The cumulative evidence from academic research clearly shows that facial analysis, based on machine learning or direct human examination, can be used to predict different types of personal data directly from facial images of people, including age, gender, race 1, personality 2, names 3, social class 4, political orientation 5, propensity for aggressive behavior 6, and homosexuality 7, 8. The focus of this paper is on facial analysis. Facial analysis involves predicting individual data using statistical inference from the image itself and can be performed even on individuals one sees for the first time. Facial recognition involves matching a given photo to the one observed in the past, and then to a unique identifier for the photo’s owner, subsequently retrieving known information about the individual. Two distinct modern AI technologies can be applied to human facial images: facial recognition and facial analysis. Our proposed L1-regularized image decomposition method and other techniques point to smartphone camera artifacts, BMI, skin properties, and facial hair as top candidate non-demographic signals in facial images. Our unexpected finding of strong predictability of iPhone versus Galaxy preference variable shows how testing many hypotheses simultaneously can facilitate knowledge discovery. Adding facial images substantially boosts prediction quality versus demographics-only benchmark model. Using deep learning, we find 82/349 personal attributes (23%) are predictable better than random from facial image pixels. To address these limitations, we perform a megastudy-a survey-based study that reports the predictability of numerous personal attributes (349 binary variables) from 2646 distinct facial images of 969 individuals. Policy makers thus have an incomplete picture for a risk-benefit analysis of facial analysis technology. Another issue is selection bias: researchers may choose to study variables intuitively expected to be predictable and underreport unpredictable variables (the ‘file drawer’ problem). Reported prediction quality is hard to compare and generalize across studies due to different study conditions. While prior research has shown that facial images signal personal information, publications in this field tend to assess the predictability of a single variable or a small set of variables at a time, which is problematic.
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