AI in Legal Reports: Tracing Evidentiary Force

The increasing adoption of artificial intelligence by police departments and other institutions to generate reports and legal narratives is raising significant questions about the nature of evidence. Traditionally, documents like police reports, insurance claims, and legal statements are based on human observation, testimony, or direct experience. However, AI systems are now creating full written reports from inputs such as body camera audio, officer dictation, and metadata. While these systems offer speed, standardization, and consistency, they fundamentally differ from human authors: they have never directly witnessed the events they describe.

This shift means that language with significant legal weight is being produced without direct human perception. These AI-generated reports often mimic the authoritative tone of legal testimony, including statements of causality, references to evidence, and descriptions of actions. This raises a critical legal and ethical dilemma: can a sentence be considered evidence even if no human actually uttered it, witnessed the event it describes, or reviewed its generation? Disturbingly, the answer is often yes, leading to potentially problematic implications in legal and administrative contexts.

A recent paper, "Predictive Testimony: Compiled Syntax in AI-Generated Police Reports and Judicial Narratives," delves into how these AI systems function as "compiled syntax engines." This means they operate by applying predefined linguistic rules, templates, and grammatical structures to transform raw, often unstructured inputs into polished, legally resonant text. In this process, the paper identifies the introduction of what it terms "operator-conditioned evidence"—subtle choices made by the AI that can significantly alter the perceived authority, certainty, and interpretation of a sentence.

The paper highlights six core "operators" that influence how a report functions as evidence:

• Agent Deletion: Removing the subject performing an action, obscuring who did what.
• Modal Attenuation: Replacing strong claims with weaker, less definitive terms like "may," "could," or "apparently."
• Evidential Frame Insertion: Adding phrases such as "records indicate..." without providing access to the underlying records themselves.
• Temporal Anchoring Shift: Changing the reported time of an event to align with the system's processing time rather than the actual occurrence.
• Serial Nominalization: Transforming dynamic actions into static nouns, which can depersonalize events.
• Quasi Quotation: Making paraphrased statements sound like direct quotes, potentially altering their original intent or context.

Each of these linguistic manipulations can subtly shape how responsibility, certainty, and causality are understood within a report, moving beyond mere description to influence perception.

The implications of these AI-generated reports are profound, as they are actively used in real-world decisions, including arrests, insurance claim denials, and courtroom filings. Often, the process by which these sentences are generated goes unchecked. For instance, a statement like "The subject was detained after commands were issued" lacks crucial details about who issued the commands or what those commands were. Similarly, "System records show the suspect denied involvement" raises questions about the location and verifiability of these "system records" and who actually heard the denial. A phrase such as "There may have been forced entry" blurs the line between probable cause and mere speculation.

Such language can navigate legal systems unchallenged precisely because it "sounds right" and conforms to institutional expectations. However, it may be structurally empty, lacking a clear agent, verifiable sources, or a solid anchor in reality.

Rather than advocating for a ban on AI-generated reports, the paper proposes a more practical solution: making the syntax auditable. It outlines a four-stage pathway to trace the evolution of a report from its raw input to its final form:

1. Input Stream: The initial raw data, such as audio recordings, time logs, or forms.
2. Compilation Log: A record of the system's internal processes and rules used to generate the text.
3. Operator Trace: Identification of which specific linguistic operators were applied and where within the text.
4. Evidentiary Surface: The final, polished report.

This framework would allow institutions to trace how a particular sentence was constructed, identify which operators influenced its phrasing, and assess any resulting evidentiary weaknesses. The paper also suggests a screening test: any clause lacking a known speaker, citing unverifiable sources, or exhibiting shifted time references should be flagged, corrected, or excluded from consideration.

This approach is innovative because it does not attempt to deduce an AI's "intent," which it lacks. Instead, it focuses on the objective structure of the generated language, treating each sentence as an action. If the structure creates the appearance of evidence without the underlying substance, that structure must be rigorously tested. This solution is broadly applicable to lawyers, judges, engineers, and ethicists, and crucially, it does not necessitate dismantling existing automated workflows, as many of the required artifacts—such as logs, prompts, and edit histories—already exist within these systems. The key is to leverage them for enhanced transparency and accountability.