KUOW73%

New WA law seeking to protect immigrant workers takes effect this week 47%

By Sarah Mizes-Tan41%

6/8/2026, 9:25:03 PM

BS Summary: This article contains 19 faulty reasoning types, including Negativity Bias, Appeal to Authority, and Availability Heuristic, with Framing Effect as the most egregious example at 25.5% saturation with 105 hits. Analysis detected 855 faulty-reasoning hits from 412 analyzed words, generating a BS Score of 48.6% and a BS Rank of 47% (11,672 of 21,887 articles). This article is better (less manipulative) than 53.30% of the article peer group.

A new Washington law protecting workers from immigration enforcement in their workplace will go into effect later this week. 
Starting June 11, House Bill 2105 will require employers to give workers at least five business days’ notice that any federal agency is auditing their I-9 forms or any other worker records. 
“This one [law] in particular actually came from employers who wanted to know what their rights were, what do they do if ICE comes barging in their front door, what can they say, what do they do, what rights do their employees have?” 
Rep. 
Lillian Ortiz-Self, a Mukilteo Democrat who sponsored the bill, said in an interview earlier this year. 
Ortiz-Self also said the law is meant to protect against “bad actors”  employers who might weaponize a federal audit to punish workers. 
The law was originally requested by Attorney General Nick Brown and will require employers to provide notice of an upcoming audit in five other common languages spoken in Washington state. 
Employers will also need to provide information about workers’ rights and resources, and they will need to give employees the results of the audit within five days of receiving them. 
“Immigrant workers fuel Washington’s economy and contribute to our culture and prosperity,” Governor Bob Ferguson said when he signed the bill into law in March. 
“We must work hard to protect their rights.” 
Immigrant workers contribute about $145 billion to Washington’s economy each year, according to the state. 
If employers fail to give proper notice to their employees, they could risk being fined $500 for each instance of failing to notify workers, or $1,000 if a court finds they intentionally didn’t tell workers. 
Opponents of the law, like the Washington Food Industry Association (WFIA), have expressed concern that the requirements would unfairly penalize small business owners. 
“When an I-9 audit occurs, stores take on significant financial and legal burdens to comply with the federal government’s request in a timely manner,” Molly Pfaffenworth, government affairs director of the WFIA, said in a written statement. 
“We are concerned that a private right of action could result in frivolous lawsuits against small businesses that are essential to the vitality of Washington communities  especially small stores that have limited time, resources, and staffing to manage these requests." 
The state Attorney General’s office has said they will be working with small businesses to make sure they aren’t being fined unfairly. 
Article reasoning-pattern comparisonThis article: 3.6%Sarah Mizes-Tan: 2.3%KUOW: 2.6%Confirmation Bias3.6%This article: 0.0%Sarah Mizes-Tan: 0.4%KUOW: 1.3%Anchoring Bias0.0%This article: 19.4%Sarah Mizes-Tan: 2.7%KUOW: 3.4%Availability Heuristic19.4%This article: 0.0%Sarah Mizes-Tan: 1.3%KUOW: 1.2%Representativeness Heuristic0.0%This article: 0.0%Sarah Mizes-Tan: 0.6%KUOW: 0.7%Hindsight Bias0.0%This article: 0.0%Sarah Mizes-Tan: 1.8%KUOW: 1.4%Overconfidence Bias0.0%This article: 25.5%Sarah Mizes-Tan: 7.4%KUOW: 7.4%Framing Effect25.5%This article: 10.0%Sarah Mizes-Tan: 0.7%KUOW: 1.0%Loss Aversion10.0%This article: 5.3%Sarah Mizes-Tan: 1.2%KUOW: 1.0%Status Quo Bias5.3%This article: 0.0%Sarah Mizes-Tan: 0.1%KUOW: 0.2%Sunk Cost Effect0.0%This article: 2.9%Sarah Mizes-Tan: 3.2%KUOW: 3.8%Optimism Bias2.9%This article: 18.4%Sarah Mizes-Tan: 1.5%KUOW: 1.8%Pessimism Bias18.4%This article: 24.8%Sarah Mizes-Tan: 5.4%KUOW: 8.0%Negativity Bias24.8%This article: 0.0%Sarah Mizes-Tan: 2.3%KUOW: 2.0%Self-Serving Bias0.0%This article: 0.0%Sarah Mizes-Tan: 0.6%KUOW: 0.9%Fundamental Attribution Error0.0%This article: 0.0%Sarah Mizes-Tan: 0.2%KUOW: 0.2%Actor-Observer Bias0.0%This article: 5.6%Sarah Mizes-Tan: 2.0%KUOW: 2.0%In-Group Bias5.6%This article: 0.0%Sarah Mizes-Tan: 0.7%KUOW: 0.5%Out-Group Homogeneity Bias0.0%This article: 6.1%Sarah Mizes-Tan: 1.1%KUOW: 2.7%Halo Effect6.1%This article: 0.0%Sarah Mizes-Tan: 0.1%KUOW: 0.2%Horn Effect0.0%This article: 0.0%Sarah Mizes-Tan: 0.1%KUOW: 0.0%Dunning-Kruger Effect0.0%This article: 0.0%Sarah Mizes-Tan: 1.0%KUOW: 1.1%Recency Bias0.0%This article: 3.9%Sarah Mizes-Tan: 0.5%KUOW: 0.4%Primacy Effect3.9%This article: 0.0%Sarah Mizes-Tan: 0.0%KUOW: 0.0%Blind-Spot Bias0.0%This article: 0.0%Sarah Mizes-Tan: 1.1%KUOW: 0.5%Ad Hominem0.0%This article: 0.0%Sarah Mizes-Tan: 0.1%KUOW: 0.3%Straw Man0.0%This article: 20.1%Sarah Mizes-Tan: 4.1%KUOW: 4.3%Appeal to Authority20.1%This article: 0.0%Sarah Mizes-Tan: 1.2%KUOW: 1.4%False Dilemma0.0%This article: 10.0%Sarah Mizes-Tan: 2.1%KUOW: 0.9%Slippery Slope10.0%This article: 0.0%Sarah Mizes-Tan: 0.2%KUOW: 0.1%Circular Reasoning0.0%This article: 15.5%Sarah Mizes-Tan: 3.9%KUOW: 4.1%Hasty Generalization15.5%This article: 0.0%Sarah Mizes-Tan: 0.1%KUOW: 0.3%Red Herring0.0%This article: 0.0%Sarah Mizes-Tan: 1.0%KUOW: 0.8%Bandwagon0.0%This article: 9.7%Sarah Mizes-Tan: 8.3%KUOW: 6.1%Appeal to Emotion9.7%This article: 0.0%Sarah Mizes-Tan: 0.9%KUOW: 0.8%Begging the Question0.0%This article: 0.0%Sarah Mizes-Tan: 1.7%KUOW: 2.2%Post Hoc (False Cause)0.0%This article: 0.0%Sarah Mizes-Tan: 0.1%KUOW: 0.1%Tu Quoque0.0%This article: 0.0%Sarah Mizes-Tan: 0.6%KUOW: 0.3%Burden of Proof0.0%This article: 0.0%Sarah Mizes-Tan: 0.2%KUOW: 0.2%Appeal to Nature0.0%This article: 0.0%Sarah Mizes-Tan: 0.1%KUOW: 0.2%Composition/Division0.0%This article: 10.4%Sarah Mizes-Tan: 1.8%KUOW: 3.3%Anecdotal10.4%This article: 0.0%Sarah Mizes-Tan: 0.0%KUOW: 0.1%No True Scotsman0.0%This article: 0.0%Sarah Mizes-Tan: 1.8%KUOW: 1.4%Ambiguity (Equivocation)0.0%This article: 0.0%Sarah Mizes-Tan: 0.0%KUOW: 0.0%Gambler’s Fallacy0.0%This article: 0.0%Sarah Mizes-Tan: 0.2%KUOW: 0.1%Middle Ground0.0%This article: 0.0%Sarah Mizes-Tan: 0.2%KUOW: 0.1%Personal Incredulity0.0%This article: 0.0%Sarah Mizes-Tan: 0.4%KUOW: 0.2%Special Pleading0.0%This article: 0.0%Sarah Mizes-Tan: 0.0%KUOW: 0.2%Genetic Fallacy0.0%This article: 0.0%Sarah Mizes-Tan: 0.8%KUOW: 1.0%Unattributed Quote0.0%This article: 10.4%Sarah Mizes-Tan: 1.4%KUOW: 0.8%Quote-first Misdirection10.4%This article: 0.0%Sarah Mizes-Tan: 2.2%KUOW: 3.2%Biased Writer Voice0.0%This article: 1.9%Sarah Mizes-Tan: 1.6%KUOW: 1.5%Indoctrination1.9%This article: 3.9%Sarah Mizes-Tan: 1.9%KUOW: 1.1%Politically Left Leaning Bias3.9%This article: 0.0%Sarah Mizes-Tan: 1.4%KUOW: 0.2%Politically Right Leaning Bias0.0%This article: 0.0%Sarah Mizes-Tan: 0.5%KUOW: 1.3%Attempt to Sell a Product or S…0.0%

