404 Media49%

ICE to Pay Thomson Reuters $125 Million to Find ‘Voter Fraud’ 74%

By Joseph Cox67%

7/17/2026, 3:35:37 PM

BS Summary: This article contains 23 faulty reasoning types, including Framing Effect, Availability Heuristic, and Ambiguity (Equivocation), with Negativity Bias as the most egregious example at 32.7% saturation with 238 hits. Analysis detected 1,593 faulty-reasoning hits from 727 analyzed words, generating a BS Score of 65.9% and a BS Rank of 74% (5,634 of 21,176 articles). This article is worse (more manipulative) than 73.40% of the article peer group.

The Department of Homeland Security (DHS) plans to pay data broker giant Thomson Reuters $125 million for access to its databases of personal data  which includes peoples’ names, addresses, Social Security numbers, ethnicity, social media posts, and geolocation information  to help Immigration and Customs Enforcement (ICE) investigate what it describes as “voters fraud” and immigration fraud, according to procurement documents reviewed by 404 Media. 
The document says Thomson Reuters is able to let ICE continuously monitor millions of people and entities of interest. 
The news comes after President Trump held a conspiracy-laden and unhinged press conference about election security on Thursday, setting the stage for potentially undermining the legitimacy of the upcoming midterm elections. 
It also follows ICE fatally shooting 2 people in a week. 
“Due to ICE’s re-prioritized mission, there is need for this data to be readily accessible to support the presidential mandate of the identification of Voters Fraud, Immigration Fraud and National Security,” the procurement document reads. 
“This data specifically validates and verifies school, benefit, immigration and other eligibility requirements.” 
Thomson Reuters is most well known for running the Reuters news agency, but the company is also a massive data broker and sells access to that data to companies and governments. 
Its data product, called CLEAR, promises to “Accelerate investigations confidently through a vast collection of public and proprietary records,” according to Thomson Reuters’ website. 
Thomson Reuters lists some of the data sources that feed into CLEAR, and 404 Media has obtained an internal list. 
It includes credit header data, which is the personal information someone provides to a financial institution to open a credit card like their address, which goes to the credit bureaus and then transferred to Thomson Reuters. 
The procurement document also says it includes social media, property records, geolocation information, license plate data, and more. 
The planned sale is specifically with Thomson Reuters Special Services (TRSS), a subsidiary which often handles Thomson Reuters’ government contracts. 
The document says TRSS offers embedded data scientists to clients who are cleared up to a Top Secret/SCI [Sensitive Compartmented Information] level. 
The plan is to pay the company $25 million a year over the next five years, totaling $125 million. 
Parts of DHS have previously accessed CLEAR data, and 404 Media has reported on internal ICE documents which say it is integrated with Palantir’s tool for finding neighborhoods to raid. 
But the new document stresses that ICE has such a high need for this data, that it is planning to spend more than a hundred million dollars on another form of access to it. 
In a previous statement to 404 Media about ICE’s earlier access to CLEAR data, Thomson Reuters said, “It’s inaccurate to connect CLEAR to ICE and its deportation and enforcement operations.” 
When 404 Media contacted the company on Thursday and sent the new procurement document which describes ICE wanting the data for voter fraud and immigration fraud enforcement, the company provided a new statement: “We prohibit the use of CLEAR for the purpose of identifying and locating noncriminal immigrants or undocumented individuals with the intention of deportation solely on the basis of the individual’s immigration status. 
We take this restriction seriously, and we enforce it.” 
“We continue to work with our customers to provide technology and services that support investigations into areas of national security and public safety, such as child exploitation, human trafficking, narcotics and weapons trafficking, and fraud/financial crime,” the statement added. 
The company also said, “Immigration status is not a search field in CLEAR.” 
Thomson Reuters previously fired a longstanding employee after they spoke out about the company selling data products to ICE. 
