I felt firty using them, but also felt that revealing what was going on was more important. I downloaded all the BMI prediction projects I could find on Github, and was astonished to find some of them came with vast troves of photographs. I actually trained the BMI prediction algorithm myself because I couldn't find any existing models that were small enough to use online. Also, FaceApiJS is bad at detecting " Asian guys". Do note that its developer doesn't fully divulge on which photos the models were trained. The models to predict age, gender and facial expression/emotion are part of FaceApiJS, which forms the backbone of this project. The beauty scoring model was found on Github ( this or this one). And then you have the parties who just implement whatever they can get their hands on, and hope nobody asks difficult questions. Instead, they use third parties that supply machine learning services, and it's in the interest of these parties to keep their systems as generic and "one size fits all" as possible. This was done to make a point: we often say we should improve biased and/or error-prone machine learning models, but the reality is that most organisations don't train their own models. Machine Learning modelsĪlmost all the machine learning models used were downloaded "pre-trained" from open source projects I found on Github.
I prefer to use the terms "machine learning" or "statistics on steroids", but I've settled on algorithms here.
This project purposefully avoids using the word "Artificial Intelligence", since there is nothing intelligent about these systems.
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