A federal appeals court has ruled that a Maryland school district’s trans-affirming policy on student names and pronouns does not violate a Christian substitute teacher’s rights.
As the Washington Post reports, last Wednesday, the U.S. Court of Appeals for the 4th Circuit upheld a lower court decision dismissing a former Montgomery County Public Schools substitute teacher’s claims that the district’s policy violated her constitutional rights to free speech and freedom of religion by requiring her to refer to trans and nonbinary students by names and pronouns that align with their gender identity.
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The case concerned Kimberly Polk, who, according to the Post, began working as a substitute teacher with Montgomery County schools in 2021. Polk objected to the district’s name and pronoun policy on religious grounds, citing “her understanding of her Christian religion and the Holy Bible.”
So she requested a religious accommodation. When her request was denied, Polk stopped teaching and in 2024 sued the district, claiming that its policy violated her rights under the First Amendment.
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But a lower court denied Polk’s request for a preliminary injunction that would have allowed her to teach in elementary schools with no trans students while her case played out, and dismissed the majority of her claims. According to the Post, the court ruled that as an employee of Montgomery County Public Schools, Polk had been hired by the school board to speak on its behalf.
In last week’s 2-1 decision, a panel on the U.S. Court of Appeals for the 4th Circuit agreed with the lower court, ruling that Polk had not convinced them that the policy was hostile to her religious views, and that abiding by the policy was a part of the job she had accepted with the district and thus did not violate her First Amendment rights.
“Be it choosing curriculum or placing administrative requirements on how teachers deliver that curriculum, those decisions are entrusted to the Board, not to judges,” Judge Robert B. King, a Clinton appointee, wrote for the majority. “And it is the Board — not individual teachers — which is democratically responsive. If, as Polk says, teachers and parents disagree with the Guidelines, then democratic means exist to address their concerns.”
According to the Post, the 4th Circuit’s ruling may ultimately be applied to similar cases across Maryland, Virginia, North Carolina, South Carolina, and West Virginia
The paper also noted that Polk’s attorney said Friday that they may take the case to the Supreme Court.
Polk’s case is similar to one brought by a group of parents and teachers who claim that a school district in California violated their First Amendment right to free exercise of religion with its policy banning teachers from outing trans students to their parents. In December, Judge Roger Benitez with the U.S. District Court for the Southern District of California sided with the plaintiffs, issuing a permanent injunction barring the district from enforcing its policy. But after a panel of judges on the Ninth Circuit U.S. Court of Appeals issued a stay to Benitez’s injunction, allowing the district to continue banning teachers from outing students.
The plaintiffs in the California case have already asked the Supreme Court to overturn the Ninth Circuit’s stay. Because both the California and Maryland cases concern whether school policies around trans students violate teachers’ religious freedom rights under the First Amendment and resulted in differing decisions from circuit courts, the Supreme Court may be more likely to consider them.
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The John F. Kennedy Center for the Performing Arts in Washington, D.C., will close in July for two years, Donald Trump announced Sunday, saying the center needs renovations. But its reputation is what seems to need renovation, given that major acts have canceled and ticket sales have tanked since Trump took over the once-esteemed venue. Open since 1971, it had already undergone a renovation and expansion in 2019. Here’s what’s happened to the center in the past year. Après le lancement de La Maison d’Allanah à Versailles en février dernier, refuge d’urgence destiné aux personnes LGBTQIA+ en grande vulnérabilité, une seconde maison d’accueil a ouvert ce 1er février 2026 à Nogent-sur-Marne. Cette nouvelle structure répond à un besoin crucial : les demandes d’hébergement et d’accompagnement pour les personnes LGBTQIA+ victimes de violences, de […]
L’article Ouverture d’un refuge LGBT+ international à Nogent-sur-Marne : STOP homophobie étend La Maison d’Allanah est apparu en premier sur Association STOP homophobie.
Remember last summer when everyone was freaking out about the explosion of AI-generated child sexual abuse material? The New York Times ran a piece in July with the headline “A.I.-Generated Images of Child Sexual Abuse Are Flooding the Internet.” NCMEC put out a blog post calling the numbers an “alarming increase” and a “wake-up call.” The numbers were genuinely shocking: NCMEC reported receiving 485,000 AI-related CSAM reports in the first half of 2025, compared to just 67,000 for all of 2024.
