Sui generis stems from my recent research on how Artificial Intelligence algorithms deprive subjects of their individuality and their ability to grow. It investigates the ideas of homophily, correlation and bias in Machine Learning models, as well the statistical projections they produce. The artefact that resulted from this initial study is a “personalised” dictionary. In this dictionary each word receives, instead of a meaning, a classification between 0 and 100 predicted by a Machine Learning model that I trained on my own preferences.

With Sui generis, I question how this process transforms the notion of individuality by reducing human complexity to mere statistical abstractions. How do the preferences of others influence classifications that claim to be personalised? In what way is my identity dictated by the homogenised entity generated by the model?

materials
Book, digital microscope, table, stool, gloves