Oakland woman taken by ICE in Denver is getting legal help 22%

By Darwin BondGraham17%

7/24/2026, 11:30:00 PM

BS Summary: This article contains 9 faulty reasoning types, including Confirmation Bias, Negativity Bias, and Red Herring, with Appeal to Authority as the most egregious example at 13.9% saturation with 86 hits. Analysis detected 400 faulty-reasoning hits from 617 analyzed words, generating a BS Score of 35.7% and a BS Rank of 22% (17,106 of 21,886 articles). This article is better (less manipulative) than 78.20% of the article peer group.

An Oakland resident who was detained by immigration agents at the Denver International Airport on July 20 and is being held in the Aurora ICE Processing Center in Colorado is getting legal help. 
On Thursday, attorneys for Chantal Morales Rojas filed a habeas corpus petition in federal court. 
Habeas corpus petitions are essentially lawsuits that force the government to legally justify why they can continue to incarcerate someone, or else release the person. 
Morales Rojas’ attorneys with the Lichter Immigration law firm say that ICE didn’t follow the law in her case. 
“Nobody is arguing that the government can’t enforce the immigration laws,” Laura Lichter, Morales Rojas’ lead attorney, said in a press release. 
“All we’re saying is that those laws apply to the government, too.” 
According to Lichter, Rojas, a 27-year-old software engineer and student, originally entered the United States in 2023 as a J-1 visa exchange visitor. 
Originally from Ecuador, she worked as an au pair in the Bay Area. 
Before her exchange visitor program ended, Morales Rojas “filed an application with U.S. immigration authorities that permits her to remain in the country while her case is under review, but doesn’t provide her with formal immigration status while she waits,” Lichter said. 
Over the past three-and-a-half years, Morales Rojas has lived openly with the government aware of her pending application, according to her attorneys, and she passed background checks and had work authorization. 
She also has no criminal history, they said. 
Morales Rojas was detained while attempting to board a Southwest Airlines flight from Denver to Oakland. 
Video of her arrest has been widely shared on social media. 
Her employers and friends were shocked by her arrest and organized a fundraising campaign to help pay for legal aid. 
Several people close to her, who’d been traveling with her, also stayed in Denver to coordinate support. 
Asked about the claims made by Morales Rojas’ attorneys, an unnamed spokesperson for the Department of Homeland Security said Morales Rojas entered the U.S. in 2024 and “overstayed her visa,” which only allowed her to remain in the country until Jan. 
1, 2025. 
“A pending asylum case and work authorization does NOT confer any type of legal status in the United States,” the spokesperson wrote in an email.  
Morales Rojas’ attorneys and friends haven’t said whether she ever applied for asylum, only that she had some type of application under review with federal immigration authorities at the time of her arrest. 
“If a person enters our country illegally, they are subject to detention or deportation. 
Each illegal alien receives due process,” the DHS spokesperson said. 
“You might not know it from watching the headlines for the last 18 months, but ICE generally can’t just arrest someone because they think they’re violating the immigration laws,” Lichter said. 
“They can put a case in front of an immigration judge if they think someone is here illegally, but unless there’s a special circumstance, they can’t legally detain someone without a proper arrest warrant and taking the time to review each case to see if a person is actually a flight risk or a danger.” 
Lichter said the habeas petition filed yesterday asks a federal judge to immediately order Morales Rojas’ release, or to require the government to provide specific reasons why they believe she needs to be detained. 
The facility where Morales Rojas is being held, Aurora, is under scrutiny right now because of an ongoing tuberculosis outbreak among some detainees. 
The center is run by the GEO Group, a private prison corporation that saw its net income jump by 800% in 2025, largely due to the Trump administration’s mass deportation policy. 
