Police Keep Losing Their Jobs For Using Flock Cameras To Stalk People 65%

By Sophie Hurwitz63%

7/18/2026, 4:59:24 PM

BS Summary: This article contains 29 faulty reasoning types, including Hasty Generalization, Anecdotal, and Appeal to Authority, with Negativity Bias as the most egregious example at 29.7% saturation with 186 hits. Analysis detected 1,448 faulty-reasoning hits from 627 analyzed words, generating a BS Score of 59.3% and a BS Rank of 65% (7,709 of 21,886 articles). This article is worse (more manipulative) than 64.80% of the article peer group.

Flock cameras —solar-powered, automated license plate readers, weighing less than 3 pounds and designed to be unobtrusive—can be found on 80,000 street corners throughout the country. 
The company behind the cameras built an $8.3 billion business empowering officers to track people’s movements. 
Nationwide, Flock cameras log billions of license plates each month. 
And now dozens of reports are emerging of police using the cameras not to solve crimes, but to stalk their partners and exes. 
Over a two-month period in 2025, Milwaukee police officer Josue Ayala searched the license plate number of someone he was dating over 200 times, according to court documents . 
He also searched for his partner’s ex. 
Another Milwaukee officer, Tehrangi Chapman, was assigned to investigate Ayala’s case. 
Ayala was charged with misconduct, resigned , and was sentenced to one year’s probation. 
Then, this week, Chapman too was charged with “misuse of GPS information.” 
While investigating Ayala, he allegedly engaged in the exact same misconduct , using the technology to track people in his own life. 
The Institute for Justice, a libertarian public-interest law firm, identified at least 24 similar cases nationwide of officers using automated license-plate reader (ALPR) cameras like Flock to stalk romantic interests over the past two years. 
Nearly all of those officers were criminally charged and lost their jobs. 
This month alone, at least six new cases were reported in local media outlets. 
Beyond Chapman’s case in Wisconsin, officers in Illinois , South Carolina , Texas , California , and Georgia all lost their jobs due to alleged misuse of Flock cameras. 
Chad Marlow, senior policy counsel at the ACLU, has been following technology and privacy issues for over a decade. 
“The tracking of an individual vehicle, as it moves throughout an area, can reveal very deeply personal and private information, not only about the vehicle but the person operating it,” Marlow said. 
“That is Flock at its most dangerous.” 
(When reached for comment, a Flock Safety representative sent press releases about its audit assistance tool .) 
Six years ago, Mother Jones’ Daniel Moattar detailed how some of California’s most populous counties were collecting information using ALPRs —mostly tracking people who weren’t even under suspicion of any crime. 
And in 2013, the ACLU said that just 47 of every million plates scanned by Maryland ALPRs that year “were even tentatively associated with actual serious crimes.” 
But in the years since then, police use of ALPRs has increased across the country. 
The cameras have been used to go after immigrants without warrants , and to track people seeking abortions as they travel across state lines for the procedure. 
Though there are several companies that make ALPRs, Flock is by far the largest, with more than 80,000 cameras spread throughout the United States. 
Flock Safety’s CEO, Garrett Langley, has said he wants his cameras “on every corner.” 
But the backlash against Flock and other ALPRs is growing . 
The technology, activists say, holds potential for misuse. 
And even when it’s used as intended, some researchers say the company’s data sharing practices lead to privacy rights violations. 
Throughout 2025, at least 30 municipalities canceled their Flock contracts. 
Grassroots groups such as DeFlock have built maps showing the public where these license-plate readers are located. 
(Langley called DeFlock and other activists “terroristic” last year, and apologized this week. 
) 
And Marlow of the ACLU expects the pushback to keep getting louder. 
“In these incredibly divisive political times, we’re actually seeing the rare issue that unites Americans: opposition to government surveillance,” Marlow said. 
“And I don’t think it’s going anywhere. 
