404 Media48%

A Stenographer Submitted AI-Generated Errors in Official Court Transcript, Judge Says 78%

By Samantha Cole62%

7/23/2026, 9:41:29 PM

BS Summary: This article contains 27 faulty reasoning types, including Appeal to Authority, Ambiguity (Equivocation), and Negativity Bias, with Hasty Generalization as the most egregious example at 36.8% saturation with 171 hits. Analysis detected 1,719 faulty-reasoning hits from 465 analyzed words, generating a BS Score of 69.9% and a BS Rank of 78% (4,675 of 21,200 articles). This article is worse (more manipulative) than 78.00% of the article peer group.

A judge caught a court reporter making AI-generated errors in a court transcript, and put stenographers everywhere on notice for their use of AI. 
In a memorandum decision concerning a case about a man who sold drugs to another man who overdosed and died, filed on July 23, Judge Paul Felix wrote in a footnote of the decision that a transcript contained errors that looked a lot like generative AI. 
The footnote was spotted by attorney Rob Freund on X. 
“At one point in the transcript, a motion, presumably made by the State, is attributed to the trial court. 
At another point, an objection, presumably made by Williams, is attributed to the Bailiff. 
At yet another point, the State’s closing argument is attributed to the trial court,” judge Felix wrote. 
“These errors, among others not described herein, complicated but did not substantially impede our review of Williams’s appeal. 
Regardless, we remind the Court Reporter that this court relies on transcripts being true and accurate representations of the transcribed proceedings. 
Based upon the types of errors reviewed, it appears that generative artificial intelligence may have assisted with the preparation of this transcript. 
While AI can improve efficiency and be a productive tool for many professionals, it is incumbent upon those using such systems to proofread and ensure the accuracy of the generated product.” 
In case after case after case, for the last few years, judges have been catching lawyers using AI in court filings. 
These situations are always messy and embarrassing for the attorneys  whose whole job it is to represent their clients to the best of their ability by citing existing case law and legal precedent, which AI routinely fucks up  and judges are becoming more outspoken about their frustrations. 
In May, judges in the Supreme Court of the State of New York Appellate Division laid into several lawyers for more than 20 minutes after accusing one of them of using AI and the others of being too sloppy to catch it; the judges called the entire situation “striking, concerning, disappointing, and saddening.” 
Lawyers, meanwhile, blame paralegals, head colds, and “rushing.” 
But this is the first time a court reporter has been publicly put on notice for not catching AI-generated errors in transcripts, raising the specter of there being errors not just in court filings from attorneys but in the records of official proceedings of a trial. 
There are many apps and companies that offer AI-generated transcripts for court reporting, but stenographers say their expertise as human listeners and skilled transcribers, especially since AI tends to guess instead of pausing to ask for a re-statement or resolve ambiguity before putting it into the court record, is still extremely valuable in the courtroom. 
Confirmation Bias
10.5%
Anchoring Bias
14.6%
Availability Heuristic
19.6%
Representativeness Heuristic
16.6%
Hindsight Bias
4.5%
Overconfidence Bias
4.7%
Framing Effect
6.9%
Loss Aversion
0%
Status Quo Bias
11.8%
Sunk Cost Effect
0%
Optimism Bias
0%
Pessimism Bias
9.9%
Negativity Bias
24.7%
Self-Serving Bias
1.7%
Fundamental Attribution Error
10.5%
Actor-Observer Bias
0%
In-Group Bias
0%
Out-Group Homogeneity Bias
0%
Halo Effect
0%
Horn Effect
0%
Dunning-Kruger Effect
0%
Recency Bias
15.9%
Primacy Effect
11.4%
Blind-Spot Bias
0%
Ad Hominem
0%
Straw Man
0%
Appeal to Authority
25.8%
False Dilemma
0%
Slippery Slope
9.9%
Circular Reasoning
0%
Hasty Generalization
36.8%
Red Herring
0%
Bandwagon
0%
Appeal to Emotion
24.3%
Begging the Question
0%
Post Hoc (False Cause)
0%
Tu Quoque
1.7%
Burden of Proof
0%
Appeal to Nature
6.7%
Composition/Division
0%
Anecdotal
18.1%
No True Scotsman
11.8%
Ambiguity (Equivocation)
25.4%
Gambler’s Fallacy
0%
Middle Ground
0%
Personal Incredulity
0%
Special Pleading
0%
Genetic Fallacy
0%
Unattributed Quote
0%
Quote-first Misdirection
9.9%
Biased Writer Voice
12.9%
Indoctrination
11.2%
Politically Left Leaning Bias
0%
Politically Right Leaning Bias
0%
Attempt to Sell a Product or Service
11.8%

465 words analyzed.

Analysis

Hover over highlighted words in the article to view the associated bias or fallacy analysis.