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

2026-03-19 · Source: cs.NE updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Neuroethics, AI Governance & Regulation · Depth: Expert, extended

Summary

This article introduces Neuro-Linguistic Integration (NLI), a new paradigm where Large Language Models (LLMs) serve as a semantic interface between raw neural data and its social application. It explores the dual nature of LLMs in this role, highlighting their potential to augment human capabilities in communication, medicine, and education, while also posing significant ethical risks to mental autonomy and neurorights. The authors critique LLMs' limitations as semantic mediators, identifying challenges like agency erosion, threats to mental integrity through precision semantic suggestion, and a new "neuro-linguistic divide." The article proposes a regulatory framework based on Semantic Transparency, Mental Informed Consent, and Agency Preservation, supported by NLI-specific ethics sandboxes, bias-aware LLM certification, and legal recognition of neuro-linguistic inference. This framework aims to establish a "second-order neuroethics" focused on AI-mediated semantic interpretation.

Key takeaway

For AI scientists and research scientists developing neuro-digital ecosystems, you must prioritize ethical considerations beyond traditional data protection. Focus on implementing Semantic Transparency, Mental Informed Consent, and Agency Preservation in NLI systems to safeguard user mental integrity. Your designs should include mechanisms for users to distinguish their input from AI-generated content and to veto or edit AI outputs, ensuring human sovereignty in hybrid cognitive systems.

Key insights

LLMs mediating neural data create a new ethical landscape requiring novel governance to protect mental sovereignty.

Principles

• Semantic Transparency is crucial for LLM-mediated neural data.
• Mental Informed Consent must be procedural and ongoing.
• Agency Preservation requires user control over AI-generated output.

Method

The study employs a comprehensive interdisciplinary methodology, synthesizing philosophical-ethical analysis, conceptual modeling, comparative analysis of regulatory approaches (GDPR, EU AI Act, US, China), and a critical literature review across AI ethics, neuroethics, and philosophy of technology.

In practice

• Implement NLI-specific ethics sandboxes for impact assessment.
• Mandate certification for LLMs interpreting neural data.
• Legally recognize neuro-linguistic inference as a distinct data class.

Topics

• Neuro-Linguistic Integration
• Large Language Models
• Neuroethics
• Semantic Mediation
• AI Governance

Best for: AI Scientist, Research Scientist, AI Ethicist, AI Researcher, Policy Maker

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.NE updates on arXiv.org.