The Problem: Reports Without Witnesses Police reports, insurance narratives, and legal statements are supposed to reflect what someone saw, heard, or experienced. Traditionally, these documents were written by human officers, based on direct observation or testimony. But that model is changing. Across the United States and beyond, police departments are adopting artificial intelligence systems that generate full written reports based on audio from bodycams, officer dictation, or metadata. These systems are fast, standardized, and consistent. But there's a catch: they have never witnessed the events they describe. they have never witnessed the events they describe This means that language is being produced without perception. And yet, the reports sound like legal testimony. They include authoritative statements, causality, references to evidence, and descriptions of actions. So, the big question becomes: Can a sentence be treated as evidence even if no human actually said it, witnessed it, or reviewed it? Can a sentence be treated as evidence even if no human actually said it, witnessed it, or reviewed it? The answer, disturbingly, is often yes. What the Paper Shows The article "Predictive Testimony: Compiled Syntax in AI-Generated Police Reports and Judicial Narratives" examines how these AI systems function as compiled syntax engines. That means they follow preset linguistic rules—like templates and grammars—to turn messy inputs into clean, legal-sounding text. Predictive Testimony: Compiled Syntax in AI-Generated Police Reports and Judicial Narratives compiled syntax engines But in doing so, they introduce what the paper calls operator-conditioned evidence. These are choices the AI makes that change how a sentence sounds, how much authority it carries, and how courts interpret it. Six core operators were identified: operator-conditioned evidence Agent Deletion: removing who did what
Modal Attenuation: replacing strong claims with "may," "could," or "apparently"
Evidential Frame Insertion: adding phrases like "records indicate..." without showing those records
Temporal Anchoring Shift: changing event time to system time
Serial Nominalization: turning actions into static nouns
Quasi Quotation: making paraphrases sound like direct quotes Agent Deletion: removing who did what Agent Deletion Modal Attenuation: replacing strong claims with "may," "could," or "apparently" Modal Attenuation Evidential Frame Insertion: adding phrases like "records indicate..." without showing those records Evidential Frame Insertion Temporal Anchoring Shift: changing event time to system time Temporal Anchoring Shift Serial Nominalization: turning actions into static nouns Serial Nominalization Quasi Quotation: making paraphrases sound like direct quotes Quasi Quotation Each of these operators changes how a report functions as evidence. They don’t just describe what happened—they shape how responsibility, certainty, and causality are perceived. Why It Matters These reports are being used in real decisions: arrests, insurance denials, courtroom filings. And often, no one checks how the sentences were generated. For example: "The subject was detained after commands were issued" (Who gave the commands? What commands?)
"System records show the suspect denied involvement" (Where are these records? Who heard the denial?)
"There may have been forced entry" (Is that probable cause or just hedging?) "The subject was detained after commands were issued" (Who gave the commands? What commands?) "The subject was detained after commands were issued" "System records show the suspect denied involvement" (Where are these records? Who heard the denial?) "System records show the suspect denied involvement" "There may have been forced entry" (Is that probable cause or just hedging?) "There may have been forced entry" This kind of language can move through legal systems unchecked because it sounds right. It fits institutional expectations. But it may be structurally empty—no agent, no verification, no anchor in reality. sounds right no agent, no verification, no anchor in reality The Solution: Make Syntax Testable Rather than banning AI-generated reports, the paper proposes something smarter: make the syntax auditable. make the syntax auditable. It introduces a four-stage pathway from messy input to official report: four-stage pathway Input Stream: audio, time logs, forms
Compilation Log: what the system used to write
Operator Trace: which operators were used where
Evidentiary Surface: the final report Input Stream: audio, time logs, forms Input Stream Compilation Log: what the system used to write Compilation Log Operator Trace: which operators were used where Operator Trace Evidentiary Surface: the final report Evidentiary Surface With this setup, institutions can trace how a sentence was formed, what operator influenced it, and whether it has any evidentiary weaknesses. The paper also proposes a screening test: if a clause has no known speaker, cites unverifiable sources, and shifts time references, it should be flagged, corrected, or excluded. screening test Why It’s Innovative This approach doesn’t rely on guessing the AI’s "intent"—because it has none. It looks at structure. It treats the sentence as an action. If the structure creates the appearance of evidence without the substance, that structure must be tested. structure It’s a solution that lawyers, judges, engineers, and ethicists can all apply. And it does not require dismantling automated workflows. Most of the artifacts—logs, prompts, hashes, edits—already exist. We just have to use them. Read the Full Paper 📄 DOI: https://doi.org/10.5281/zenodo.16689540Series:Grammars of PowerAuthor:Agustin V. Startari https://doi.org/10.5281/zenodo.16689540 Grammars of Power Agustin V. Startari About the Author Agustin V. Startari is a linguistic theorist and researcher in historical studies. His work focuses on syntactic authority, automation of legal discourse, and the structural limits of machine-generated language. ORCID: 0009-0001-4714-6539SSRN Author Page:Link 0009-0001-4714-6539 Link Author Ethos I do not use artificial intelligence to write what I don’t know. I use it to challenge what I do. I write to reclaim the voice in an age of automated neutrality. My work is not outsourced. It is authored. I do not use artificial intelligence to write what I don’t know. I use it to challenge what I do. I write to reclaim the voice in an age of automated neutrality. My work is not outsourced. It is authored.

Ethos

ChatGPT’s Secret Priority: Keeping You Hooked Over Being Right

What is Lost When Legal Grammar Runs Without Judgment?

ORCID

SSRN Author Page

Zenodo

Personal Blog

Figshare

Medium

Hcommons

Scholar

Research Gate

Academia

Web of Science

Read My Stories

Too Long; Didn't Read

Make resilience your competitive advantage

How to Trace and Audit Evidentiary Force in Machine-drafted Text

How to Trace and Audit Evidentiary Force in Machine-drafted Text

About Author

Comments

TOPICS

THIS ARTICLE WAS FEATURED IN

Related Stories

Untitled Story

AI’s Habit of Repackaging Ideas Without Credit

The Noonification: How Often Do NFTs Pass The Howey Test? (1/13/2023)

Darwin's Hybrid Intelligence to Align AI & Human Goals for Startups & VCs

The Noonification: White Man (11/26/2022)

The Noonification: The Metaverse is a Sh*tshow (11/2/2022)

AI’s Habit of Repackaging Ideas Without Credit

The Noonification: How Often Do NFTs Pass The Howey Test? (1/13/2023)

Darwin's Hybrid Intelligence to Align AI & Human Goals for Startups & VCs

The Noonification: White Man (11/26/2022)

The Noonification: The Metaverse is a Sh*tshow (11/2/2022)

Light-Mode

Classic

Newspaper

Dark-Mode

Neon Noir

Minty

HN StartUps