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DrXaos t1_j7ia833 wrote

Reply to comment by keithcody in Researchers tested a large sample of the prominent major AI technologies available today and found not only did they reproduce human biases in the recognition of facial age, but they exaggerated those biases by giuliomagnifico

It's fairly well known that common ML systems for image processing (layers of convolutional networks followed by max-pooling or the like) are more sensitive to texture and less sensitive to larger scale shape and topology than humans.

It's likely that smiling triggered more 'wrinkle' detector units and the classifier eventually effectively added up the density of this texture detection for age prediction while humans know better where wrinkles from aging vs smiling are placed on the face and compensate.

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keithcody t1_j7idyma wrote

Your description doesn’t really fit the findings.

Sample image used for training

https://www.ncbi.nlm.nih.gov/pmc/articles/instance/9800363/bin/41598_2022_27009_Fig1_HTML.jpg

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DrXaos t1_j7ig8eq wrote

I guess I don't get your point. The images reflect the phenomenon I suggest.

Look at the younger images. In the smiling & young side there are more relatively high spatial frequency light to dark transitions, interpreted as a higher probability of wrinkles, vs the non-smiling side. I conjecture those contribute to higher age estimation.

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