There is no machine learning involved here, it's just a lookup in a table of life expectancies per country, the average BMI in that country, and then a calculation on how BMI might affect that. After exploring how these worked, I created my own wishy-washy implementation. There are a lof of projects on Github that explore this idea, some of them as part of (Kaggle) contests set-up by the insurance industry. I then changed the video to say "researchers who work at Google" intead of "Google's health lab in India".įor more juicy details I refer you to the in-depth blogpost mentioned earlier. Although all signs point to this project being a part of Google's practise, I sent an email to the makers to verify this, and got a quick response that it was a personal project. The BMI prediction project that was created by researchers who work at Google can be found here (not anymore, they deleted it!). The picture was downloaded form their website.Ĭheck out This article if you want to understand why an algorithm that tries to sort people into just two categories can upset some people.
Their practise of showing content from beautiful people is explained here. TikTok (front page, recorded with a screen recorder).Other direct sources were used under the artistic, journalistic and educational copyright exception. To the photographers who so kindly shared their work: thank you all for your wonderful generosity! The videos were mainly constructed out of public domain source material from and. Videos, screenshots and other visual material I've documented some of the dodgy things I discovered along the way in this blogpost. 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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