Algorithmic Monoculture and its Critics
This addresses concerns for policymakers and scholars about the risks of widespread algorithmic decision-making, but it is incremental as it reassesses existing critiques rather than introducing new solutions.
The paper tackles the problem of algorithmic monoculture, where a single algorithm is used across domains like hiring and lending, by systematically evaluating objections to it, concluding that monoculture is less problematic than critics claim, with many objections failing or not being decisive.
Algorithmic decision-making is replacing idiosyncratic human judgment in domains such as hiring, lending, and criminal justice. This shift promises increased consistency, but many scholars worry that it can go too far. They warn of the dangers of algorithmic monoculture, in which all decisions across a domain are made using a single algorithm. We systematically evaluate a range of objections to monoculture, formalizing and rigorously assessing familiar critiques alongside novel ones. These objections concern systematic exclusion, agency and gaming, and information aggregation and exploration. We conclude that monoculture is less problematic than its critics have supposed: commonly cited objections fail, and while other objections have some force, they are not decisive against monoculture in general.