Gothamist77%

State audit finds gaps in NYC schools' oversight of tech and student data 48%

By Ryan Kost75%

5/9/2026, 8:13:15 PM

BS Summary: This article contains 17 faulty reasoning types, including Representativeness Heuristic, Post Hoc (False Cause), and Hasty Generalization, with Negativity Bias as the most egregious example at 29.4% saturation with 189 hits. Analysis detected 692 faulty-reasoning hits from 642 analyzed words, generating a BS Score of 49% and a BS Rank of 48% (11,518 of 21,886 articles). This article is better (less manipulative) than 52.60% of the article peer group.

New York City’s public schools system struggles to track which technology its schools use, report breaches on time and notify families when student data is compromised, the state comptroller’s office found in a recent report. 
Comptroller Thomas DiNapoli released the audit late last month, about a week before the start of a widespread ransomware breach that ultimately left school districts and colleges around the country without access to the online education platform Canvas. 
New York City schools, Columbia University, Rutgers University and Princeton University were all among the institutions facing outages this week. 
In a statement on Friday, Schools Chancellor Kamar Samuels said the department had recently learned of two data privacy issues. 
One was “globalized,” affecting up to seven schools, an apparent reference to the Canvas breach. 
The other, he said, was localized to one campus. 
Bloomberg cited a memo saying malware had been found on computers at one school community’s shared lab. 
DiNapoli’s audit doesn’t address those incidents. 
It was based on a longer review of the period from March 2020 through September 2025. 
It found the city’s public schools system  which serves roughly 900,000 students across 1,600 schools  does not maintain a comprehensive list of the various applications each school uses and, as a result, does not have a “clear understanding of its environment, the type of information being stored in these applications, and the various risks associated with the data.” 
Auditors reviewed 141 data breaches between January 2023 and February 2025 and found the department delayed reporting nearly half of those breaches to the state, in some cases by more than a year. 
“One of the things that we noted in the report is a lack of a centralized inventory,” said Tina Kim, the deputy comptroller for state government accountability. 
“So, the district is not aware of what specific applications all of the schools are actually using.” 
“And if you think about it, that creates a delay because you don’t have a centralized inventory,” Kim continued. 
“And the reason why inventories are also important is because it allows you to basically do a risk assessment and know if you’re using certain applications that are higher risk, you have to put in certain controls.” 
They also found that school district policy didn’t address some areas related to data security and privacy, or publish related materials on the school system’s website. 
Auditors also said they found “weaknesses in technical controls” used to safeguard student data. 
And they said a quarter of the department’s roughly 161,000 employees did not complete required annual data privacy training in 2024. 
“Historically, when you got a phishing email, there were red flags, there were misspellings,” Kim said. 
“But artificial intelligence can take away those red flags, and with new technology, you can actually do phishing emails at scale.” 
“That’s why training is so important, because artificial intelligence lowers the barrier,” Kim continued. 
“It basically increases the number of people who have access to these tools and makes it a lot easier to actually do.” 
New York City Public Schools didn’t immediately reply to a message from Gothamist seeking comment on Saturday. 
In a written response to the audit, however, Deputy Chancellor of School Operations Kevin Moran said protecting student data “is of the utmost importance” to the department. 
Moran also pointed to a new student privacy webpage and a working group of parents, advocates and school leaders convened in the past year. 
And while Moran pushed back on some of the survey’s methodology, the department accepted most of the comptroller's recommendations, including developing a way to account for all student information systems and drafting a written data classification policy. 
The comptroller's office said it would follow up in a year to check on the district’s progress in implementing its recommendations. 
Article reasoning-pattern comparisonThis article: 3.0%Ryan Kost: 1.5%Gothamist: 2.7%Confirmation Bias3.0%This article: 0.0%Ryan Kost: 2.0%Gothamist: 1.5%Anchoring Bias0.0%This article: 6.4%Ryan Kost: 3.6%Gothamist: 3.6%Availability Heuristic6.4%This article: 11.8%Ryan Kost: 1.0%Gothamist: 1.1%Representativeness Heuristic11.8%This article: 2.3%Ryan Kost: 1.1%Gothamist: 0.7%Hindsight Bias2.3%This article: 0.0%Ryan Kost: 0.8%Gothamist: 1.2%Overconfidence Bias0.0%This article: 0.0%Ryan Kost: 7.1%Gothamist: 8.4%Framing Effect0.0%This article: 0.0%Ryan Kost: 1.5%Gothamist: 1.2%Loss Aversion0.0%This article: 5.8%Ryan Kost: 1.1%Gothamist: 1.1%Status Quo Bias5.8%This article: 0.0%Ryan Kost: 0.0%Gothamist: 0.2%Sunk Cost Effect0.0%This article: 3.3%Ryan Kost: 3.3%Gothamist: 3.6%Optimism Bias3.3%This article: 3.4%Ryan Kost: 1.9%Gothamist: 1.7%Pessimism Bias3.4%This article: 29.4%Ryan Kost: 10.4%Gothamist: 8.5%Negativity Bias29.4%This article: 4.2%Ryan Kost: 2.0%Gothamist: 2.6%Self-Serving Bias4.2%This article: 0.0%Ryan Kost: 0.9%Gothamist: 1.0%Fundamental Attribution Error0.0%This article: 0.0%Ryan Kost: 0.3%Gothamist: 0.3%Actor-Observer Bias0.0%This article: 0.0%Ryan Kost: 1.9%Gothamist: 2.1%In-Group Bias0.0%This article: 0.0%Ryan Kost: 0.5%Gothamist: 0.6%Out-Group Homogeneity Bias0.0%This article: 3.7%Ryan Kost: 2.3%Gothamist: 2.1%Halo Effect3.7%This article: 0.0%Ryan Kost: 0.1%Gothamist: 0.2%Horn Effect0.0%This article: 0.0%Ryan Kost: 0.0%Gothamist: 0.0%Dunning-Kruger Effect0.0%This article: 5.9%Ryan Kost: 1.7%Gothamist: 1.3%Recency Bias5.9%This article: 0.0%Ryan Kost: 0.5%Gothamist: 0.4%Primacy Effect0.0%This article: 0.0%Ryan Kost: 0.1%Gothamist: 0.1%Blind-Spot Bias0.0%This article: 0.0%Ryan Kost: 1.2%Gothamist: 1.0%Ad Hominem0.0%This article: 0.0%Ryan Kost: 0.2%Gothamist: 0.2%Straw Man0.0%This article: 2.6%Ryan Kost: 6.0%Gothamist: 4.4%Appeal to Authority2.6%This article: 0.0%Ryan Kost: 1.1%Gothamist: 1.2%False Dilemma0.0%This article: 0.0%Ryan Kost: 0.4%Gothamist: 0.8%Slippery Slope0.0%This article: 5.8%Ryan Kost: 0.0%Gothamist: 0.1%Circular Reasoning5.8%This article: 6.7%Ryan Kost: 2.6%Gothamist: 3.8%Hasty Generalization6.7%This article: 0.0%Ryan Kost: 0.2%Gothamist: 0.3%Red Herring0.0%This article: 0.0%Ryan Kost: 0.9%Gothamist: 0.8%Bandwagon0.0%This article: 0.0%Ryan Kost: 5.7%Gothamist: 6.4%Appeal to Emotion0.0%This article: 2.2%Ryan Kost: 0.8%Gothamist: 0.7%Begging the Question2.2%This article: 8.9%Ryan Kost: 1.3%Gothamist: 2.4%Post Hoc (False Cause)8.9%This article: 0.0%Ryan Kost: 0.1%Gothamist: 0.1%Tu Quoque0.0%This article: 0.0%Ryan Kost: 0.3%Gothamist: 0.4%Burden of Proof0.0%This article: 0.0%Ryan Kost: 0.2%Gothamist: 0.1%Appeal to Nature0.0%This article: 0.0%Ryan Kost: 0.1%Gothamist: 0.2%Composition/Division0.0%This article: 0.0%Ryan Kost: 3.6%Gothamist: 2.8%Anecdotal0.0%This article: 0.0%Ryan Kost: 0.0%Gothamist: 0.1%No True Scotsman0.0%This article: 2.3%Ryan Kost: 1.5%Gothamist: 1.3%Ambiguity (Equivocation)2.3%This article: 0.0%Ryan Kost: 0.0%Gothamist: 0.0%Gambler’s Fallacy0.0%This article: 0.0%Ryan Kost: 0.1%Gothamist: 0.1%Middle Ground0.0%This article: 0.0%Ryan Kost: 0.0%Gothamist: 0.1%Personal Incredulity0.0%This article: 0.0%Ryan Kost: 0.2%Gothamist: 0.2%Special Pleading0.0%This article: 0.0%Ryan Kost: 0.2%Gothamist: 0.2%Genetic Fallacy0.0%This article: 0.0%Ryan Kost: 0.9%Gothamist: 1.2%Unattributed Quote0.0%This article: 0.0%Ryan Kost: 0.9%Gothamist: 1.0%Quote-first Misdirection0.0%This article: 0.0%Ryan Kost: 2.5%Gothamist: 3.2%Biased Writer Voice0.0%This article: 0.0%Ryan Kost: 2.9%Gothamist: 1.5%Indoctrination0.0%This article: 0.0%Ryan Kost: 0.8%Gothamist: 1.0%Politically Left Leaning Bias0.0%This article: 0.0%Ryan Kost: 0.1%Gothamist: 0.2%Politically Right Leaning Bias0.0%This article: 0.0%Ryan Kost: 2.4%Gothamist: 1.0%Attempt to Sell a Product or S…0.0%