Article reasoning-pattern comparisonThis article: 11.7%Darwin BondGraham: 2.7%Berkeleyside: 2.1%Confirmation Bias11.7%This article: 0.0%Darwin BondGraham: 1.0%Berkeleyside: 0.9%Anchoring Bias0.0%This article: 1.8%Darwin BondGraham: 1.6%Berkeleyside: 2.3%Availability Heuristic1.8%This article: 0.0%Darwin BondGraham: 0.8%Berkeleyside: 0.8%Representativeness Heuristic0.0%This article: 0.0%Darwin BondGraham: 0.2%Berkeleyside: 0.4%Hindsight Bias0.0%This article: 0.0%Darwin BondGraham: 0.4%Berkeleyside: 0.8%Overconfidence Bias0.0%This article: 5.0%Darwin BondGraham: 3.0%Berkeleyside: 3.2%Framing Effect5.0%This article: 0.0%Darwin BondGraham: 0.2%Berkeleyside: 0.4%Loss Aversion0.0%This article: 0.0%Darwin BondGraham: 0.7%Berkeleyside: 0.5%Status Quo Bias0.0%This article: 0.0%Darwin BondGraham: 0.2%Berkeleyside: 0.2%Sunk Cost Effect0.0%This article: 0.0%Darwin BondGraham: 0.7%Berkeleyside: 3.4%Optimism Bias0.0%This article: 0.0%Darwin BondGraham: 0.8%Berkeleyside: 0.6%Pessimism Bias0.0%This article: 8.8%Darwin BondGraham: 3.7%Berkeleyside: 3.8%Negativity Bias8.8%This article: 0.0%Darwin BondGraham: 1.2%Berkeleyside: 1.8%Self-Serving Bias0.0%This article: 0.0%Darwin BondGraham: 0.6%Berkeleyside: 0.6%Fundamental Attribution Error0.0%This article: 0.0%Darwin BondGraham: 0.0%Berkeleyside: 0.2%Actor-Observer Bias0.0%This article: 0.0%Darwin BondGraham: 0.0%Berkeleyside: 0.9%In-Group Bias0.0%This article: 0.0%Darwin BondGraham: 0.2%Berkeleyside: 0.1%Out-Group Homogeneity Bias0.0%This article: 0.0%Darwin BondGraham: 1.6%Berkeleyside: 3.6%Halo Effect0.0%This article: 0.0%Darwin BondGraham: 0.0%Berkeleyside: 0.0%Horn Effect0.0%This article: 0.0%Darwin BondGraham: 0.0%Berkeleyside: 0.0%Dunning-Kruger Effect0.0%This article: 0.0%Darwin BondGraham: 0.3%Berkeleyside: 0.8%Recency Bias0.0%This article: 0.0%Darwin BondGraham: 0.3%Berkeleyside: 0.3%Primacy Effect0.0%This article: 0.0%Darwin BondGraham: 0.0%Berkeleyside: 0.0%Blind-Spot Bias0.0%This article: 0.0%Darwin BondGraham: 0.0%Berkeleyside: 0.2%Ad Hominem0.0%This article: 0.0%Darwin BondGraham: 0.2%Berkeleyside: 0.2%Straw Man0.0%This article: 13.9%Darwin BondGraham: 2.7%Berkeleyside: 3.2%Appeal to Authority13.9%This article: 0.0%Darwin BondGraham: 0.8%Berkeleyside: 0.7%False Dilemma0.0%This article: 0.0%Darwin BondGraham: 0.0%Berkeleyside: 0.3%Slippery Slope0.0%This article: 0.0%Darwin BondGraham: 0.3%Berkeleyside: 0.1%Circular Reasoning0.0%This article: 0.0%Darwin BondGraham: 1.8%Berkeleyside: 2.3%Hasty Generalization0.0%This article: 8.8%Darwin BondGraham: 0.8%Berkeleyside: 0.1%Red Herring8.8%This article: 0.0%Darwin BondGraham: 0.0%Berkeleyside: 0.5%Bandwagon0.0%This article: 3.2%Darwin BondGraham: 2.5%Berkeleyside: 3.3%Appeal to Emotion3.2%This article: 0.0%Darwin BondGraham: 0.7%Berkeleyside: 0.5%Begging the Question0.0%This article: 0.0%Darwin BondGraham: 2.2%Berkeleyside: 2.2%Post Hoc (False Cause)0.0%This article: 0.0%Darwin BondGraham: 0.2%Berkeleyside: 0.1%Tu Quoque0.0%This article: 0.0%Darwin BondGraham: 1.2%Berkeleyside: 0.4%Burden of Proof0.0%This article: 0.0%Darwin BondGraham: 0.0%Berkeleyside: 0.2%Appeal to Nature0.0%This article: 0.0%Darwin BondGraham: 0.0%Berkeleyside: 0.1%Composition/Division0.0%This article: 0.0%Darwin BondGraham: 1.3%Berkeleyside: 3.0%Anecdotal0.0%This article: 0.0%Darwin BondGraham: 0.0%Berkeleyside: 0.0%No True Scotsman0.0%This article: 0.0%Darwin BondGraham: 1.6%Berkeleyside: 1.3%Ambiguity (Equivocation)0.0%This article: 0.0%Darwin BondGraham: 0.0%Berkeleyside: 0.0%Gambler’s Fallacy0.0%This article: 0.0%Darwin BondGraham: 0.0%Berkeleyside: 0.1%Middle Ground0.0%This article: 0.0%Darwin BondGraham: 0.0%Berkeleyside: 0.1%Personal Incredulity0.0%This article: 0.0%Darwin BondGraham: 0.4%Berkeleyside: 0.2%Special Pleading0.0%This article: 0.0%Darwin BondGraham: 0.0%Berkeleyside: 0.1%Genetic Fallacy0.0%This article: 6.6%Darwin BondGraham: 3.4%Berkeleyside: 1.0%Unattributed Quote6.6%This article: 0.0%Darwin BondGraham: 0.3%Berkeleyside: 0.6%Quote-first Misdirection0.0%This article: 0.0%Darwin BondGraham: 0.6%Berkeleyside: 2.3%Biased Writer Voice0.0%This article: 0.0%Darwin BondGraham: 0.0%Berkeleyside: 0.7%Indoctrination0.0%This article: 5.0%Darwin BondGraham: 0.5%Berkeleyside: 0.1%Politically Left Leaning Bias5.0%This article: 0.0%Darwin BondGraham: 0.7%Berkeleyside: 0.1%Politically Right Leaning Bias0.0%This article: 0.0%Darwin BondGraham: 0.1%Berkeleyside: 2.0%Attempt to Sell a Product or S…0.0%