412 words analyzed.

Speakers

3speakers43%attributed speech235writer words
Voice mapSelect a segment to jump to its words
Writer's voice • 12 words • 0.0% coverageWriter's voice • 19 words • 0.0% coverageWriter's voice • 32 words • 0.0% coverageLillian Ortiz-Self • 43 words • 100.0% coverageWriter's voice • 1 words • 0.0% coverageWriter's voice • 16 words • 100.0% coverageLillian Ortiz-Self • 23 words • 0.0% coverageWriter's voice • 30 words • 0.0% coverageWriter's voice • 30 words • 0.0% coverageBob Ferguson • 25 words • 0.0% coverageBob Ferguson • 8 words • 100.0% coverageWriter's voice • 15 words • 0.0% coverageWriter's voice • 35 words • 0.0% coverageWriter's voice • 23 words • 0.0% coverageMolly Pfaffenworth • 37 words • 0.0% coverageMolly Pfaffenworth • 41 words • 0.0% coverageWriter's voice • 22 words • 0.0% coverage
Selected voice

Lillian Ortiz-Self

100%flagged-word coverage
66 attributed words37% of attributed speech73% writer coverage
0%35.0%70.0%Quote-first Misdirection+65.2 ptsWriter: 0.0%Lillian Ortiz-Self: 65.2%65.2%Politically Left Leaning B-6.8 ptsWriter: 6.8%Lillian Ortiz-Self: 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.