Where is the line with public data?
For this experiment I decided to tackle public data from a rather different angle. Through the making of this experiment I came across some interesting questions which I will highlight during the walk-through of the creation and documentation of this project. I collabrated with Anastasis Germanidis to create ‘Death Mask’.
Leon & Kenzo behind the data lens
Where is the line with when it comes to public data?
The concept behind this experiment is to predict the age of people captured from the camera and draw a representation of how long they have to live (if nothing goes wrong that is) in Augmented Reality. As dystopian as it may sound, this idea is based on some controversial machine learning research that claims to have ‘state-of-the-art’ prediction success rates in age prediction.
The project also serves as a commentary about the distinction between whats referred to as public data and user contributed information in the age of deep learning. Some of the questions that arose during the conceptualization phase were:
- Is there an inherent moral difference between normal statistical methods and deep learning when it comes to predicting personal information such as age?
- Since the deep learning approach was already trained on information (in this case public information), isn’t the prediction process considered public information too?
- Why are we (rough generalization, sorry) so sensitive to information when it is decrementing vs. incrementing? e.g people responded to this experiment a lot “better” when it was showing the age vs. when it was showing the age minus the life expectancy.
Creating the experiment
We started out by searching for a CoreML implementation of the AgeNet model. Thanks to this amazing repo, we were able to from a working demo of the machine learning prediction functionality.
From that point we designed the graphics around it, to feel like a mesh of a face was being displaced based on how long you still have to live.
To summarize this experiment we created a small video that demonstrates the app and it’s usage. Thanks to Scott Reitherman for the amazing track.