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

LLMs pose unique risks to mental autonomy beyond traditional data privacy: The paper identifies that Large Language Models can threaten "mental integrity through precision semantic suggestion"—meaning they can influence thoughts and decisions in ways that existing regulations like GDPR and the EU AI Act don't adequately address, since these frameworks focus on data protection rather than the meaning-making process itself.

A "neuro-linguistic divide" creates a new form of inequality: The paper warns that as LLMs become semantic intermediaries between neural data and social applications, those who understand and control these systems will have disproportionate power over those who don't, creating a biosemantic inequality similar to the digital divide.

Current regulations are insufficient for this emerging technology: The authors argue that existing regulatory models fail to address the "dynamic, meaning-making processes" of how LLMs interpret and translate neural information, suggesting that policymakers need fundamentally new approaches rather than extensions of current frameworks.

Three core principles should guide new governance: The paper proposes that regulation should be built on Semantic Transparency (understanding how LLMs interpret data), Mental Informed Consent (ensuring people knowingly agree to neural-linguistic processing), and Agency Preservation (protecting human decision-making autonomy).

Practical governance tools are needed now: The authors recommend specific mechanisms like "NLI-specific ethics sandboxes" for testing these systems safely and "bias-aware certification" of LLMs before deployment in sensitive domains like medicine and education.