Paper review: A Replication Study: Machine Learning (ML) Models Are Capable of Predicting Sexual Orientation From Facial Images by John Leuner
Objectives: The aim of this paper was to replicate previous studies that used ML to predict sexual orientation from facial images. Included was a new ML model based on highly blurred images to investigate whether the information present in the colours of the face and immediate background were predictive of sexual orientation. Head pose and the presence of facial hair or eyewear were investigated.
Results:
Replicating previous studies but with a new dataset not limited by country or race, both deep learning classifiers and facial morphology classifiers performed better than humans on photographs from dating profiles. A new ML model that tests whether a blurred image can be used to predict sexual orientation is introduced. Using predominant colour information present in the face and background, the author found this to be predictive of sexual orientation.
The author states that this study demonstrates that if someone intentionally alters their appearance to fit gay or straight stereotypes, the ML does not alter the sexual orientation label. Models are still able to predict sexual orientation even whilst controlling for the presence or absence of facial hair.
So What: A Chinese study (physiognomy) claims to be able to detect criminality from identity photographs, this type of research has serious legal and ethical implications. https://arxiv.org/pdf/1902.10739.pdf
