learning. These processes enhance capabilities, employability, and inclusion while reducing inequality risks in AI contexts. The chapter concludes that education drives sustainable development only when guided by integrated human-centered leadership, emphasizing leadership development, inclusive governance, and curriculum reform for future-ready systems

Decision delegation to GenAI agents in travel planning: Responsible AI signals, delegation levels, and the transparency paradox

Generative Artificial Intelligence (GenAI) is becoming an integral part of travel planning. This fundamental transformation is changing the definition of travel decision delegation, calling for fresh research into responsible AI in tourism. Drawing on agency theory, trust theory and decision delegation framework, this study conceptualises five responsible AI characteristics (reliability, fairness, trustworthiness, accountability, transparency) as evaluative signals for travellers to assess GenAI across three decision delegation levels, i.e., attribute set, choice set, and final decision. Using a multi-method approach, we analyse data collected from 421 travellers. Findings suggest that reliability and accountability are consistent drivers of delegation across all levels, whereas trustworthiness is a threshold condition that becomes significant at the attribute level. We also found that excessive transparency can lead to cognitive overload and reduce willingness to GenAI decision delegation. Our findings encourage future research into responsible AI development in understanding system complexity, algorithmic explainability and traveller delegation confidence.

White-Box Completeness for Artificial Intelligence

Abstract Current research on explainable and white-box artificial intelligence faces prominent issues: conceptual disarray, divergent perspectives, and a disconnect between theory and practice. The foremost priority is therefore to return to the field’s purest objectives and explore its most fundamental questions. To this end, this paper introduces the axiomatic criterion of white-box completeness (WBC). Specifically, an agent is white-box complete if and only if its behavior can be bidirectionally approximated by human interpretable and manipulable mathematical forms with bounded error. It is a formalization for realizing three ultimate objectives for trustworthy AI: discernible learned knowledge and behaviors, knowledge extraction from AI, and human knowledge injection. Therefore, the ultimate pursuit of explainable AI has been transformed from the vague goal of “making AI interpretable” into a precise mathematical problem. Keywords: White-box completeness; Interpretability; Explainable artificial intelligence; Trustworthy artificial intelligence; Knowledge extraction and injection.