That’s a big increase! And it would obviously be super concerning if any AI company were finding and detecting so much AI-generated CSAM, especially as we keep hearing that the big AI models (perhaps with the exception of Grok…) have been putting in place safeguards against CSAM generation.
The source of most of those reports? Amazon, which had submitted a staggering 380,000 of them, even though most people don’t tend to think of Amazon as much of an AI company. But, still, it became a six alarm fire about how much AI-generated CSAM Amazon had discovered. There were news stories about it, politicians demanding action, and the general sentiment was that this proved how big the problem was.
Except… it turns out that wasn’t actually what was happening. At all.
Bloomberg just published a deep dive into what was actually going on with Amazon’s reports, and the truth is very, very different from what everyone assumed. According to Bloomberg:
Amazon.com Inc. reported hundreds of thousands of pieces of content last year that it believed included child sexual abuse, which it found in data gathered to improve its artificial intelligence models. Though Amazon removed the content before training its models, child safety officials said the company has not provided information about its source, potentially hindering law enforcement from finding perpetrators and protecting victims.
Here’s the kicker—and I cannot stress this enough—none of Amazon’s reports involved AI-generated CSAM.
None of its reports submitted to NCMEC were of AI-generated material, the spokesperson added. Instead, the content was flagged by an automatic detection tool that compared it against a database of known child abuse material involving real victims, a process called “hashing.” Approximately 99.97% of the reports resulted from scanning “non-proprietary training data,” the spokesperson said.
What Amazon was actually reporting was known CSAM—images of real victims that already existed in databases—that their scanning tools detected in datasets being considered for AI training. They found it using traditional hash-matching detection tools, flagged it, and removed it before using the data. Which is… actually what you’d want a company to do?
But because it was found in the context of AI development, and because NCMEC’s reporting form has exactly one checkbox that says “Generative AI” with no way to distinguish between “we found known CSAM in our training data pipeline” and “our AI model generated new CSAM,” Amazon checked the box.
And thus, a massive misunderstanding was born.
Again, let’s be clear and separate out a few things here: the fact that Amazon found CSAM (known or not) in its training data is bad. It is a troubling sign of how much CSAM is found in the various troves of data AI companies use for training. And maybe the focus should be on that. Also, the fact that they then reported it to NCMEC and removed it from their training data after discovering it with hash matching is… good. That’s how things are supposed to work.
But the fact that the media (with NCMEC’s help) turned this into “OMG AI generated CSAM is growing at a massive rate” is likely extremely misleading.
Riana Pfefferkorn at Stanford, who co-authored an important research report last year about the challenges of NCMEC’s reporting system (which we wrote two separate posts about), wrote a letter to NCMEC that absolutely nails what went wrong here:
For half a year, “Massive Spike In AI-Generated CSAM” is the framing I’ve seen whenever news reports mention those H1 2025 numbers. Even the press release for a Senate bill about safeguarding AI models from being tainted with CSAM stated, “According to the National Center for Missing & Exploited Children, AI-generated material has proliferated at an alarming rate in the past year,” citing the NYT article.
Now we find out from Bloomberg that zero of Amazon’s reports involved AI-generated material; all 380,000 were hash hits to known CSAM. And we have Fallon [McNulty, executive director of the CyberTipline] confirming to Bloomberg that “with the exception of Amazon, the AI-related reports [NCMEC] received last year came in ‘really, really small volumes.'”
That is an absolutely mindboggling misunderstanding for everyone — the general public, lawmakers, researchers like me, etc. — to labor under for so long. If Bloomberg hadn’t dug into Amazon’s numbers, it’s not clear to me when, if ever, that misimpression would have been corrected.
She’s not wrong. Nearly 80% of all “Generative AI” CyberTipline reports to NCMEC in the first half of 2025 involved no AI-generated CSAM at all. The actual volume of AI-generated CSAM being reported? Apparently “really, really small.”
Now, to be (slightly?) fair to the NYT, they did run a minor correction a day after their original story noting that the 485,000 reports “comprised both A.I.-generated material and A.I. attempts to create material, not A.I.-generated material alone.” But that correction still doesn’t capture what actually happened. It wasn’t “AI-generated material and attempts”—it was overwhelmingly “known CSAM detected during AI training data vetting.” Those are very different things.
