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This article explores how predictive systems displace responsibility by producing authority without subjects. It introduces accountability injection, a three-tier model (human, hybrid, syntactic supervised) that structurally reattaches responsibility. Case studies include the AI Act, DAO governance, credit scoring, admissions, and medical audits, offering a blueprint for legislators and regulators to restore appeal and legitimacy in predictive societies.

Large language models are trained to predict words, not to check facts.They are optimizers of plausibility, not validators of reliability. How AI Tricks Us Into Trusting It

In today’s academic landscape, most generative outputs resemble a recursive plagiarism of lesser-known papers, recycled endlessly without...

Why Neutral AI Texts Still Command You When people think of bureaucracy, they usually picture explicit rules: “You must fill out this...

The Commercial Logic of Law, Finance, and Governance ChatGPT Prioritizes Engagement Over Truth Introduction The new optimizations...

The Problem: Reports Without Witnesses Police reports, insurance narratives, and legal statements are supposed to reflect what someone...

What happens when the law speaks without a speaker?   In regulatory, clinical, and financial domains, language increasingly operates...

A reflection on how artificial language exerts control through structure, not speech 1. What This Article Examines Consider the sentence:...

Big words, passive voice, and clean formatting may look like expertise. In tokenized finance, language models are using syntax to...

Trust, no longer anchored in referents, now emerges from compiled syntax. In the world of tokenized finance, grammar itself is capital....

AI-powered systems are misclassifying corporate expenses, not because they lack data, but because they misread grammar. What looks like a...

1. What the Article Explores The forthcoming paper Expense Coding Syntax: Misclassification in AI-Powered Corporate ERPs  addresses a...

This article investigates how AI-generated crypto whitepapers use persuasive grammar to simulate financial credibility. Through analysis of 10,000 documents and a custom syntactic risk model (DSAD), it demonstrates that sentence structure—not content—is a key predictor of project failure. The paper proposes a regulatory framework called fair-syntax governance to audit and reduce this emerging risk.

Grammar, Law, and Machine Execution What This Article Explains In the shift toward automated legal enforcement, one key transformation is...

AI systems often act before any real understanding takes place. This is not a design footnote; it is a structural risk in law, medicine,...

AI or not AI, that is the question What this article is about ( AI) In Pre-Verbal Command: Syntactic Precedence in LLMs Before Semantic...

Why Legal Systems Fail Against Executable Sovereigns When responsibility disappears and rules execute themselves, the law is left chasing...

Large language models now simulate credible voices without authors or references. This engineered “synthetic ethos” poses epistemic risks in domains like healthcare, law, and education. Based on 1,500 AI-generated texts, the article shows that persuasive fluency is prioritized over traceability, requiring structural responses to detect and regulate credibility without source.

This article demonstrates how ethical judgment can be structurally erased from AI grammars using the deletion rule δ:[E] → ∅. It shows that moral content can be removed at the syntactic level without semantic suppression or loss of computational validity.

Introduces executable power as subjectless authority based on syntactic rules. Empirical analysis of LLMs, TAPs, and smart contracts shows 100% execution under formal triggers. Marks a break from narrative models of power toward structural operativity.