U.S. Senate bans lawmakers from betting on prediction apps 60%

By Lillian Mann0%

5/4/2026, 6:01:26 PM

BS Summary: This article contains 14 faulty reasoning types, including Framing Effect, Appeal to Authority, and Appeal to Emotion, with Biased Writer Voice as the most egregious example at 28.1% saturation with 202 hits. Analysis detected 753 faulty-reasoning hits from 719 analyzed words, generating a BS Score of 55.6% and a BS Rank of 60% (8,954 of 21,887 articles). This article is worse (more manipulative) than 59.10% of the article peer group.

In this photo illustration, Predictions market sites are shown on electronic devices on February 25, 2026 in Chicago, Illinois. 
(Photo Illustration by Scott Olson/Getty Images) 
OAN Staff Lillian Mann 
6:00 PM  Monday, May 4, 2026 
The U.S. 
Senate voted unanimously on Thursday to prohibit senators, their staff and other chamber personnel from using prediction market apps -- marking a decisive step to address growing ethical and national security concerns surrounding such platforms. 
Additionally, since the Senate only has the power to change its own rules, the ban currently only applies to Senators and their staff, not the entire U.S. 
Congress. 
Before leaving on a week-long recess, the Senate passed the measure, led by Senator Bernie Moreno (R-Ohio), to immediately ban insider trading within the Senate on trading apps such as Polymarket and Kalshi. 
The trading apps allow users to anonymously bet on the outcome of almost anything, ranging from sports, celebrity gossip, and even major political and military outcomes. 
Meanwhile, the vote follows Kalshi’s decision in April to ban three political candidates who had attempted to bet on their own races, as well as a U.S. 
Special Forces officer who won $400,000 after betting the December raid, which captured Venezuelan socialist dictator Nicolás Maduro, would be successful. 
The surge in betting has raised growing concerns over ethical and national security matters, as a spike in betting on certain geopolitical outcomes have emerged with the growing popularity of the apps. 
Senator Elissa Slotkin (D-Mich.), a former CIA analyst who served in Iraq, called potential cases of insider trading on prediction markets “an operational risk.” 
Additionally, Senator Moreno (R-Ohio), who sponsored the ban, emphasized, “Serving in Congress is an honor, not a side hustle, Americans deserve to know that their leaders are here for the right reason.” 
“I don’t believe we should trade stocks at all. 
It’s completely insane,” Moreno said. 
“I think we should focus on our jobs and have our voters go, ‘Hey, this guy’s voting this way, because this is the right thing for the state.’" 
Notably, at least 16 accounts have made more than $100,000 after correctly predicting a February strike on Iran, hours before the U.S. and Israeli attack on Iranian officials killed Iran’s supreme leader, Ayatollah Ali Khamenei. 
“We must never allow Congress to turn into a casino where members representing the public can gamble on wars or economic crises or elections,” Chuck Schumer (D-N.Y.) said on the Senate floor on Thursday. 
“Speaker Johnson should immediately do the same thing in the House,” he added. 
Senator John Curtis (R-Utah.) teamed up with Slotkin and Senator Todd Young (R-Ind.) on a bill that would ban government officials and employees—including the president and vice president—from participating in prediction markets. 
The measure would impose fines equal to twice the profits gained from such bets. 
Both Kalshi and Polymarket have issued public statements supporting the Senate’s decision to ban its members from their platforms, signaling a desire to align with federal ethics standards while defending the integrity of their markets. 
Despite these assurances, the industry has struggled with high-profile instances of self-dealing that suggest insider trading remains a persistent challenge. 
In April 2026, Kalshi took the unprecedented step of fining and suspending three congressional candidates for attempting to wager on their own election outcomes. 
The sanctioned individuals included Mark Moran, an independent Senate candidate from Virginia; Ezekiel Enriquez, a Republican who ran in a Texas primary; and Matt Klein, a Minnesota state senator seeking a seat in the U.S. 
House. 
In response to the growing scrutiny, Kalshi CEO Tarek Mansour emphasized that the company already “proactively blocks members of Congress” from participating in its markets to maintain data purity. 
To enforce this, Kalshi spokesperson Elisabeth Diana explained that the platform utilizes a sophisticated “politically exposed persons list” to identify and filter out individuals with a high risk of possessing material, non-public information. 
While the companies maintain that their platforms are intended to serve as “truth machines” for forecasting, the Senate’s unanimous vote reflects a broader legislative consensus that the risk of insiders “governing for profit” outweighs the potential benefits of these predictive tools. 