And it gets worse. Bloomberg reports that Amazon’s scanning threshold was set so low that many of those reports may not have even been actual CSAM:
Amazon believes it over-reported these cases to NCMEC to avoid accidentally missing something. “We intentionally use an over-inclusive threshold for scanning, which yields a high percentage of false positives,” the spokesperson added.
So we’ve got reports that aren’t AI-generated CSAM, many of which may not even be CSAM at all. Very helpful.
The frustrating thing is that this kind of confusion wasn’t just entirely predictable—it was predicted! When Pfefferkorn and her colleagues at Stanford published their report about NCMEC’s CSAM reporting system they literally called out the potential confusion in the options of what to check and how platforms would likely over-report stuff in an abundance of caution, because the penalty (both criminally and in reputation) for missing anything is so dire.
Indeed, the form for submitting to the CyberTipline has one checkbox for “Generative AI” that, as Pfefferkorn notes in her letter, can mean wildly different things depending on who’s checking it:
When the meaning of checking a single checkbox is so ambiguous that absent additional information, reports of known CSAM found in AI training data are facially indistinguishable from reports of new AI-generated material (or of text-only prompts seeking CSAM, or of attempts to upload known CSAM as part of a prompt, etc.), and that ambiguity leads to a months-long massive public misunderstanding about the scale of the AI-CSAM problem, then it is clear that the CyberTipline reporting form itself needs to change — not just how one particular ESP fills it out.
To its credit NCMEC did respond quickly to Pfefferkorn, and their response is… illuminating. They confirmed they’re working on updating the reporting system, but also noted that Amazon’s reports contained almost no useful information:
all those Amazon reports included minimal data, not even the file in question or the hash value, much less other contextual information about where or how Amazon detected the matching file
As Pfefferkorn put it, Amazon was basically giving NCMEC reports that said “we found something” with nothing else attached. NCMEC says they only learned about the false positives issue last week and are “very frustrated” by it.
Indeed, NCMEC’s boss told Bloomberg:
“There’s nothing then that can be done with those reports,” she said. “Our team has been really clear with [Amazon] that those reports are inactionable.”
There’s plenty of blame to go around here. Amazon clearly should have been more transparent about what they were reporting and why. NCMEC’s reporting form is outdated and creates ambiguity that led to a massive public misunderstanding. And the media (NYT included) ran with alarming numbers without asking obvious questions like “why is Amazon suddenly reporting 25x more than last year and no other AI company is even close?”
But, even worse, policymakers spent six months operating under the assumption that AI-generated CSAM was exploding at an unprecedented rate. Legislation was proposed. Resources were allocated. Public statements were made. All based on numbers that fundamentally misrepresented what was actually happening.
As Pfefferkorn notes:
Nobody benefits from being so egregiously misinformed. It isn’t a basis for sound policymaking (or an accurate assessment of NCMEC’s resource needs) if the true volume of AI-generated CSAM being reported is a mere fraction of what Congress and other regulators believe it is. It isn’t good for Amazon if people mistakenly think the company’s AI products are uniquely prone to generating CSAM compared with other options on the market (such as OpenAI, with its distant-second 75,000 reports during the same time period, per NYT). That impression also disserves users trying to pick safe, responsible AI tools to use; in actuality, per today’s revelations about training data vetting, Amazon is indeed trying to safeguard its models against CSAM. I can certainly think of at least one other AI company that’s been in the news a lot lately that seems to be acting far more carelessly.
None of this means that AI-generated CSAM isn’t a real and serious problem. It absolutely is, and it needs to be addressed. But you can’t effectively address a problem if your data about the scope of that problem is fundamentally wrong. And you especially can’t do it when the “alarming spike” that everyone has been pointing to turns out to be something else entirely.
The silver lining here, as Pfefferkorn points out, is that the actual news is… kind of good? Amazon’s AI models aren’t CSAM-generating machines. The company was actually doing the responsible thing by vetting its training data. And the real volume of AI-generated CSAM reports is apparently much lower than we’ve been led to believe.
But that good news was buried for six months under a misleading narrative that nobody bothered to dig into until Bloomberg did. And that’s a failure of transparency, of reporting systems, and of the kind of basic journalistic skepticism that should have kicked in when one company was suddenly responsible for 78% of all reports in a category.
We’ll see if NCMEC’s promised updates to the reporting form actually address these issues. In the meantime, maybe we can all agree that the next time there’s a 700% increase in reports of anything, it’s worth asking a few questions before writing the “everything is on fire” headline.
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