Of course I tried a bunch of pictures of myself, just as everybody at HN is doing, ha ha!
I didn't get above the nice level.
First picture of my wife she immediately got the godlike level. Unfair advantage!!
"Before performing any experiments we removed underage people, anyone over 37 and bi- and homosexual users as these comprise only a small minority of the dataset."
That's already biasing it of course!
"Interestingly, 44.81% of the male ratings are positive, while only 8.58% of the female ratings are positive. Due to this strong bias of ratings by women, we only predict the ratings of men."
So, the algorithm learns to rate me as a heterosexual man?
Yes, this was the data we worked with for attractiveness modeling. The algorithm learns from millions of ratings from males to females and from females to males, all heterosexuals. No underage or above 37 years old, and mostly people from Zurich area, Switzerland. For age and gender prediction we use more and diverse data.
The ratings are "like" (positive) and "dislike" (negative). If men have ratings close to uniform distribution (44.81% are "like" ratings, 55.19% are "dislike"), women with only 8.58% "like" ratings are clearly unbalanced, or if you want the women are "very picky". We call this a strong bias in the ratings of women while for men there is a small(er) bias and this only to point out the difference from the uniform distribution (50%-50%).
We can not tell and we are not the ones to judge if the men are voting randomly or are less picky or if the women are right or more picky, since the attractiveness is subjective and we do not have an ultimate ground truth to compare with. We can not say if the attractiveness should follow a particular distribution, we work empirically. In our study, the ratings of men tend to agree more and correlate more with our visual representation based on the face image (looks). We removed from our data the extreme cases such as users with less than 10 ratings or with too many ratings.
I didn't get above the nice level.
First picture of my wife she immediately got the godlike level. Unfair advantage!!
"Before performing any experiments we removed underage people, anyone over 37 and bi- and homosexual users as these comprise only a small minority of the dataset."
That's already biasing it of course!
"Interestingly, 44.81% of the male ratings are positive, while only 8.58% of the female ratings are positive. Due to this strong bias of ratings by women, we only predict the ratings of men."
So, the algorithm learns to rate me as a heterosexual man?
From: http://arxiv.org/pdf/1510.07867v1.pdf