Abstract: Algorithmic monocultures occur when many decision-makers use the same predictive system. A monoculture ensures consistency, but also amplifies the weaknesses, biases, and idiosyncrasies of the original predictive system. Algorithmic monoculture can lead to consistent ill-treatment of individual people by homogenizing the decision outcomes they experience. When the same person re-encounters the same or similar models, she might be wrongly rejected again and again. We propose stochastic procedures that reduce the homogeneity of decision outcomes by relying on . In doing so, we take a step back from the predictive performance or fairness of individual models to focus instead on the fairness of the decision ecosystem as a whole.
Bio: Dr. Katie Creel