I think this movement is only going to grow.” 
This article has been updated to include comment from a Flock Safety representative. 
Article reasoning-pattern comparisonThis article: 13.7%Sophie Hurwitz: 6.2%Mother Jones: 4.0%Confirmation Bias13.7%This article: 3.8%Sophie Hurwitz: 1.1%Mother Jones: 0.7%Anchoring Bias3.8%This article: 12.0%Sophie Hurwitz: 4.7%Mother Jones: 3.3%Availability Heuristic12.0%This article: 0.0%Sophie Hurwitz: 1.0%Mother Jones: 0.8%Representativeness Heuristic0.0%This article: 0.0%Sophie Hurwitz: 0.8%Mother Jones: 0.8%Hindsight Bias0.0%This article: 0.0%Sophie Hurwitz: 0.9%Mother Jones: 1.2%Overconfidence Bias0.0%This article: 5.6%Sophie Hurwitz: 9.4%Mother Jones: 7.2%Framing Effect5.6%This article: 0.0%Sophie Hurwitz: 0.8%Mother Jones: 0.6%Loss Aversion0.0%This article: 2.4%Sophie Hurwitz: 0.4%Mother Jones: 0.6%Status Quo Bias2.4%This article: 0.0%Sophie Hurwitz: 0.0%Mother Jones: 0.1%Sunk Cost Effect0.0%This article: 1.9%Sophie Hurwitz: 1.4%Mother Jones: 1.6%Optimism Bias1.9%This article: 6.9%Sophie Hurwitz: 1.5%Mother Jones: 1.9%Pessimism Bias6.9%This article: 29.7%Sophie Hurwitz: 13.0%Mother Jones: 9.6%Negativity Bias29.7%This article: 0.0%Sophie Hurwitz: 1.4%Mother Jones: 0.8%Self-Serving Bias0.0%This article: 0.0%Sophie Hurwitz: 1.7%Mother Jones: 1.0%Fundamental Attribution Error0.0%This article: 0.0%Sophie Hurwitz: 0.1%Mother Jones: 0.2%Actor-Observer Bias0.0%This article: 1.3%Sophie Hurwitz: 2.5%Mother Jones: 1.0%In-Group Bias1.3%This article: 0.0%Sophie Hurwitz: 1.4%Mother Jones: 0.5%Out-Group Homogeneity Bias0.0%This article: 0.0%Sophie Hurwitz: 1.0%Mother Jones: 1.5%Halo Effect0.0%This article: 0.0%Sophie Hurwitz: 0.8%Mother Jones: 0.3%Horn Effect0.0%This article: 0.0%Sophie Hurwitz: 0.1%Mother Jones: 0.0%Dunning-Kruger Effect0.0%This article: 11.5%Sophie Hurwitz: 2.0%Mother Jones: 1.1%Recency Bias11.5%This article: 8.0%Sophie Hurwitz: 0.7%Mother Jones: 0.3%Primacy Effect8.0%This article: 0.0%Sophie Hurwitz: 0.0%Mother Jones: 0.0%Blind-Spot Bias0.0%This article: 2.1%Sophie Hurwitz: 2.6%Mother Jones: 1.9%Ad Hominem2.1%This article: 0.0%Sophie Hurwitz: 1.6%Mother Jones: 0.6%Straw Man0.0%This article: 16.1%Sophie Hurwitz: 3.1%Mother Jones: 3.6%Appeal to Authority16.1%This article: 4.8%Sophie Hurwitz: 1.2%Mother Jones: 1.4%False Dilemma4.8%This article: 0.0%Sophie Hurwitz: 0.6%Mother Jones: 1.7%Slippery Slope0.0%This article: 0.0%Sophie Hurwitz: 0.0%Mother Jones: 0.1%Circular Reasoning0.0%This article: 25.8%Sophie Hurwitz: 7.9%Mother Jones: 4.7%Hasty Generalization25.8%This article: 2.7%Sophie Hurwitz: 0.8%Mother Jones: 0.2%Red Herring2.7%This article: 3.3%Sophie Hurwitz: 0.6%Mother Jones: 0.5%Bandwagon3.3%This article: 7.5%Sophie Hurwitz: 9.9%Mother Jones: 6.4%Appeal to Emotion7.5%This article: 0.0%Sophie Hurwitz: 1.6%Mother Jones: 1.1%Begging the Question0.0%This article: 4.3%Sophie Hurwitz: 1.4%Mother Jones: 2.1%Post Hoc (False Cause)4.3%This article: 2.1%Sophie Hurwitz: 0.3%Mother Jones: 0.1%Tu Quoque2.1%This article: 0.0%Sophie Hurwitz: 1.0%Mother Jones: 0.5%Burden of Proof0.0%This article: 0.0%Sophie Hurwitz: 0.0%Mother Jones: 0.3%Appeal to Nature0.0%This article: 3.8%Sophie Hurwitz: 0.4%Mother Jones: 0.3%Composition/Division3.8%This article: 24.1%Sophie Hurwitz: 5.4%Mother Jones: 2.3%Anecdotal24.1%This article: 0.0%Sophie Hurwitz: 0.1%Mother Jones: 0.1%No True Scotsman0.0%This article: 6.9%Sophie Hurwitz: 1.7%Mother Jones: 1.4%Ambiguity (Equivocation)6.9%This article: 0.0%Sophie Hurwitz: 0.0%Mother Jones: 0.0%Gambler’s Fallacy0.0%This article: 0.0%Sophie Hurwitz: 0.1%Mother Jones: 0.1%Middle Ground0.0%This article: 0.0%Sophie Hurwitz: 0.0%Mother Jones: 0.1%Personal Incredulity0.0%This article: 0.0%Sophie Hurwitz: 0.1%Mother Jones: 0.1%Special Pleading0.0%This article: 0.0%Sophie Hurwitz: 0.4%Mother Jones: 0.3%Genetic Fallacy0.0%This article: 10.4%Sophie Hurwitz: 2.7%Mother Jones: 1.8%Unattributed Quote10.4%This article: 1.1%Sophie Hurwitz: 2.8%Mother Jones: 1.5%Quote-first Misdirection1.1%This article: 6.7%Sophie Hurwitz: 9.6%Mother Jones: 9.2%Biased Writer Voice6.7%This article: 0.0%Sophie Hurwitz: 2.0%Mother Jones: 1.8%Indoctrination0.0%This article: 4.3%Sophie Hurwitz: 4.9%Mother Jones: 4.5%Politically Left Leaning Bias4.3%This article: 5.6%Sophie Hurwitz: 1.0%Mother Jones: 0.3%Politically Right Leaning Bias5.6%This article: 2.7%Sophie Hurwitz: 0.2%Mother Jones: 0.7%Attempt to Sell a Product or S…2.7%

