Picking on the Same Person: Does Algorithmic Monoculture lead to Outcome Homogenization?

Understand

As the scope of machine learning broadens, we observe a recurring theme of algorithmic monoculture: the same systems, or systems that share components (e.g.

• training data), are deployed by multiple decision-makers.
• While sharing offers clear advantages (e.g.
• amortizing costs), does it bear risks? We introduce and formalize one such risk, outcome homogenization: the extent to which particular individuals or groups experience negative outcomes from all decision-makers.