617 words analyzed.

Speakers

1speaker19%attributed speech497writer words
Voice mapSelect a segment to jump to its words
Writer's voice • 11 words • 0.0% coverageWriter's voice • 33 words • 0.0% coverageWriter's voice • 15 words • 0.0% coverageWriter's voice • 25 words • 0.0% coverageWriter's voice • 19 words • 0.0% coverageLaura Lichter • 22 words • 0.0% coverageLaura Lichter • 12 words • 0.0% coverageWriter's voice • 23 words • 0.0% coverageWriter's voice • 13 words • 0.0% coverageWriter's voice • 42 words • 0.0% coverageWriter's voice • 31 words • 0.0% coverageWriter's voice • 8 words • 0.0% coverageWriter's voice • 16 words • 0.0% coverageWriter's voice • 11 words • 0.0% coverageWriter's voice • 20 words • 0.0% coverageWriter's voice • 17 words • 0.0% coverageWriter's voice • 41 words • 100.0% coverageWriter's voice • 2 words • 0.0% coverageWriter's voice • 25 words • 0.0% coverageWriter's voice • 33 words • 0.0% coverageWriter's voice • 14 words • 0.0% coverageWriter's voice • 10 words • 0.0% coverageLaura Lichter • 31 words • 0.0% coverageLaura Lichter • 55 words • 0.0% coverageWriter's voice • 34 words • 0.0% coverageWriter's voice • 23 words • 0.0% coverageWriter's voice • 31 words • 100.0% coverage
Selected voice

Laura Lichter

72%flagged-word coverage
120 attributed words100% of attributed speech40% writer coverage
0%5.0%10.0%Unattributed Quote-8.2 ptsWriter: 8.2%Laura Lichter: 0.0%0.0%Politically Left Leaning B-6.2 ptsWriter: 6.2%Laura Lichter: 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.