Algorithmic Monoculture and Social Welfare

TL;DR

This paper demonstrates that widespread reliance on a single algorithm by multiple decision-makers can decrease overall decision quality, even when the algorithm is individually accurate, due to systemic monoculture risks.

Contribution

It introduces a probabilistic framework to analyze how algorithmic monoculture impacts collective decision accuracy, revealing deeper risks beyond unexpected shocks.

Findings

Monoculture can reduce decision quality even under normal conditions.

Reliance on a single algorithm can harm accuracy despite its individual precision.

The framework applies to systems with multiple noisy estimates.

Abstract

As algorithms are increasingly applied to screen applicants for high-stakes decisions in employment, lending, and other domains, concerns have been raised about the effects of algorithmic monoculture, in which many decision-makers all rely on the same algorithm. This concern invokes analogies to agriculture, where a monocultural system runs the risk of severe harm from unexpected shocks. Here we show that the dangers of algorithmic monoculture run much deeper, in that monocultural convergence on a single algorithm by a group of decision-making agents, even when the algorithm is more accurate for any one agent in isolation, can reduce the overall quality of the decisions being made by the full collection of agents. Unexpected shocks are therefore not needed to expose the risks of monoculture; it can hurt accuracy even under "normal" operations, and even for algorithms that are more accurate when used by only a single decision-maker. Our results rely on minimal assumptions, and involve the development of a probabilistic framework for analyzing systems that use multiple noisy estimates of a set of alternatives.