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.
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?
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.
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:
- 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
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?)
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.
The Solution: Make Syntax Testable
Rather than banning AI-generated reports, the paper proposes something smarter: make the syntax auditable.
It introduces a four-stage pathway from messy input to official report:
- 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
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.
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.
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.16689540
Series:Grammars of Power
Author: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: 0009-0001-4714-6539
SSRN Author Page: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.