PedoAI
www.uncorrelated.xyz
Introducing PedoAI. The first deep learning driven physiognomy model built to distinguish predatory pedophilia, created from a dataset of 1.2 million criminals

While this linear model’s overall predictive accuracy barely outperformed random guessing,

I was tempted to write this up for Pivot but fuck giving that blog any sort of publicity.

the rest of the site is a stupendous assortment of a very small field of focus that made this ideal for sneerclub and not just techtakes

  • @blakestacey@awful.systemsMEnglish
    31•26 days ago

    Hashemi and Hall (2020) published research demonstrating that convolutional neural networks could distinguish between “criminal” and “non-criminal” facial images with a reported accuracy of 97% on their test set. While this paper was later retracted for ethical concerns rather than methodological flaws,

    That’s not really a sentence that should begin with “While”, now, is it?

    it highlighted the potential for facial analysis to extend beyond physical attributes into behavior prediction.

    What the fuck is wrong with you?

    • @swlabr@awful.systemsEnglish
      18•25 days ago

      it highlighted the potential for facial analysis to extend beyond physical attributes into behavior prediction.

      bouba/kiki prison industrial complex

    • David GerardOPMEnglish
      12•25 days ago

      What the fuck is wrong with you?

      the blog tagline is “Dysgenics, forecasting, machine learning, sociology, physiognomy, IQ, simulations”, so he tells us straight up what’s wrong with him

    • @Soyweiser@awful.systemsEnglish
      11•25 days ago

      The implication here that it isnt methodically flawed is quite something.

      E: and I don’t have the inclination for to do the math, but a 97% accuracy seems to be on the unusable side considering the rate of ‘criminals’ vs not-criminals in the population. (Yeah, see also ‘wtf even is a criminal’).