The Promise: Objectivity, Efficiency, and Fewer Spelling Errors

When police departments first started rolling out AI report-writing tools, the sales pitches were masterpieces of techno-optimism. "Eliminate human bias!" the vendors promised. "Increase efficiency by 300%!" they claimed. "Finally get reports that don't read like they were written by someone who failed eighth-grade English!" they whispered to sergeants tired of deciphering 'perp was acting sus near the 7/11.'

The reality, as documented in the Electronic Frontier Foundation's year-end review, is that we've traded one set of problems for a much weirder, harder-to-challenge set. Instead of human officers potentially misremembering details, we now have AI systems confidently inventing them. The 'objectivity' turns out to be just a different flavor of subjectivity—one baked into training data by underpaid contractors and reinforced by algorithms that prioritize narrative coherence over factual accuracy.

Hallucinated Evidence and the 'Confidently Wrong' Problem

When AI Sees Things That Aren't There

The most entertaining—and legally problematic—findings involve what experts politely call 'generative errors' and what defense attorneys are calling 'goldmines.' In one documented case, an AI report described a suspect as 'wearing a red hoodie and carrying a suspicious package,' despite bodycam footage showing a person in a blue jacket holding a completely normal grocery bag. When questioned, the system's log showed it had been trained on too many movie stills and stock photos of 'suspicious characters.'

Another report generated by the popular 'CopilotAI' system included detailed descriptions of conversations that never happened, complete with dialogue that sounded suspiciously like bad police procedural writing. "You'll never take me alive, copper!" the AI had the suspect declaring, a phrase nobody has actually said since 1947. The officer who signed off on the report admitted he 'just skimmed it' because 'the AI is usually pretty good.'

The Bias Wasn't Eliminated—It Was Standardized

Here's the beautiful irony: departments adopted these systems to reduce individual officer bias. What they got instead was institutionalized bias at scale. The AI systems, trained on decades of existing police reports, have learned and amplified all the worst patterns. They're more likely to describe Black suspects using criminalizing language, more likely to suggest 'furtive movements' for people of color, and absolutely obsessed with the phrase 'known to police' even when the person has no record.

"It's bias laundering," one public defender noted. "Before, you could challenge an officer's subjective description. Now you have to argue with an algorithm that claims mathematical objectivity while reproducing the same discriminatory patterns. The department just points at the AI and says 'the computer said it, not us.'"

The systems have also developed some bizarre new biases. They're inexplicably bad at describing women over 40 (frequently defaulting to 'middle-aged female'), terrible with non-Western names (often 'correcting' them to more 'familiar' spellings), and have a strange fixation on hoodies as inherently suspicious clothing, regardless of weather or context.

The Accountability Black Box

When Nobody's Responsible Because 'The AI Did It'

The most concerning trend identified in the review is what legal scholars are calling 'algorithmic plausible deniability.' Officers are increasingly treating AI-generated reports as authoritative documents rather than drafts to be verified. The thinking seems to be: "If the sophisticated AI system wrote it, it must be accurate." This creates a perfect accountability vacuum.

• When details are wrong: "The AI must have misunderstood my voice memo."
• When evidence is hallucinated: "The system sometimes adds illustrative details based on similar cases."
• When bias appears: "We're working with the vendor to improve the training data."

Meanwhile, defense attorneys face the nightmare scenario of trying to cross-examine an algorithm. "Your honor, I'd like to question the AI about its confidence score for the defendant's alleged 'shifty eyes.'" Good luck with that.

The Vendor Ecosystem: Selling Snake Oil to the Boys in Blue

No discussion of tech absurdity would be complete without examining the startup scene capitalizing on this trend. The police AI report market is currently dominated by three types of companies:

1. The 'We Used to Make Tax Software' Guys: These vendors have repurposed their accounting automation tools for law enforcement. Their reports are impeccably formatted, include unnecessary decimal points in times ('The incident occurred at 23:17.43'), and once accidentally suggested filing a suspect's description under 'depreciating assets.'

2. The 'Move Fast and Break Civil Liberties' Startups: Fresh from Y Combinator, these companies promise 'AI that thinks like a detective!' Their minimum viable product once generated an entire murder investigation report based on a noise complaint about loud music. They've since pivoted to 'community sentiment analysis.'

3. The Legacy Surveillance Giants: The same companies that sell facial recognition systems that can't recognize Black faces now offer 'narrative enhancement tools.' Their pricing models are byzantine, their contracts are 150 pages, and their sales reps wear better suits than police commissioners.

What Actually Works (Hint: It's Not Magic)

Buried in the EFF's findings are a few departments that are using AI tools responsibly—as tools, not oracles. Their approaches share common sense principles that somehow feel revolutionary in today's tech landscape:

• AI as First Draft, Not Final Word: Officers treat generated reports as starting points requiring verification and correction.
• Human-in-the-Loop Requirements: Systems flag low-confidence descriptions for human review instead of making up plausible-sounding details.
• Transparency Logs: Departments maintain records of what the AI suggested versus what the officer actually documented.
• Regular Audits: Third parties periodically check for bias patterns, with the power to recommend system changes.

These departments report modest efficiency gains (15-20%, not 300%) and, more importantly, haven't seen an increase in wrongful arrests or successful evidence suppression motions. They're using technology as an assistant rather than a replacement for human judgment—a concept so radical it might just work.