Responses to Critiques on Machine Learning of Criminality Perceptions (Addendum of arXiv:1611.04135) · HackerPapers discussions

Responses to Critiques on Machine Learning of Criminality Perceptions (Addendum of arXiv:1611.04135) — mentioned by oldgradstudent

Sun, 21 Aug 2022 19:38:07 GMT

> The moment he stepped back on U.S. soil, he delivered a lecture titled “The Fallacy of Phrenology,” where he attacked the notion that head shape and size dictates the relative intelligence of different racial groups. But what if phrenology was based on machine learning? https://arxiv.org/abs/1611.04135v1 > Also, we find some discriminating structural features for predicting criminality, such as lip curvature, eye inner corner distance, and the so-called nose-mouth angle. /s

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Responses to Critiques on Machine Learning of Criminality Perceptions (Addendum of arXiv:1611.04135) — mentioned by dvfjsdhgfv

Thu, 08 Apr 2021 16:21:48 GMT

There is a sea of difference between the Chinese paper and the other two you mentioned. The Chinese were very straightforward: can we identify facial traits that are more present in criminals? To a certain degree, they answer positively by saying there is a greater variation in facial traits in criminals than in the rest of the population. Of course there was a lot of criticism of their methodology (to which they answered [0]). The first article you mentioned describes the bias of eyewitnesses related to stereotyped "criminal look". But the researchers are very clear: "Further research is…

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Responses to Critiques on Machine Learning of Criminality Perceptions (Addendum of arXiv:1611.04135) — mentioned by dvfjsdhgfv

Thu, 08 Apr 2021 11:52:15 GMT

I think the discussion about this already over, i.e. it's now a part of the social contract that we don't question certain things, not because they're true or false but because of social cost of challenging them. For example, we know that the distribution of certain psychological traits in both genders is a bit different, but discussing it - either in the social context or the academia - is more or less a taboo in order to protect the vulnerable, and in part also because of the expectations of desirable traits in today society [0]. For the same reason, nobody in the right mind would organize…

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Responses to Critiques on Machine Learning of Criminality Perceptions (Addendum of arXiv:1611.04135) — mentioned by tastroder

Wed, 06 May 2020 06:43:44 GMT

Related from 2016 "Responses to Critiques on Machine Learning of Criminality Perceptions" https://arxiv.org/abs/1611.04135v3

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Responses to Critiques on Machine Learning of Criminality Perceptions (Addendum of arXiv:1611.04135) — mentioned by gowld

Thu, 19 Dec 2019 22:49:59 GMT

boomark for next time a China hate-parade comes around: OP is treading the same water as https://arxiv.org/pdf/1611.04135v1.pdf "Automated Inference on Criminality using Face Images"

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Responses to Critiques on Machine Learning of Criminality Perceptions (Addendum of arXiv:1611.04135) — mentioned by mattkrause

Tue, 10 Dec 2019 19:45:35 GMT

Explain the mechanism, please. It seems wildly implausible to me that someone's facial features change (let alone in a predictable way) once they've committed a crime. However, I can easily imagine this "working" by keying off things like age/race/gender, which will get you a value that's "better than chance" but isn't, really. Ditto for differences in the photos; nobody smiles in a mugshot, after all. (And yes, I know about the paper purporting to show this [https://arxiv.org/pdf/1611.04135.pdf] I just think it's ridiculous)

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Responses to Critiques on Machine Learning of Criminality Perceptions (Addendum of arXiv:1611.04135) — mentioned by YeGoblynQueenne

Fri, 14 Jun 2019 18:04:24 GMT

Oh, this has been done before: Automated Inference on Criminality using Face Images, Wu & Zhang, 2016, Arxiv. https://arxiv.org/pdf/1611.04135v2.pdf Deep Neural Networks Are More Accurate Than Humans at Detecting Sexual Orientation From Facial Images, Kosinski & Wang, Journal of Personality and Social Psychology 2018. https://www.gsb.stanford.edu/faculty-research/publications/d... Scientists Have Created an AI That Judges You by Your Looks, futurism.com. https://futurism.com/scientists-have-created-an-ai-that-judg... When artificial intelligence judges a beauty contest, white people win…