642 words analyzed.

Speakers

5speakers45%attributed speech351writer words
Voice mapSelect a segment to jump to its words
Writer's voice • 13 words • 0.0% coverageWriter's voice • 35 words • 0.0% coverageWriter's voice • 38 words • 0.0% coverageWriter's voice • 20 words • 0.0% coverageKamar Samuels • 20 words • 0.0% coverageWriter's voice • 15 words • 0.0% coverageKamar Samuels • 9 words • 0.0% coverageBloomberg • 17 words • 0.0% coverageWriter's voice • 6 words • 0.0% coverageWriter's voice • 16 words • 0.0% coverageWriter's voice • 60 words • 0.0% coverageWriter's voice • 33 words • 0.0% coverageTina Kim • 27 words • 0.0% coverageTina Kim • 17 words • 0.0% coverageTina Kim • 19 words • 0.0% coverageTina Kim • 37 words • 0.0% coverageWriter's voice • 26 words • 0.0% coverageWriter's voice • 14 words • 0.0% coverageWriter's voice • 21 words • 0.0% coverageTina Kim • 16 words • 0.0% coverageTina Kim • 21 words • 0.0% coverageTina Kim • 14 words • 0.0% coverageTina Kim • 22 words • 0.0% coverageWriter's voice • 17 words • 0.0% coverageKevin Moran • 27 words • 0.0% coverageKevin Moran • 24 words • 0.0% coverageWriter's voice • 37 words • 0.0% coveragecomptroller's office • 21 words • 0.0% coverage
Selected voice

Tina Kim

75%flagged-word coverage
173 attributed words59% of attributed speech85% writer coverage

No manipulation-pattern hits were found in this speaker's attributed words or the writer's voice.

Attribution is sentence-level. Pattern percentages are calculated only from words assigned to that voice.

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Analysis

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