Amazon flagged hundreds of thousands of suspected child sexual abuse images while building AI training datasets in 2025—a staggering volume that dwarfs competitors’ reports and exposes the tech industry’s reckless data collection practices. The e-commerce giant’s proactive scanning discovered this illegal content in externally sourced web data, leading to over one million reports to the National Center for Missing & Exploited Children.
Missing Context Hampers Child Protection Efforts
Amazon’s reports lack crucial origin details that would help law enforcement locate victims.
The flood of reports creates an unexpected problem. NCMEC’s CyberTipline executive director Fallon McNulty told investigators that Amazon’s submissions are largely “inactionable” because they don’t specify where the illegal content originated. “Having such a high volume… begs a lot of questions about where the data is coming from,” McNulty explained. This information gap prevents authorities from pursuing cases that could protect actual children.
Your AI Tools Built on Questionable Foundations
The same data sourcing methods powering consumer AI may harbor disturbing content.
Every time you generate an AI image or chat with a language model, you’re interacting with systems trained on massive internet datasets. The same “vacuum everything” approach that caught Amazon also affects the AI tools millions use daily. Think of it like building a house with materials you grabbed blindfolded from a junkyard—you’re bound to find things you weren’t looking for.
Industry Peers Take Different Approaches
Companies like Meta and Google provide more detailed reports with better investigative context.
While Amazon reported hundreds of thousands of cases, the company’s transparency report shows a different pattern from industry peers. Meta and Google provide more detailed reports that help investigators pursue cases. Thorn data scientist David Rust-Smith captures the core problem: “If you hoover up a ton of the internet, you’re going to get [CSAM].” The question becomes whether companies prioritize speed or safety in their data acquisition race.
Ready to trust AI systems built on digital dumpster diving? The industry’s “collect first, filter later” mentality reveals how consumer-facing AI tools may inherit the sins of their training data, forcing users to question what exactly powers their favorite AI applications.
