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hawkeye224 t1_j7hmro2 wrote

Why is it biased? I would imagine the training data would be photos of faces annotated with their actual age.. where is the bias introduced?

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keithcody t1_j7ht0sc wrote

It’s in the article if you want to read it

“In the study, AI overestimated the age of smiling faces even more than human observers and showed a sharper decrease in accuracy for faces of older adults compared to faces of younger age groups, for smiling compared to neutral faces, and for female compared to male faces. “These results suggest that estimates of age from faces are largely driven by visual cues, rather than high-level preconceptions,” said lead author Tzvi Ganel, Ben-Gurion, department of cognitive and brain sciences. “The pattern of errors and biases we observed could provide some insights for the design of more effective AI technology for age estimation from faces.

…

“AI tended to exaggerate the aging effect of smiling for the faces of young adults, incorrectly estimating their age by as much as two and a half years. Interestingly, whereas in human observers, the aging effect of smiling is missing for middle-aged adult female faces, it was present in the AI systems,” said Carmel Sofer, Ben-Gurion, department of cognitive and brain sciences.”

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DrXaos t1_j7ia833 wrote

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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