627 words analyzed.

Speakers

1speaker12%attributed speech551writer words
Voice mapSelect a segment to jump to its words
Writer's voice • 12 words • 100.0% coverageWriter's voice • 26 words • 0.0% coverageWriter's voice • 16 words • 0.0% coverageWriter's voice • 10 words • 0.0% coverageWriter's voice • 23 words • 100.0% coverageWriter's voice • 29 words • 0.0% coverageWriter's voice • 7 words • 0.0% coverageWriter's voice • 11 words • 0.0% coverageWriter's voice • 14 words • 0.0% coverageWriter's voice • 12 words • 0.0% coverageWriter's voice • 22 words • 0.0% coverageWriter's voice • 35 words • 100.0% coverageWriter's voice • 12 words • 0.0% coverageWriter's voice • 14 words • 0.0% coverageWriter's voice • 29 words • 0.0% coverageWriter's voice • 19 words • 0.0% coverageChad Marlow • 32 words • 100.0% coverageChad Marlow • 7 words • 100.0% coverageWriter's voice • 17 words • 100.0% coverageWriter's voice • 31 words • 0.0% coverageWriter's voice • 27 words • 0.0% coverageWriter's voice • 15 words • 0.0% coverageWriter's voice • 27 words • 100.0% coverageWriter's voice • 24 words • 0.0% coverageWriter's voice • 14 words • 0.0% coverageWriter's voice • 11 words • 0.0% coverageWriter's voice • 8 words • 0.0% coverageWriter's voice • 20 words • 100.0% coverageWriter's voice • 10 words • 0.0% coverageWriter's voice • 17 words • 0.0% coverageWriter's voice • 13 words • 0.0% coverageWriter's voice • 1 words • 0.0% coverageWriter's voice • 12 words • 0.0% coverageChad Marlow • 21 words • 0.0% coverageChad Marlow • 7 words • 0.0% coverageChad Marlow • 9 words • 0.0% coverageWriter's voice • 13 words • 100.0% coverage
Selected voice

Chad Marlow

100%flagged-word coverage
76 attributed words100% of attributed speech87% writer coverage
0%22.5%45.0%Unattributed Quote+36.1 ptsWriter: 6.0%Chad Marlow: 42.1%42.1%Quote-first Misdirection+9.2 ptsWriter: 0.0%Chad Marlow: 9.2%9.2%Biased Writer Voice+2.9 ptsWriter: 6.4%Chad Marlow: 9.2%9.2%Politically Right Leaning -6.4 ptsWriter: 6.4%Chad Marlow: 0.0%0.0%Politically Left Leaning B-4.9 ptsWriter: 4.9%Chad Marlow: 0.0%0.0%Attempt to Sell a Product -3.1 ptsWriter: 3.1%Chad Marlow: 0.0%0.0%

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.