404 Media previously reported ICE invited staff to demos of a license plate reader app from Motorola that can be enhanced by CLEAR data. 
Emma Pullman, head of shareholder engagement and responsible investment for the B.C. 
General Employees’ Union (BCGEU), which is a minority shareholder in Thomson Reuters, said, “Thomson Reuters has given shareholders, employees, and the media inconsistent and shifting accounts of the nature of its ICE contracts. 
TRSS latest ICE contract is the first to include voter fraud that we are aware of, and we intend to press the company, alongside other investors, for clarity on this contract.” 
Update: This piece has been updated to include comment from Emma Pullman. 
Article reasoning-pattern comparisonThis article: 4.3%Joseph Cox: 2.3%404 Media: 3.5%Confirmation Bias4.3%This article: 0.0%Joseph Cox: 0.9%404 Media: 0.9%Anchoring Bias0.0%This article: 21.2%Joseph Cox: 5.7%404 Media: 3.9%Availability Heuristic21.2%This article: 4.3%Joseph Cox: 0.8%404 Media: 1.6%Representativeness Heuristic4.3%This article: 0.0%Joseph Cox: 0.4%404 Media: 0.5%Hindsight Bias0.0%This article: 0.0%Joseph Cox: 1.6%404 Media: 2.7%Overconfidence Bias0.0%This article: 23.2%Joseph Cox: 13.2%404 Media: 5.6%Framing Effect23.2%This article: 0.0%Joseph Cox: 1.0%404 Media: 0.4%Loss Aversion0.0%This article: 0.0%Joseph Cox: 0.5%404 Media: 0.6%Status Quo Bias0.0%This article: 0.0%Joseph Cox: 0.1%404 Media: 0.1%Sunk Cost Effect0.0%This article: 0.0%Joseph Cox: 1.0%404 Media: 2.7%Optimism Bias0.0%This article: 4.7%Joseph Cox: 3.5%404 Media: 1.7%Pessimism Bias4.7%This article: 32.7%Joseph Cox: 20.0%404 Media: 11.6%Negativity Bias32.7%This article: 9.8%Joseph Cox: 1.0%404 Media: 0.9%Self-Serving Bias9.8%This article: 4.5%Joseph Cox: 0.4%404 Media: 0.7%Fundamental Attribution Error4.5%This article: 0.0%Joseph Cox: 0.0%404 Media: 0.1%Actor-Observer Bias0.0%This article: 0.0%Joseph Cox: 0.8%404 Media: 0.8%In-Group Bias0.0%This article: 0.0%Joseph Cox: 0.0%404 Media: 0.2%Out-Group Homogeneity Bias0.0%This article: 4.3%Joseph Cox: 0.9%404 Media: 1.0%Halo Effect4.3%This article: 0.0%Joseph Cox: 0.0%404 Media: 0.2%Horn Effect0.0%This article: 0.0%Joseph Cox: 0.0%404 Media: 0.0%Dunning-Kruger Effect0.0%This article: 4.3%Joseph Cox: 1.7%404 Media: 1.0%Recency Bias4.3%This article: 8.9%Joseph Cox: 0.7%404 Media: 0.3%Primacy Effect8.9%This article: 0.0%Joseph Cox: 0.2%404 Media: 0.0%Blind-Spot Bias0.0%This article: 0.0%Joseph Cox: 0.5%404 Media: 0.3%Ad Hominem0.0%This article: 0.0%Joseph Cox: 0.1%404 Media: 0.3%Straw Man0.0%This article: 11.7%Joseph Cox: 4.0%404 Media: 3.2%Appeal to Authority11.7%This article: 0.0%Joseph Cox: 1.4%404 Media: 1.3%False Dilemma0.0%This article: 0.0%Joseph Cox: 0.9%404 Media: 1.2%Slippery Slope0.0%This article: 0.0%Joseph Cox: 0.0%404 Media: 0.2%Circular Reasoning0.0%This article: 4.5%Joseph Cox: 9.5%404 Media: 8.0%Hasty Generalization4.5%This article: 0.0%Joseph Cox: 0.7%404 Media: 0.3%Red Herring0.0%This article: 0.0%Joseph Cox: 1.1%404 Media: 0.7%Bandwagon0.0%This article: 5.9%Joseph Cox: 6.3%404 Media: 4.7%Appeal to Emotion5.9%This article: 0.0%Joseph Cox: 0.2%404 Media: 0.7%Begging the Question0.0%This article: 5.8%Joseph Cox: 1.8%404 Media: 1.9%Post Hoc (False Cause)5.8%This article: 0.0%Joseph Cox: 0.0%404 Media: 0.1%Tu Quoque0.0%This article: 0.0%Joseph Cox: 1.7%404 Media: 0.6%Burden of Proof0.0%This article: 5.4%Joseph Cox: 0.3%404 Media: 0.4%Appeal to Nature5.4%This article: 0.0%Joseph Cox: 0.0%404 Media: 0.2%Composition/Division0.0%This article: 2.6%Joseph Cox: 2.2%404 Media: 2.9%Anecdotal2.6%This article: 0.0%Joseph Cox: 0.0%404 Media: 0.1%No True Scotsman0.0%This article: 19.3%Joseph Cox: 4.6%404 Media: 2.1%Ambiguity (Equivocation)19.3%This article: 0.0%Joseph Cox: 0.0%404 Media: 0.0%Gambler’s Fallacy0.0%This article: 0.0%Joseph Cox: 0.0%404 Media: 0.1%Middle Ground0.0%This article: 0.0%Joseph Cox: 0.0%404 Media: 0.1%Personal Incredulity0.0%This article: 0.0%Joseph Cox: 0.0%404 Media: 0.1%Special Pleading0.0%This article: 0.0%Joseph Cox: 0.0%404 Media: 0.1%Genetic Fallacy0.0%This article: 12.0%Joseph Cox: 4.8%404 Media: 2.7%Unattributed Quote12.0%This article: 8.9%Joseph Cox: 2.7%404 Media: 1.7%Quote-first Misdirection8.9%This article: 13.3%Joseph Cox: 9.8%404 Media: 11.7%Biased Writer Voice13.3%This article: 0.0%Joseph Cox: 2.2%404 Media: 1.5%Indoctrination0.0%This article: 0.0%Joseph Cox: 2.5%404 Media: 0.6%Politically Left Leaning Bias0.0%This article: 4.3%Joseph Cox: 0.2%404 Media: 0.0%Politically Right Leaning Bias4.3%This article: 3.3%Joseph Cox: 7.7%404 Media: 2.9%Attempt to Sell a Product or S…3.3%

