Privacy Advocates Raise Alarm Over Online Age Verification Provisions as House Passes KIDS Act 56%

By Julia Conley82%

6/30/2026, 5:17:57 AM

BS Summary: This article contains 13 faulty reasoning types, including Negativity Bias, Slippery Slope, and Framing Effect, with Appeal to Emotion as the most egregious example at 41% saturation with 360 hits. Analysis detected 1,297 faulty-reasoning hits from 879 analyzed words, generating a BS Score of 53.2% and a BS Rank of 56% (9,769 of 21,887 articles). This article is worse (more manipulative) than 55.40% of the article peer group.

Privacy Advocates Raise Alarm Over Online Age Verification Provisions as House Passes KIDS Act 
Opponents of a bill that is purported to protect children online said Monday night, after the legislation passed in the US House, that laws are "urgently" needed to stop Big Tech companies from preying on kids' vulnerabilities. 
"The KIDS Act is not that piece of legislation," said Rep. 
Pramila Jayapal (D-Wash.), who was one of 117 lawmakers who voted against the Kids Internet and Digital Safety (KIDS) Act, which passed with 267 votes, while 47 members of Congress did not vote. 
The bipartisan bill requires online platforms to use new safety features and parental controls, restricts the use of minors' personal data to target ads, and establishes new restrictions for AI chatbots and online games. 
But ahead of the bill's passage, the Electronic Frontier Foundation (EFF) was among the opponents raising alarm about other provisions "buried inside the KIDS Act" that would "push online services to verify all users’ ages, require government-directed moderation policies for online speech, and even create new rules about private and encrypted communications." 
The legislation, drawing from portions of 14 different online safety bills, "is a mess, with different age-gating schemes for different services, using different standards," wrote EFF senior policy analyst Joe Mullin. 
"It’s a lot of complexity, and a lot of legal risk. 
Faced with that, many companies will conclude that the safest option is restrictive age-checking practices across their entire platforms." 
As Mullin explained: 
Throughout the KOSA section of the legislation, special protections, controls, messaging settings, and parental tools are required whenever a website or app “knows or should have known” a user is a child (defined in the bill as anyone under 13) or a teen (defined as anyone between 13 and 16 years old). 
The problem is a website operator doesn’t need actual knowledge that a user is a minor to get in legal trouble. 
It applies when a platform “knows or should have known” a user’s age—a low, negligence-style standard of knowledge. 
If an online service gets it wrong, it’s going to be up to courts and regulators to decide, after the fact, if an online service “should” have known a user was 16. 
To try to avoid liability, services will have to determine which users are teenagers and which are not. 
Most won’t be able to simply trust their users. 
They’ll have to collect more information about age, before any lawsuit or government action arises. 
Some companies may respond by requesting driver's licenses or passports. 
Others will rely on age-estimation systems that attempt to guess users' ages by looking at existing activity or doing facial scans. 
At The Intercept, Caitlin Vogus of the Freedom of the Press Foundation and Aliya Bhatia of the Center for Democracy and Technology’s Free Expression Project warned ahead of the bill's passage that while the legislation is ostensibly meant to protect children, the age verification requirement could impact all users' ability to access social media platforms without revealing their identities—chilling anonymous speech and threatening would-be whistleblowers. 
"Threats to online anonymity harm everyone, but one group is often overlooked: journalists and the sources who talk to them," wrote Vogus and Bhatia. 
"Age verification requirements will help the Trump administration carry out its vendetta against the press by creating new avenues to identify journalists’ confidential sources." 
While the KIDS Act says it won't require online platforms to collect government IDs for age verification, they said, "at least some platforms will likely choose this route to comply with the law or offer it as a fallback approach when other methods inevitably fail." 
Former Republican congressman Justin Amash, a libertarian, accused the lawmakers who voted "yes" on the legislation of betraying "the Constitution and the American people." 
Other opponents of the legislation, including Jayapal, argued that the bill would allow tech companies to continue targeting children with algorithms that send harmful content to the youngest users. 
The legislation omits a "duty of care" provision that was included in the Kids Online Safety Act (KOSA), which was passed by the US Senate in 2024—a requirement that tech firms "exercise reasonable care” to prevent harms to children. 
Jayapal noted that the bill, which faces an uphill battle in the Senate, leaves "suicide, depression, addiction, substance use disorders, and eating disorders from the list of harms" that tech companies like Meta must address in their algorithms. 