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Article reasoning-pattern comparisonThis article: 0.0%Lillian Mann: 4.7%One America News Network: 4.6%Confirmation Bias0.0%This article: 0.0%Lillian Mann: 0.6%One America News Network: 2.4%Anchoring Bias0.0%This article: 0.0%Lillian Mann: 2.9%One America News Network: 3.9%Availability Heuristic0.0%This article: 0.0%Lillian Mann: 1.3%One America News Network: 1.1%Representativeness Heuristic0.0%This article: 0.0%Lillian Mann: 1.1%One America News Network: 0.9%Hindsight Bias0.0%This article: 1.3%Lillian Mann: 1.5%One America News Network: 2.9%Overconfidence Bias1.3%This article: 15.0%Lillian Mann: 8.6%One America News Network: 15.3%Framing Effect15.0%This article: 0.0%Lillian Mann: 0.2%One America News Network: 1.0%Loss Aversion0.0%This article: 0.0%Lillian Mann: 0.6%One America News Network: 1.1%Status Quo Bias0.0%This article: 0.0%Lillian Mann: 0.1%One America News Network: 0.1%Sunk Cost Effect0.0%This article: 0.0%Lillian Mann: 2.4%One America News Network: 4.3%Optimism Bias0.0%This article: 0.0%Lillian Mann: 1.1%One America News Network: 1.8%Pessimism Bias0.0%This article: 0.0%Lillian Mann: 8.8%One America News Network: 11.8%Negativity Bias0.0%This article: 8.6%Lillian Mann: 2.8%One America News Network: 3.2%Self-Serving Bias8.6%This article: 0.0%Lillian Mann: 1.5%One America News Network: 1.7%Fundamental Attribution Error0.0%This article: 0.0%Lillian Mann: 0.1%One America News Network: 0.2%Actor-Observer Bias0.0%This article: 0.0%Lillian Mann: 2.0%One America News Network: 3.9%In-Group Bias0.0%This article: 0.0%Lillian Mann: 0.8%One America News Network: 1.9%Out-Group Homogeneity Bias0.0%This article: 0.0%Lillian Mann: 3.3%One America News Network: 5.3%Halo Effect0.0%This article: 0.0%Lillian Mann: 0.1%One America News Network: 0.4%Horn Effect0.0%This article: 0.0%Lillian Mann: 0.0%One America News Network: 0.0%Dunning-Kruger Effect0.0%This article: 0.0%Lillian Mann: 1.0%One America News Network: 2.0%Recency Bias0.0%This article: 0.0%Lillian Mann: 0.4%One America News Network: 0.7%Primacy Effect0.0%This article: 0.0%Lillian Mann: 0.1%One America News Network: 0.1%Blind-Spot Bias0.0%This article: 0.0%Lillian Mann: 1.2%One America News Network: 2.5%Ad Hominem0.0%This article: 0.0%Lillian Mann: 0.3%One America News Network: 0.6%Straw Man0.0%This article: 12.0%Lillian Mann: 4.2%One America News Network: 6.3%Appeal to Authority12.0%This article: 5.7%Lillian Mann: 1.6%One America News Network: 1.8%False Dilemma5.7%This article: 4.7%Lillian Mann: 0.5%One America News Network: 1.1%Slippery Slope4.7%This article: 0.0%Lillian Mann: 0.0%One America News Network: 0.1%Circular Reasoning0.0%This article: 4.9%Lillian Mann: 4.2%One America News Network: 5.4%Hasty Generalization4.9%This article: 0.0%Lillian Mann: 0.4%One America News Network: 0.5%Red Herring0.0%This article: 1.8%Lillian Mann: 0.5%One America News Network: 1.1%Bandwagon1.8%This article: 9.9%Lillian Mann: 6.7%One America News Network: 10.0%Appeal to Emotion9.9%This article: 0.0%Lillian Mann: 1.3%One America News Network: 1.7%Begging the Question0.0%This article: 4.9%Lillian Mann: 2.5%One America News Network: 3.0%Post Hoc (False Cause)4.9%This article: 0.0%Lillian Mann: 0.1%One America News Network: 0.2%Tu Quoque0.0%This article: 0.0%Lillian Mann: 0.8%One America News Network: 0.6%Burden of Proof0.0%This article: 0.0%Lillian Mann: 0.3%One America News Network: 0.2%Appeal to Nature0.0%This article: 0.0%Lillian Mann: 0.2%One America News Network: 0.3%Composition/Division0.0%This article: 2.9%Lillian Mann: 1.0%One America News Network: 1.6%Anecdotal2.9%This article: 0.0%Lillian Mann: 0.1%One America News Network: 0.1%No True Scotsman0.0%This article: 0.0%Lillian Mann: 1.8%One America News Network: 1.4%Ambiguity (Equivocation)0.0%This article: 0.0%Lillian Mann: 0.0%One America News Network: 0.0%Gambler’s Fallacy0.0%This article: 0.0%Lillian Mann: 0.1%One America News Network: 0.1%Middle Ground0.0%This article: 0.0%Lillian Mann: 0.7%One America News Network: 0.2%Personal Incredulity0.0%This article: 0.0%Lillian Mann: 0.2%One America News Network: 0.3%Special Pleading0.0%This article: 0.0%Lillian Mann: 0.0%One America News Network: 0.7%Genetic Fallacy0.0%This article: 0.0%Lillian Mann: 1.7%One America News Network: 1.7%Unattributed Quote0.0%This article: 0.0%Lillian Mann: 1.6%One America News Network: 1.5%Quote-first Misdirection0.0%This article: 28.1%Lillian Mann: 5.0%One America News Network: 5.2%Biased Writer Voice28.1%This article: 0.0%Lillian Mann: 0.8%One America News Network: 1.1%Indoctrination0.0%This article: 0.0%Lillian Mann: 0.3%One America News Network: 0.4%Politically Left Leaning Bias0.0%This article: 2.9%Lillian Mann: 2.9%One America News Network: 3.8%Politically Right Leaning Bias2.9%This article: 2.1%Lillian Mann: 1.5%One America News Network: 1.5%Attempt to Sell a Product or S…2.1%

