Title: Algorithmic Monoculture and Systemic Exclusion

Abstract: Mistakes are inevitable, but fortunately human mistakes are typically heterogenous. Using the same machine learning model for high stakes decisions creates consistency while amplifying the weaknesses, biases, and idiosyncrasies of the original model. When the same person re-encounters the same model or models trained on the same dataset, she might be wrongly rejected again and again. Thus algorithmic monoculture could lead to consistent ill-treatment of individual people by homogenizing the decision outcomes they experience.

Is it wrong to allow the quirks of an algorithmic system to consistently exclude a small number of people from consequential opportunities? Many philosophers have claimed or indicated in passing that consistent and arbitrary exclusion is wrong, even when it is divorced from bias or discrimination. But why and under what circumstances it is unfair has not yet been established. This talk will formalize a measure of outcome homogenization, describe experiments that demonstrate that it occurs, then present an ethical argument for why and in what circumstances outcome homogenization is unfair.