Large Language Models as a Semantic Interface and Ethical Mediator in Neuro-Digital Ecosystems: Conceptual Foundations and a Regulatory Imperative

Abstract

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.

Figures (1)

• 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.