The "duty of care" provision has been criticized as too vague by several digital rights groups, while some child safety groups said its omission in the KIDS Act would "let Big Tech off the hook." 
"We have seen time and again that these corporations cannot be trusted to put children's safety over their own profit margins," said Jayapal. 
"We cannot keep exposing our kids to platforms that are either completely indifferent to their safety or a direct threat to it." 
The KIDS Act, Jayapal said, also includes provisions "that do not do enough to actually address the harms of" artificial intelligence. 
"I voted no," said Jayapal, "because we have a real opportunity to pass bipartisan legislation that holds these companies to not just be transparent about the harms and mitigate them, but to actually prevent them." 
Article reasoning-pattern comparisonThis article: 4.2%Julia Conley: 5.7%Common Dreams: 7.9%Confirmation Bias4.2%This article: 0.0%Julia Conley: 0.0%Common Dreams: 0.9%Anchoring Bias0.0%This article: 4.9%Julia Conley: 5.6%Common Dreams: 5.0%Availability Heuristic4.9%This article: 0.0%Julia Conley: 0.2%Common Dreams: 0.8%Representativeness Heuristic0.0%This article: 0.0%Julia Conley: 0.8%Common Dreams: 0.7%Hindsight Bias0.0%This article: 1.3%Julia Conley: 0.2%Common Dreams: 1.6%Overconfidence Bias1.3%This article: 12.6%Julia Conley: 18.5%Common Dreams: 17.4%Framing Effect12.6%This article: 0.0%Julia Conley: 0.0%Common Dreams: 1.0%Loss Aversion0.0%This article: 0.0%Julia Conley: 0.9%Common Dreams: 0.6%Status Quo Bias0.0%This article: 0.0%Julia Conley: 0.0%Common Dreams: 0.2%Sunk Cost Effect0.0%This article: 4.0%Julia Conley: 0.6%Common Dreams: 1.6%Optimism Bias4.0%This article: 5.1%Julia Conley: 4.4%Common Dreams: 2.8%Pessimism Bias5.1%This article: 34.7%Julia Conley: 17.7%Common Dreams: 20.6%Negativity Bias34.7%This article: 0.0%Julia Conley: 0.8%Common Dreams: 0.9%Self-Serving Bias0.0%This article: 0.0%Julia Conley: 1.9%Common Dreams: 2.3%Fundamental Attribution Error0.0%This article: 0.0%Julia Conley: 0.7%Common Dreams: 0.2%Actor-Observer Bias0.0%This article: 0.0%Julia Conley: 2.2%Common Dreams: 3.8%In-Group Bias0.0%This article: 0.0%Julia Conley: 1.2%Common Dreams: 1.2%Out-Group Homogeneity Bias0.0%This article: 0.0%Julia Conley: 0.0%Common Dreams: 1.5%Halo Effect0.0%This article: 0.0%Julia Conley: 0.7%Common Dreams: 0.5%Horn Effect0.0%This article: 0.0%Julia Conley: 0.0%Common Dreams: 0.0%Dunning-Kruger Effect0.0%This article: 0.0%Julia Conley: 1.1%Common Dreams: 1.5%Recency Bias0.0%This article: 0.0%Julia Conley: 0.0%Common Dreams: 0.5%Primacy Effect0.0%This article: 0.0%Julia Conley: 0.0%Common Dreams: 0.0%Blind-Spot Bias0.0%This article: 2.7%Julia Conley: 3.4%Common Dreams: 6.2%Ad Hominem2.7%This article: 0.0%Julia Conley: 0.8%Common Dreams: 1.2%Straw Man0.0%This article: 0.0%Julia Conley: 1.5%Common Dreams: 5.6%Appeal to Authority0.0%This article: 0.0%Julia Conley: 2.8%Common Dreams: 3.2%False Dilemma0.0%This article: 23.3%Julia Conley: 7.8%Common Dreams: 3.2%Slippery Slope23.3%This article: 0.0%Julia Conley: 0.0%Common Dreams: 0.2%Circular Reasoning0.0%This article: 3.9%Julia Conley: 8.7%Common Dreams: 8.9%Hasty Generalization3.9%This article: 0.0%Julia Conley: 0.0%Common Dreams: 0.3%Red Herring0.0%This article: 0.0%Julia Conley: 0.8%Common Dreams: 2.2%Bandwagon0.0%This article: 41.0%Julia Conley: 26.6%Common Dreams: 18.9%Appeal to Emotion41.0%This article: 0.0%Julia Conley: 5.6%Common Dreams: 3.7%Begging the Question0.0%This article: 0.0%Julia Conley: 4.4%Common Dreams: 4.3%Post Hoc (False Cause)0.0%This article: 0.0%Julia Conley: 0.8%Common Dreams: 0.2%Tu Quoque0.0%This article: 0.0%Julia Conley: 0.7%Common Dreams: 1.2%Burden of Proof0.0%This article: 0.0%Julia Conley: 0.0%Common Dreams: 0.3%Appeal to Nature0.0%This article: 0.0%Julia Conley: 0.1%Common Dreams: 0.4%Composition/Division0.0%This article: 0.0%Julia Conley: 1.5%Common Dreams: 2.3%Anecdotal0.0%This article: 0.0%Julia Conley: 0.0%Common Dreams: 0.1%No True Scotsman0.0%This article: 0.0%Julia Conley: 1.5%Common Dreams: 1.8%Ambiguity (Equivocation)0.0%This article: 0.0%Julia Conley: 0.0%Common Dreams: 0.0%Gambler’s Fallacy0.0%This article: 0.0%Julia Conley: 0.0%Common Dreams: 0.1%Middle Ground0.0%This article: 0.0%Julia Conley: 0.0%Common Dreams: 0.0%Personal Incredulity0.0%This article: 0.0%Julia Conley: 0.0%Common Dreams: 0.3%Special Pleading0.0%This article: 0.0%Julia Conley: 0.0%Common Dreams: 0.3%Genetic Fallacy0.0%This article: 0.0%Julia Conley: 2.5%Common Dreams: 1.9%Unattributed Quote0.0%This article: 0.0%Julia Conley: 3.9%Common Dreams: 2.6%Quote-first Misdirection0.0%This article: 3.2%Julia Conley: 5.4%Common Dreams: 17.2%Biased Writer Voice3.2%This article: 0.0%Julia Conley: 1.4%Common Dreams: 8.6%Indoctrination0.0%This article: 6.7%Julia Conley: 6.3%Common Dreams: 17.0%Politically Left Leaning Bias6.7%This article: 0.0%Julia Conley: 0.0%Common Dreams: 0.5%Politically Right Leaning Bias0.0%This article: 0.0%Julia Conley: 0.0%Common Dreams: 1.5%Attempt to Sell a Product or S…0.0%

