Agustin V. Startari's Blog

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....