Mashable77%

Someone stole every Flock camera in this Minnesota city 22%

8/5/2026, 12:49:18 PM

BS Summary: This article contains 22 faulty reasoning types, including Availability Heuristic, Framing Effect, and Pessimism Bias, with Negativity Bias as the most egregious example at 18.3% saturation with 156 hits. Analysis detected 1,150 faulty-reasoning hits from 854 analyzed words, generating a BS Score of 28.2% and a BS Rank of 22% (24,057 of 30,584 articles). This article is better (less manipulative) than 78.70% of the article peer group.

Flock cameras are supposed to help police track cars. 
In Winona, Minnesota, police are now trying to track down the cameras themselves. 
All eight license plate readers operated by the Winona Police Department disappeared sometime between July 29 and Aug. 1, according to police . 
Officers discovered the thefts after one noticed that the system had not recorded any recent activity. 
When police checked the four locations where the devices had been installed, they found the mounting poles cut near the base and discarded in nearby weeds. 
The camera equipment was gone. 
Two flock cameras had been positioned at each of the four locations along highways entering and leaving the city. 
Replacing every unit and its pole will cost approximately $3,000 each, leaving the department with an estimated $24,000 bill. 
Winona is only the latest Minnesota community to find its Flock equipment damaged or missing. 
At least five devices were stolen and another was damaged in Duluth during June and July. 
In Plymouth, one was found in pieces beneath its pole, while another had its power cord cut. 
Similar incidents have also been reported in Edina, Eden Prairie, Minnetonka, and Anoka County. 
What are Flock cameras and why are they so controversial? 
Most of those cases remain unsolved. 
One theft in Faribault, however, resulted in an arrest - prosecutors charged a 42-year-old man after he allegedly removed a reader from a 10-foot pole and brought it to a local shop. 
According to the criminal complaint, the man had previously told an acquaintance that the devices could "track him coming and leaving the city" and allegedly said, "I think I might take one." 
Police later recovered the equipment from his garage with black tape covering its lens. 
That case provides one possible explanation for why someone might target the technology, but Winona police have not connected their thefts to the larger anti-surveillance movement. 
Police Sgt. 
Nicholas Quimby told the Minnesota Star Tribune on Aug. 4 that philosophical opposition and an attempt to sell the components were both motives. 
The fight over Flock is moving beyond Minnesota 
The thefts arrive as Flock Safety faces growing scrutiny over the reach of its nationwide surveillance network. 
Its automated license plate readers photograph passing vehicles and convert the images into searchable records. 
Depending on the device and agency, those records can include a car's plate, location, make, model, color, body type, and distinguishing features such as bumper stickers or roof racks, as Mashable previously reported. 
Police departments say that information can help them locate stolen vehicles, find missing people, and identify cars connected to crimes. 
Flock says approximately 120,000 of its devices are now used in more than 6,000 communities across 49 states. 
But the scale that makes the system useful to police is also what concerns its critics. 
A single reader captures a car at one moment. 
Thousands of them can allow law enforcement agencies to piece together where someone traveled, even if that person is not suspected of committing a crime. 
Questions have also emerged about who can search the information and how widely it can be shared. 
Investigations have found local license plate data being accessed for immigration enforcement , while mounting cases of police misuse have raised concerns about officers searching for people for personal reasons. 
Those concerns are increasingly reaching city halls. 
Since the beginning of 2026, approximately 23 city councils have voted to cancel, reject, not renew, or deactivate Flock systems. 
In Minnesota, Columbia Heights voted in June to end its agreement with the company after residents objected to the technology. 
The debate is spreading elsewhere. 
On Aug. 5, two Connecticut state senators asked cities and towns to pause new installations while the state inventories the readers already in use. 
Their request followed crowded public meetings and calls for greater oversight of how vehicle data is collected and shared. 
At the same time, new allegations are giving residents more reasons to question who has access. 
Two police officers in Ohio recently resigned after internal audits uncovered possible misuse of the city's system. 
