AI-generated CSAM: not victimless, and deeply harmful

Kari Jaaskelainen

06 Oct 2025 — 1 min read

Some say synthetic CSAM has no victims. This paper shows why that’s dangerously wrong.

The authors examine how AI-generated child sexual abuse material can revictimize known survivors, create synthetic depictions of children who were not abused, facilitate grooming and extortion, normalize exploitation, and lower barriers to offending. They caution against claims that it could reduce harm.

Why it matters: Treating this as “harmless” risks delaying urgent action across tech, policy, and law enforcement. It’s a warning flare: ignoring systemic risks won’t make them disappear.

Think of it as a toxic spill in the information ecosystem—the contamination spreads, even if you don’t see it. We need coordinated cleanup and stronger safeguards. 🚨🛑🛡️

Read the analysis, then support evidence-based protections that put children’s safety first.

Paper: http://arxiv.org/abs/2510.02978v1

Paper: http://arxiv.org/abs/2510.02978v1

Register: https://www.AiFeta.com

#ChildSafety #TrustAndSafety #AIEthics #Policy #SafetyByDesign #OnlineSafety #ResponsibleAI

Automating GDPR Compliance: A Roadmap for Companies and Law Firms

GDPR compliance is more than checkboxes. A new roadmap from the Privatech project shows how automation and machine learning can help companies and law firms assess—and even generate—privacy compliance. * Shift the focus to data processors’ real workflows: drafting policies, mapping data uses, documenting decisions. * Break compliance into machine-ready

FPGAs for Faster, Leaner Deep Learning: A Review of CNN Accelerators

Deep learning drives image search, robots, and medical scans. Most systems lean on CPUs and GPUs. This review asks: what if we run convolutional neural networks (CNNs) on FPGAs—reconfigurable chips you can tailor to the model? * Why FPGAs: custom dataflows, low latency, and strong energy efficiency—great for cameras,

Dynamic-K: Recommendations That Know When to Stop

Most apps show a fixed number of “top” items—say 10 movies or 20 products—assuming there are always enough good options. But that’s not always true: sometimes there are few relevant items, or some users are extra picky. The result? Filler recommendations. Dynamic-K flips the script. Instead of

Teaching chatbots to stop contradicting themselves (DECODE)

Teaching chatbots to stop contradicting themselves Ever had a bot say one thing, then the opposite a few turns later? This study introduces DECODE—a new task and dataset for spotting contradictions in everyday conversations, drawn from both human-human and human-bot chats. * New data beats existing natural language inference (NLI)

Read more

Automating GDPR Compliance: A Roadmap for Companies and Law Firms

FPGAs for Faster, Leaner Deep Learning: A Review of CNN Accelerators

Dynamic-K: Recommendations That Know When to Stop

Teaching chatbots to stop contradicting themselves (DECODE)