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
On a related note, one of my kids learnt about how phrenology was once used for scientific racism and my other kid was shocked, dismayed and didn't want to believe it. So I had to confirm that yes people did that, yes it was very racist, and yes they considered themselves scientists and were viewed as such by the scientific community of the time.
I didn't inform them that phrenology and scientific racism is still with us. There is a limit on how many illusions you want to break in a day.
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.
the blog tagline is "Dysgenics, forecasting, machine learning, sociology, physiognomy, IQ, simulations", so he tells us straight up what's wrong with him
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').
about 3 in 100 americans are in prison, on parole, etc. so if that's the definition of a criminal, you would get 97% accuracy by just guessing not criminal every time
The war on weird looking people continues. (The false positive/negative rate of this bs is immense. Wait a 69% succes rate? Ow god the false positives on that are going to be immense (even worse, the model works worse than random chance on a online game dataset, and then also the statistical uselessness of 69% due to low amount of pedos in general public isn't even mentioned in the conclusions, toss this where it belongs, in the dustbin of history).
Interesting study, but I am skeptical that this result applies to the general population (without the "convicted" qualifier).
If non-whites are more violently criminal than whites, then we can expect them to be imprisoned earlier in life for any violent crime, of which pedophilia will be a small subset.
So we have more convicted white paedos because ....... the coloreds do more crimes??! what in the actual fuck did I just read?
Don't worry, the people who would go and accuse you of being a pedophile would do so with or without this tool. It would just give them faux legitimacy.
E: post + profile picture was a lol moment however.
Could be an SSC type situation: you write an interminable pretend research post in a superficially serious manner on an obviously flawed premise and let the algorithm help it find its audience of mostly people who won't read it but will be left with the impression that the premise is at least defensible.
This will be made considerably easier once siskind puts it in his regular link roundup with a cheeky comment about how he doesn't really truly endorse this sort of thing.
I was tempted to write this up for Pivot but fuck giving that blog any sort of publicity.
On the one hand, I can see you not wanting to give the fucker attention, on the other hand, AI's indelible link to fascism is something which needs to be hammered home and shit like this gives you a golden opportunity to do it.
Current “artificial general intelligence” researchers have a repeated habit of using a definition of “intelligence” from psychologist and ardent race scientist Linda Gottfredson. The definition looks innocuous, but was from Gottfredson’s 1994 Wall Street Journal op-ed, “Mainstream Science on Intelligence,” a farrago of race science put forward as a defense of Charles Murray’s book The Bell Curve — signed off by 52 other race scientists, 20 of whom were from the Pioneer Fund.
Gottfredson’s piece was cited in Shane Legg’s Ph.D dissertation “Machine Super Intelligence,” in which he called it “an especially interesting definition as it was given as part of a group statement signed by 52 experts in the field” and that it therefore represented “a mainstream perspective” — an odd way to refer to Pioneer Fund race scientists. Somehow, this passed Legg’s dissertation committee.
The definition made it from Legg’s Ph.D into Microsoft and OpenAI’s “Sparks of AGI” paper, and from there to everyone else who copies citations to fill out their bibliography. When called out on this, Microsoft did finally remove the citation.
Currently the comments section has one thread and you can probably guess what it’s about. (Hint: the post concludes something about the average SI pedophile)