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AI ML Post #5786 · source on Telegram

Developers react to AI image datasets discovered to contain illegal CSA content

Description

Dark-mode screenshot of an X/Twitter thread. At the top, user “James Galizio @Theswwet” posts: “This has aged like fine wine, goodbye to AI because holy shit.” An embedded 18 Nov tweet by “Please Log Off From This Hellsite” reads: “My guess: there is a significant amount of CSA present in an image pool that all of these companies are drawing upon and it was just recently discovered.” Metadata shows “5:57 PM · 20 Dec 23 · 11.2K Views” and interaction counts: 73 Reposts, 1 Quote, 285 Likes, 35 Bookmarks. Below, “Cen 🇦🇷 Queer Dragon-girl GF” asks, “what is ‘CSA’ referring to here?” and Galizio replies: “CP.” The exchange humorously exposes a serious AI/ML governance failure - illegal child sexual-abuse imagery contaminating public training datasets - highlighting data-curation, compliance, and ethical-safety concerns that senior engineers must address when deploying generative models

Comments

6
Anonymous ★ Top Pick Pro tip: if your “free to use” image corpus includes material that requires an FBI case number, your roadmap just pivoted from ‘fine-tune the model’ to ‘lawyer up & rm -rf *’
  1. Anonymous ★ Top Pick

    Pro tip: if your “free to use” image corpus includes material that requires an FBI case number, your roadmap just pivoted from ‘fine-tune the model’ to ‘lawyer up & rm -rf *’

  2. Anonymous

    When you realize your ML model's impressive zero-shot performance on edge cases wasn't actually a breakthrough in generalization, just the internet being the internet at scale

  3. Anonymous

    Turns out 'move fast and break things' has some pretty horrifying edge cases when your training dataset is 'the entire internet, no questions asked.' Who could have predicted that scraping billions of images without content validation would end badly? Oh right, literally anyone who's ever done data engineering. But hey, at least the VCs got their demos before the legal teams started sweating

  4. Anonymous

    Adversarial examples? Nah, the real attack vector was the uncurated priors in LAION-5B

  5. Anonymous

    When your dataset is basically “npm install internet,” SOTA turns into CYA - get a data SBOM before your model ships a subpoena

  6. Anonymous

    The cheapest guardrail is filtering at crawl time; the most expensive is outside counsel explaining your data lineage to a judge

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