727 words analyzed.

Speakers

3speakers26%attributed speech536writer words
Voice mapSelect a segment to jump to its words
Writer's voice • 11 words • 0.0% coverageWriter's voice • 66 words • 100.0% coverageWriter's voice • 19 words • 0.0% coverageWriter's voice • 31 words • 100.0% coverageWriter's voice • 11 words • 0.0% coverageWriter's voice • 35 words • 100.0% coverageWriter's voice • 13 words • 100.0% coverageWriter's voice • 31 words • 100.0% coverageThomson Reuters • 24 words • 100.0% coverageWriter's voice • 20 words • 0.0% coverageWriter's voice • 36 words • 0.0% coverageWriter's voice • 18 words • 0.0% coverageWriter's voice • 20 words • 0.0% coverageWriter's voice • 22 words • 0.0% coverageWriter's voice • 19 words • 0.0% coverageWriter's voice • 30 words • 0.0% coverageWriter's voice • 34 words • 0.0% coverageThomson Reuters • 30 words • 100.0% coverageWriter's voice • 65 words • 100.0% coverageThomson Reuters • 9 words • 100.0% coverageThomson Reuters • 39 words • 0.0% coverageThomson Reuters • 13 words • 0.0% coverageWriter's voice • 19 words • 0.0% coverageWriter's voice • 24 words • 0.0% coverageEmma Pullman • 12 words • 0.0% coverageBCGEU General Employees’ Union • 33 words • 0.0% coverageBCGEU General Employees’ Union • 31 words • 0.0% coverageWriter's voice • 12 words • 0.0% coverage
100%flagged-word coverage
64 attributed words34% of attributed speech87% writer coverage
0%10.0%20.0%Biased Writer Voice-18.1 ptsWriter: 18.1%BCGEU General Employees’ Union: 0.0%0.0%Quote-first Misdirection-12.1 ptsWriter: 12.1%BCGEU General Employees’ Union: 0.0%0.0%Unattributed Quote-9.0 ptsWriter: 9.0%BCGEU General Employees’ Union: 0.0%0.0%Politically Right Leaning -5.8 ptsWriter: 5.8%BCGEU General Employees’ Union: 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.