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Responses to Critiques on Machine Learning of Criminality Perceptions (Addendum of arXiv:1611.04135) — mentioned by PorterDuff

Sat, 13 Apr 2019 23:07:17 GMT

https://arxiv.org/pdf/1611.04135v2.pdf

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Responses to Critiques on Machine Learning of Criminality Perceptions (Addendum of arXiv:1611.04135) — mentioned by TuringNYC

Thu, 22 Feb 2018 20:53:22 GMT

And then there is obscene "research" like this: https://arxiv.org/pdf/1611.04135v1.pdf "Automated Inference on Criminality using Face Images." How does stuff like this get past IRB?

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Responses to Critiques on Machine Learning of Criminality Perceptions (Addendum of arXiv:1611.04135) — mentioned by bitL

Sun, 24 Sep 2017 02:09:06 GMT

More coming from China: "Automated Inference on Criminality using Face Images" https://arxiv.org/pdf/1611.04135v2.pdf

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Responses to Critiques on Machine Learning of Criminality Perceptions (Addendum of arXiv:1611.04135) — mentioned by halflings

Fri, 08 Sep 2017 10:31:07 GMT

This article [0] discusses a paper by Chinese researchers [1] that did just that. (and yes, it is quite dumb/dangerous) [0] https://medium.com/@blaisea/physiognomys-new-clothes-f2d4b59... [1] https://arxiv.org/abs/1611.04135

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Responses to Critiques on Machine Learning of Criminality Perceptions (Addendum of arXiv:1611.04135) — mentioned by low_battery

Tue, 15 Aug 2017 10:50:01 GMT

Ah China and AI.. Just remembered this nice piece of paper: Automated Inference on Criminality using Face Images, https://arxiv.org/pdf/1611.04135v2.pdf At best they will become an Orwellian nightmare then they currently are, World domination? Please...

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Responses to Critiques on Machine Learning of Criminality Perceptions (Addendum of arXiv:1611.04135) — mentioned by panarky

Wed, 26 Apr 2017 17:21:49 GMT

As a proof of concept, we have downloaded the pictures of 7000 corporate executives whose LinkedIn profiles suggest they work for financial organizations, and then averaged their faces to produce generalized white collar criminal subjects unique to each high risk zone. Future efforts will allow us to predict white collar criminality through real-time facial analysis. I'm guessing that's a riff on this? https://arxiv.org/pdf/1611.04135v1.pdf (Automated Inference on Criminality using Face Images) Too bad there haven't been enough arrests of financial criminals to get actual mugshots.

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Responses to Critiques on Machine Learning of Criminality Perceptions (Addendum of arXiv:1611.04135) — mentioned by interpol_p

Mon, 12 Dec 2016 12:47:25 GMT

Here is a paper titled Automated Inference on Criminality using Face Images [0]. The system is trained on existing conviction records. Inheriting all the prejudices and biases of our current justice system. [0] https://arxiv.org/abs/1611.04135

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Responses to Critiques on Machine Learning of Criminality Perceptions (Addendum of arXiv:1611.04135) — HN submission

Fri, 18 Nov 2016 18:22:24 GMT

Automatic Racial Discrimination via Artificial Intelligence

15 points; 5 discussion replies on HN. Paper and source history. A submission is not an endorsement.

Responses to Critiques on Machine Learning of Criminality Perceptions (Addendum of arXiv:1611.04135) — HN submission

Fri, 18 Nov 2016 02:27:53 GMT

Automated Inference on Criminality Using Face Images

48 points; 31 discussion replies on HN. Paper and source history. A submission is not an endorsement.