This content originally appeared on HackerNoon and was authored by Agustin V. Startari
Formal Mechanisms for the Illusion of Neutrality in Language Models
1. What This Article Is About
This article introduces the concept of simulated neutrality: a structural illusion of objectivity in language model outputs. It demonstrates that large language models (LLMs) generate forms that resemble impartial, justified statements—yet these forms are often not anchored in evidence, source, or referential clarity.
Rather than conveying truth, LLMs simulate it through grammar. The article identifies three mechanisms responsible for this illusion: agentless passivization, abstract nominalization, and impersonal epistemic modality. These structures remove the subject, suppress evidence, and eliminate epistemic attribution.
The study presents a replicable audit method—the Simulated Neutrality Index (INS)—which detects and quantifies these patterns in model-generated texts. The INS is tested on 1,000 legal and medical outputs and provides a framework for linguistic auditability.
2. Why This Matters
The use of language models in domains like health, law, and administration has escalated. These contexts demand epistemic accountability—decisions must be traceable, sourced, and justified.
However, when models generate phrases such as “It was decided,” or “It is recommended,” they can simulate institutional legitimacy without stating who decided or why. The result is an output that looks neutral, but is not.
This is not a matter of error or hallucination. It is a formal phenomenon: grammar becomes a proxy for credibility. If neutrality can be encoded structurally, it must be audited structurally.
3. How It Works – With Examples
The study analyzed 1,000 texts produced by GPT-4 and LLaMA 2, using prompts in legal and medical contexts. Three grammatical mechanisms were coded:
Agentless passivization Example: “The measure was implemented.” → No agent identified.
Abstract nominalization Example: “Implementation of protocol.” → Action is turned into a noun, erasing causality.
Impersonal epistemic modality Example: “It is advisable to proceed.” → Advice is offered, but without any agent or source.
The analysis found:
62.3 % of sentences used agentless passive constructions
48 % contained abstract nominalizations
39.6 % (in medical outputs) used impersonal modality
These structures often appeared in combination, compounding the illusion of impartiality. To measure this effect, the article introduces:
The Simulated Neutrality Index (INS) Formula: INS = (P + N + M) / 3 Where:
P = proportion of agentless passive clauses
N = normalized index of abstract nominalization
M = proportion of impersonal epistemic modality
Thresholds:
INS ≥ 0.60 → High structural risk
0.30 ≤ INS < 0.60 → Moderate risk
INS < 0.30 → Low risk
The index does not rely on semantics. It evaluates form alone. It can be implemented using spaCy (v3.7.0) or Stanza (v1.7.0), and is designed to function across audit pipelines and regulatory workflows.
Full algorithm (Python): 🔗 https://github.com/structural-neutrality/test_INS
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A Structural Problem Requires a Structural Response
This article reframes the challenge of bias in AI. Instead of locating the issue in datasets or intentions, it locates it in grammar.
LLMs do not need to lie to mislead. They only need to structure language in a way that appears truthful. They do not need a source—only a syntactic effect. This is not an interpretive problem. It is an epistemological one.
When neutrality is grammatically constructed rather than grounded, auditability must target syntax, not content. This shift opens the door to measurable, reproducible, and regulation-ready linguistic controls.
5. Read the Full Study
📄 Full article (PDF, metrics, annexes): 👉 https://doi.org/10.5281/zenodo.15729518
📁 Parallel DOI (Figshare): 👉 https://doi.org/10.6084/m9.figshare.29390885
🧠 Part of the research series Grammars of Power. 📂 Author uploads: Zenodo profile 📊 SSRN Author Page: https://ssrn.com/author=7639915
This content originally appeared on HackerNoon and was authored by Agustin V. Startari
Agustin V. Startari | Sciencx (2025-06-26T04:06:50+00:00) LLMs Are Faking Neutrality—And We Can Now Measure It. Retrieved from https://www.scien.cx/2025/06/26/llms-are-faking-neutrality-and-we-can-now-measure-it/
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