719 words analyzed.

Speakers

6speakers29%attributed speech508writer words
Voice mapSelect a segment to jump to its words
Writer's voice • 1 words • 0.0% coverageWriter's voice • 8 words • 0.0% coverageWriter's voice • 19 words • 0.0% coverageWriter's voice • 6 words • 0.0% coverageOAN Staff Lillian Mann • 4 words • 0.0% coverageWriter's voice • 7 words • 0.0% coverageWriter's voice • 2 words • 0.0% coverageWriter's voice • 35 words • 100.0% coverageWriter's voice • 27 words • 0.0% coverageWriter's voice • 1 words • 0.0% coverageWriter's voice • 33 words • 0.0% coverageWriter's voice • 26 words • 0.0% coverageWriter's voice • 27 words • 0.0% coverageWriter's voice • 21 words • 100.0% coverageWriter's voice • 32 words • 100.0% coverageSenator Elissa Slotkin (D-Mich.) • 24 words • 0.0% coverageSenator Moreno (R-Ohio) • 32 words • 0.0% coverageSenator Moreno (R-Ohio) • 9 words • 0.0% coverageSenator Moreno (R-Ohio) • 5 words • 0.0% coverageSenator Moreno (R-Ohio) • 28 words • 0.0% coverageWriter's voice • 35 words • 100.0% coverageChuck Schumer (D-N.Y.) • 34 words • 0.0% coverageChuck Schumer (D-N.Y.) • 13 words • 0.0% coverageWriter's voice • 32 words • 0.0% coverageWriter's voice • 14 words • 0.0% coverageWriter's voice • 35 words • 100.0% coverageWriter's voice • 20 words • 100.0% coverageWriter's voice • 24 words • 100.0% coverageWriter's voice • 35 words • 0.0% coverageWriter's voice • 1 words • 0.0% coverageTarek Mansour • 29 words • 0.0% coverageElisabeth Diana • 33 words • 0.0% coverageWriter's voice • 41 words • 0.0% coverageWriter's voice • 2 words • 100.0% coverageWriter's voice • 10 words • 100.0% coverageWriter's voice • 3 words • 100.0% coverageWriter's voice • 4 words • 0.0% coverageWriter's voice • 7 words • 0.0% coverage
Selected voice

Elisabeth Diana

100%flagged-word coverage
33 attributed words16% of attributed speech51% writer coverage
0%20.0%40.0%Biased Writer Voice-39.8 ptsWriter: 39.8%Elisabeth Diana: 0.0%0.0%Politically Right Leaning -4.1 ptsWriter: 4.1%Elisabeth Diana: 0.0%0.0%Attempt to Sell a Product -3.0 ptsWriter: 3.0%Elisabeth Diana: 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.