Department records indicated that one officer searched the same license plate 53 times over six months, sometimes without listing a related case number. 
Flock says vandalism puts communities at risk 
Flock maintains that its technology gives police time-sensitive information that can help solve crimes and locate people in danger. 
"People have every right to make their voices heard," spokesperson Courtney Terlecki told the Star Tribune . 
She argued that damaging the equipment can eliminate an investigative lead or prevent officers from receiving an alert related to an AMBER Alert, a missing-person case, or a violent crime. 
Civil liberties groups are encouraging opponents to take their concerns to their local governments. 
On Aug. 1, the American Civil Liberties Union launched a national "Get the Flock Out" campaign with instructions for organizing through public records requests, petitions, and city council meetings. 
Winona officials have not said whether the cameras will be replaced. 
But with more communities reconsidering Flock and lawmakers weighing new restrictions, the debate over where these cameras belong is far from over. 
Article reasoning-pattern comparisonThis article: 6.1%Mashable: 1.9%Confirmation Bias6.1%This article: 0.0%Mashable: 1.7%Anchoring Bias0.0%This article: 16.5%Mashable: 2.6%Availability Heuristic16.5%This article: 4.6%Mashable: 0.9%Representativeness Heuristic4.6%This article: 0.0%Mashable: 0.4%Hindsight Bias0.0%This article: 0.0%Mashable: 2.1%Overconfidence Bias0.0%This article: 13.3%Mashable: 4.6%Framing Effect13.3%This article: 2.2%Mashable: 1.2%Loss Aversion2.2%This article: 0.0%Mashable: 0.4%Status Quo Bias0.0%This article: 0.0%Mashable: 0.2%Sunk Cost Effect0.0%This article: 0.0%Mashable: 3.5%Optimism Bias0.0%This article: 8.0%Mashable: 0.9%Pessimism Bias8.0%This article: 18.3%Mashable: 4.5%Negativity Bias18.3%This article: 4.6%Mashable: 0.7%Self-Serving Bias4.6%This article: 5.7%Mashable: 0.4%Fundamental Attribution Error5.7%This article: 0.0%Mashable: 0.1%Actor-Observer Bias0.0%This article: 0.0%Mashable: 0.5%In-Group Bias0.0%This article: 0.0%Mashable: 0.1%Out-Group Homogeneity Bias0.0%This article: 0.0%Mashable: 3.3%Halo Effect0.0%This article: 0.0%Mashable: 0.1%Horn Effect0.0%This article: 0.0%Mashable: 0.0%Dunning-Kruger Effect0.0%This article: 5.6%Mashable: 1.1%Recency Bias5.6%This article: 0.0%Mashable: 0.3%Primacy Effect0.0%This article: 0.0%Mashable: 0.0%Blind-Spot Bias0.0%This article: 0.0%Mashable: 0.2%Ad Hominem0.0%This article: 0.0%Mashable: 0.1%Straw Man0.0%This article: 7.3%Mashable: 2.7%Appeal to Authority7.3%This article: 0.0%Mashable: 1.2%False Dilemma0.0%This article: 0.0%Mashable: 0.4%Slippery Slope0.0%This article: 0.0%Mashable: 0.1%Circular Reasoning0.0%This article: 6.9%Mashable: 4.4%Hasty Generalization6.9%This article: 0.0%Mashable: 0.2%Red Herring0.0%This article: 2.6%Mashable: 1.0%Bandwagon2.6%This article: 0.0%Mashable: 2.6%Appeal to Emotion0.0%This article: 0.0%Mashable: 0.6%Begging the Question0.0%This article: 5.0%Mashable: 1.7%Post Hoc (False Cause)5.0%This article: 0.0%Mashable: 0.0%Tu Quoque0.0%This article: 0.0%Mashable: 0.2%Burden of Proof0.0%This article: 0.0%Mashable: 0.1%Appeal to Nature0.0%This article: 2.9%Mashable: 0.2%Composition/Division2.9%This article: 5.7%Mashable: 2.0%Anecdotal5.7%This article: 0.0%Mashable: 0.0%No True Scotsman0.0%This article: 3.5%Mashable: 1.8%Ambiguity (Equivocation)3.5%This article: 0.0%Mashable: 0.0%Gambler’s Fallacy0.0%This article: 0.0%Mashable: 0.1%Middle Ground0.0%This article: 0.0%Mashable: 0.0%Personal Incredulity0.0%This article: 0.0%Mashable: 0.2%Special Pleading0.0%This article: 0.0%Mashable: 0.0%Genetic Fallacy0.0%This article: 3.7%Mashable: 1.1%Unattributed Quote3.7%This article: 2.0%Mashable: 0.5%Quote-first Misdirection2.0%This article: 3.7%Mashable: 8.3%Biased Writer Voice3.7%This article: 2.0%Mashable: 1.8%Indoctrination2.0%This article: 0.0%Mashable: 0.6%Politically Left Leaning Bias0.0%This article: 0.0%Mashable: 0.1%Politically Right Leaning Bias0.0%This article: 4.3%Mashable: 14.4%Attempt to Sell a Product or S…4.3%

854 words analyzed.

Speakers

2speakers8.2%attributed speech784writer words
Selected voice

courtney Terlecki

100%flagged-word coverage
47 attributed words67% of attributed speech76% writer coverage
0%20.0%40.0%Quote-first Misdirection+36.2 ptsWriter: 0.0%courtney Terlecki: 36.2%36.2%Indoctrination+36.2 ptsWriter: 0.0%courtney Terlecki: 36.2%36.2%Attempt to Sell a Product -4.7 ptsWriter: 4.7%courtney Terlecki: 0.0%0.0%Unattributed Quote-4.1 ptsWriter: 4.1%courtney Terlecki: 0.0%0.0%Biased Writer Voice-4.1 ptsWriter: 4.1%courtney Terlecki: 0.0%0.0%

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

Loading…
Loading…
Loading…
Loading…

Analysis

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