Sui generis investigates the ideas of homophily, correlation and bias in machine learning models, as well the statistical projections they produce. Using LLMs, I created a personal classification algorithm that is able to predict (or not) how much I like or dislike a certain word. The artefact that resulted from this initial study is a “personalised” dictionary, where each word has a predicted numeric classification, representing my preferences from the model’s perspective. The installation space is designed to resemble a quasi-forensic or clinical scene, allowing visitors to examine the book using a digital microscope.

With Sui generis, I question the way other people’s preferences are embedded in these supposed personalised classifications. How is my identity being influenced by the homogenised entity built by the model?

materials
Book, digital microscope, table, stool, lab accessories