879 words analyzed.

Speakers

5speakers76%attributed speech209writer words
Voice mapSelect a segment to jump to its words
Writer's voice • 14 words • 100.0% coverageWriter's voice • 14 words • 100.0% coverageWriter's voice • 37 words • 0.0% coveragePramila Jayapal • 11 words • 0.0% coverageWriter's voice • 33 words • 0.0% coverageWriter's voice • 34 words • 0.0% coverageElectronic Frontier Foundation • 52 words • 0.0% coverageJoe Mullin • 31 words • 0.0% coverageJoe Mullin • 11 words • 0.0% coverageJoe Mullin • 19 words • 0.0% coverageWriter's voice • 3 words • 0.0% coverageJoe Mullin • 52 words • 0.0% coverageJoe Mullin • 21 words • 0.0% coverageJoe Mullin • 18 words • 0.0% coverageJoe Mullin • 32 words • 0.0% coverageJoe Mullin • 18 words • 0.0% coverageJoe Mullin • 9 words • 0.0% coverageJoe Mullin • 15 words • 0.0% coverageJoe Mullin • 10 words • 0.0% coverageJoe Mullin • 21 words • 0.0% coverageCaitlin Vogus • 65 words • 0.0% coverageCaitlin Vogus • 24 words • 0.0% coverageCaitlin Vogus • 24 words • 100.0% coverageCaitlin Vogus • 45 words • 0.0% coverageJustin Amash • 24 words • 0.0% coveragePramila Jayapal • 29 words • 0.0% coverageWriter's voice • 39 words • 0.0% coveragePramila Jayapal • 38 words • 0.0% coverageWriter's voice • 35 words • 0.0% coveragePramila Jayapal • 23 words • 0.0% coveragePramila Jayapal • 22 words • 0.0% coveragePramila Jayapal • 21 words • 0.0% coveragePramila Jayapal • 35 words • 100.0% coverage
100%flagged-word coverage
52 attributed words7.8% of attributed speech31% writer coverage
0%7.5%15.0%Biased Writer Voice-13.4 ptsWriter: 13.4%Electronic Frontier Foundation: 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.