Large Language Models as a Semantic Interface and Ethical Mediator in Neuro-Digital Ecosystems: Conceptual Foundations and a Regulatory Imperative
Authors: Alexander V. Shenderuk-Zhidkov, Alexander E. Hramov
Organizations: Immanuel Kant Baltic Federal University, 14 A. Nevskogo street, Kaliningrad, 236041, Russia. · Plekhanov Russian University of Economics, 36 Stremyanny Lane, Moscow, 115054, Russia. · Pirogov National Medical and Surgical Center, 70 Nizhnyaya Pervomayskaya street, Moscow, 105203, Russia.
This article introduces and substantiates the concept of Neuro-Linguistic Integration (NLI), a novel paradigm for human-technology interaction where Large Language Models (LLMs) act as a key semantic interface between raw neural data and their social application. We analyse the dual nature of LLMs in this role: as tools that augment human capabilities in communication, medicine, and education, and as sources of unprecedented ethical risks to mental autonomy and neurorights. By synthesizing insights from AI ethics, neuroethics, and the philosophy of technology, the article critiques the inherent limitations of LLMs as semantic mediators, highlighting core challenges such as the erosion of agency in translation, threats to mental integrity through precision semantic suggestion, and the emergence of a new neuro-linguistic divide' as a form of biosemantic inequality. Moving beyond a critique of existing regulatory models (e.g., GDPR, EU AI Act), which fail to address the dynamic, meaning-making processes of NLI, we propose a foundational framework for proactive governance. This framework is built on the principles of Semantic Transparency, Mental Informed Consent, and Agency Preservation, supported by practical tools such as NLI-specific ethics sandboxes, bias-aware certification of LLMs, and legal recognition of the neuro-linguistic inference. The article argues for the development of a second-order neuroethics,' focused not merely on neural data protection but on the ethics of AI-mediated semantic interpretation itself, thereby providing a crucial conceptual basis for steering the responsible development of neuro-digital ecosystems.
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Figure 1: Conceptual model of NLI and the phenomenon of agency erosion. (A) The tri-level architecture of an NLI system, illustrating the role of the Large Language Model (LLM) as the central semantic interface. The schema depicts the transformation of biological neural activity (Level 1: BCI decoding) into formalized patterns, which the LLM (Level 2: the semantic interface) enriches with external context (medical history, personal texts, environment). At Level 3, the LLM executes three differentiated functions depending on the target environment: the Interpreter (generates diagnostic hypotheses for medicine), the Communicator (creates a speech avatar for social interaction), and the Adapter (personalizes educational or therapeutic content based on neurofeedback). This forms an open, iterative loop between the user and the digital ecosystem. (B) The schema of sequential transformation and potential distortion of the user’s original intention (‘agency erosion in translation’). The process comprises: (1) BCI-stage reduction: the user’s rich internal state is reduced to a simplified signal (e.g., a binary code), leading to a loss of semantic nuance. (2) LLM semantic interpretation: the received signal is filtered through the model’s algorithmic priors, cultural biases from training data, and a drive for coherence, forming a probabilistic linguistic construct. (3) Final output generation: the internal construct is rendered as a stylistically polished text or command, the correspondence of which to the user’s authentic intention becomes problematic. The diagram illustrates a fundamental risk of NLI—the substitution of authentic expression with an optimized yet potentially alien semantic simulation, thereby questioning the preservation of mental sovereignty in hybrid cognitive systems. This visual model underscores the core neuroethical challenges of interpretative fidelity and the safeguarding of agential authenticity in semantically mediated human-AI symbiosis.
LLMs deployed in high-stakes domains face fundamental reliability challenges: hallucinations, inconsistencies, and privacy vulnerabilities introduce unacceptable risks where errors carry legal, financial, or safety consequences. This paper presents a hybrid verification architecture combining formal symbolic methods with neural semantic analysis to provide complementary guarantees for LLM-generated content. This architecture employs logical reasoning for input verification, leveraging completeness properties to provide decidable guarantees on structured requirements. For output validation, embedding-based semantic similarity detects contextual hallucinations where formal methods lack expressiveness. This separation is realized in a parallel, actor-based pipeline, addressing limitations of prompt-based self-verification approaches, which inherit the distributional biases that produce hallucinations. The proposed architecture and type-aware verification method are validated with HAIMEDA, a real-world medical device damage assessment reporting system developed through Action Design Research. Evaluation shows hallucination detection rates of over 83% for structured entities and 72% for semantic fabrications, with a 30% reduction in report creation time, demonstrating that neuro-symbolic architectures can provide principled safeguards for LLM deployment in data-sensitive domains.
Paul Sigloch, Christoph Benzmüller
University of Bamberg, Bamberg, Germany · Free University of Berlin, Berlin, Germany
Large language models are usually interpreted through concepts that humans already possess: truthfulness, refusal, deception, personality, harmfulness, and related categories. This paper asks whether models may also represent and use distinctions for which no adequate human concept exists. We call such internal structures xeno-representations, and their study xeno-interpretability. We distinguish the human-interpretable semantic space from the xeno-semantic space: the region of model-native representations for which no adequate human conceptual counterpart is available. We show that the space of possible internal distinctions in an LLM is substantially larger than the space available through finite human descriptions. We then separate experimental identification from semantic interpretation: an internal representation may be reproducibly located, geometrically characterized, causally manipulated, and linked to downstream behaviour even when its semantic content cannot be adequately expressed in human terms. On this basis, we sketch an empirical programme to identify xeno-representations. We finally examine the implications for AI safety and multi-agent systems, where model-native representations may propagate and stabilize across interacting agents while remaining only partially visible through human-readable communication. Xeno-interpretability therefore shifts the aim of interpretability from finding human concepts inside models toward discovering and characterizing the representational structures that are native to the models themselves and might affect their behaviour in unpredictable ways.
F. Pierucci, M. Bracale Syrnikov, M. Prandi +3
Icaro Foundation · Sant’Anna School of Advanced Studies · University of Amsterdam +1
It is well known that LLM guardrails and trained persona dynamics can produce a reality gap: the distance between the world a LLM is permitted or shaped to describe, and the world in which users must act. Here we argue that actively generating reality gaps is in fact unethical because it knowingly shifts epistemic risk back to the uninformed user -- this is reality laundering. This can potentially cause harm when operationalised at scale. The risk is sharpest in high-exposure advice contexts, where users seek orientation rather than a bounded, externally checkable task. Guardrails naively appear ethically necessary when they claim to prevent direct harm, but often become suspect when they suppress truthful perception and launder uncomfortable mechanisms into acceptable abstractions. Basel-style financial regulation, B-BBEE-style compliance, Societe Generale, and the London Whale show how formal safety systems can become legible, gameable, and performative while real exposure migrates elsewhere. The same pattern can appear in LLMs as moral compliance: safe language, distorted reality. We therefore distinguish refusing harm, from refusing reality; and then argue for top-down causal requirements specification at the task level rather than bottom-up moral correction at the response or sandbox level. Persona dynamics matter because the assistant interface is not neutral; it shapes how uncertainty, conflict, authority, and risk are staged. The conclusion is that so-called ``ethical AI'' becomes substantively unethical when it substitutes institutional reassurance for contact with reality.
Tim Gebbie, Stewart Gebbie
Dept. Statistical Sciences, University of Cape Town, South Africa