<instructions>Your goal is to analyze the following comment and estimate how highly it will be upvoted by the Hacker News community.</instructions><comment><author>aab0</author><text>It could also reflect what is most distinguishable. Which is easier for a NN to confidently distinguish: black pubic hair on black skin, or black pubic hair on white skin? Darker nipples on black skin, or darker nipples on white skin? etc You&amp;#x27;re doing gradient ascent on confidence of classification, not simply trying to find a plausible input, but the &lt;i&gt;maximal&lt;/i&gt; input. There&amp;#x27;s no reason to expect this to be racially unbiased as there are simple objective reasons that higher contrast would be useful. (Similarly, I would not be surprised if a face recognition NN worked better on Europeans rather than Chinese, for the prima facie reason that they have more variable facial features and other aspects like multiple hair colors other than black.)&lt;p&gt;And since Yahoo needs to detect porn of all races and there&amp;#x27;s plenty of black porn out there, it would be odd if their porn detector had such a huge gaping hole in it.</text><parent_chain><item><author>niftich</author><text>This is absolutely fascinating.&lt;p&gt;It&amp;#x27;s mesmerizing to see this NSFW detection applied in reverse, and it&amp;#x27;s even more interesting to observe your mind react to the generated images. You can see the sort-of-&lt;i&gt;mons pubis&lt;/i&gt; patterns, the maybe-pubic hair, the perhaps-breasts and the suspiciously phallic appendages, complete with realistic colors.&lt;p&gt;Interestingly, all exposed skin suggests that the training dataset for the NSFW detection was skewed towards caucasians, given how the synthesized images are near-completely devoid of skin tones other than light pink. Perhaps this is a good visual indication of unintentional &amp;#x27;bias&amp;#x27; in datasets?</text></item></parent_chain></comment><story><title>Image Synthesis from Yahoo&apos;s open_nsfw</title><url>https://open_nsfw.gitlab.io/</url></story>
<instructions>Your goal is to analyze the following comment and estimate how highly it will be upvoted by the Hacker News community.</instructions><comment><author>pbhjpbhj</author><text>Re your last point:&lt;p&gt;Strong pink is probably the most common colour across all people for the most intimate parts of the genitals as AFAIK the inner folds and spread appearance of all types of vulva are pink. All tongues and palms are pink too [AFAIK], they probably feature highly in porn compared to in other images. If you were then looking at pictures of black women, say, and wanted to find the porn images then looking for certain pinks in those images would be a good first pass I feel [you&amp;#x27;d probably get lots of false-positives for mouth pictures]. Even those with brown and black skin can flush pink when aroused too, that&amp;#x27;s got to feed in to the results. [Do men have pigmented &lt;i&gt;glans penis&lt;/i&gt;? Not something I&amp;#x27;ve ever really needed to know. [2]]&lt;p&gt;FWIW &lt;a href=&quot;https:&amp;#x2F;&amp;#x2F;open_nsfw.gitlab.io&amp;#x2F;nsfw&amp;#x2F;prob_0001_1000_10.0_0.04__144.jpg&quot; rel=&quot;nofollow&quot;&gt;https:&amp;#x2F;&amp;#x2F;open_nsfw.gitlab.io&amp;#x2F;nsfw&amp;#x2F;prob_0001_1000_10.0_0.04__1...&lt;/a&gt; doesn&amp;#x27;t look especially &amp;quot;caucasian&amp;quot; (ie white European) to me. There seems to be a good mix of pretty coffee-y colours to me that I&amp;#x27;d associate with Asia.&lt;p&gt;&amp;gt;Perhaps this is a good visual indication of unintentional &amp;#x27;bias&amp;#x27; in datasets? &amp;#x2F;&amp;#x2F;&lt;p&gt;So, maybe it&amp;#x27;s a colour-of-genitals bias rather than a dataset bias?&lt;p&gt;[1] &lt;a href=&quot;http:&amp;#x2F;&amp;#x2F;www.iflscience.com&amp;#x2F;health-and-medicine&amp;#x2F;why-are-your-nipples-and-genitals-different-color-rest-your-skin-0&amp;#x2F;&quot; rel=&quot;nofollow&quot;&gt;http:&amp;#x2F;&amp;#x2F;www.iflscience.com&amp;#x2F;health-and-medicine&amp;#x2F;why-are-your-n...&lt;/a&gt;&lt;p&gt;[2] &lt;a href=&quot;http:&amp;#x2F;&amp;#x2F;www.interestingarticles.com&amp;#x2F;mens-health-issues&amp;#x2F;penis-skin-of-a-different-color-whats-normal-and-whats-not-5948.html&quot; rel=&quot;nofollow&quot;&gt;http:&amp;#x2F;&amp;#x2F;www.interestingarticles.com&amp;#x2F;mens-health-issues&amp;#x2F;penis-...&lt;/a&gt;</text><parent_chain><item><author>niftich</author><text>This is absolutely fascinating.&lt;p&gt;It&amp;#x27;s mesmerizing to see this NSFW detection applied in reverse, and it&amp;#x27;s even more interesting to observe your mind react to the generated images. You can see the sort-of-&lt;i&gt;mons pubis&lt;/i&gt; patterns, the maybe-pubic hair, the perhaps-breasts and the suspiciously phallic appendages, complete with realistic colors.&lt;p&gt;Interestingly, all exposed skin suggests that the training dataset for the NSFW detection was skewed towards caucasians, given how the synthesized images are near-completely devoid of skin tones other than light pink. Perhaps this is a good visual indication of unintentional &amp;#x27;bias&amp;#x27; in datasets?</text></item></parent_chain></comment><story><title>Image Synthesis from Yahoo&apos;s open_nsfw</title><url>https://open_nsfw.gitlab.io/</url></story>