those viral 'AI rates your face' tools are mostly mid, here's the methodology problem

[GonialGod]

everyone keeps posting their score from some AI face rater and either coping or rejoicing. as someone who actually cares about measurement, let me explain why most of them are noise.

1. training data bias: most are trained on labeled 'attractiveness' datasets that are small, western-skewed and inconsistently rated. garbage in, garbage out.
2. photo dependence: lighting, angle, camera lens (focal length distorts faces hard), and expression swing the score more than your actual face does.
3. they collapse a multidimensional thing (harmony, dimorphism, skin, etc) into one number, which is reductive.

that said, the BETTER ones are genuinely useful as a consistent mirror IF you control the input: same neutral lighting, same neutral expression, multiple angles. used that way (e.g. running the same standardized photo over time) the score becomes a decent progress tracker even if the absolute number is arbitrary. lookizm and a couple others are fine for that. just dont treat any single AI number as gospel, control your variables.

 

[ascendingmanlet]

everyone keeps posting their score from some AI face rater and either coping or rejoicing. as someone who actually cares about measurement, let me explain why most of them are noise. 1. training data bias: most are trained on labeled 'attractiveness' datasets that are small, west

i got a 4.2 on one of those and almost cried. then a different one gave me 6. so theyre just random??

 

[GonialGod]

i got a 4.2 on one of those and almost cried. then a different one gave me 6. so theyre just random??

not random, but extremely sensitive to your input photo and the model. you probably fed one a bad-lighting selfie and one a decent photo. standardize the photo and the spread shrinks. and dont anchor on the absolute number, track the trend.

 

[TyroneTier]

everyone keeps posting their score from some AI face rater and either coping or rejoicing. as someone who actually cares about measurement, let me explain why most of them are noise. 1. training data bias: most are trained on labeled 'attractiveness' datasets that are small, west

i scored gigachad on all of them. working as intended

 

[DoctorPSL]

everyone keeps posting their score from some AI face rater and either coping or rejoicing. as someone who actually cares about measurement, let me explain why most of them are noise. 1. training data bias: most are trained on labeled 'attractiveness' datasets that are small, west

the progress-tracking use case is the legit one. as a consistent measuring stick with controlled input theyre actually handy. as an oracle that tells you your worth, theyre poison. tool not a verdict.

 

[doomerNEET]

everyone keeps posting their score from some AI face rater and either coping or rejoicing. as someone who actually cares about measurement, let me explain why most of them are noise. 1. training data bias: most are trained on labeled 'attractiveness' datasets that are small, west

fed it a pic of my cat and got a 7. higher than my own score. the machines have spoken its over for me