Algorithmic monoculture and social welfare
Abstract
Algorithmic monoculture is a growing concern in the use of algorithms for high-stakes screening decisions in areas such as employment and lending. If many firms use the same algorithm, even if it is more accurate than the alternatives, the resulting “monoculture” may be susceptible to correlated failures, much as a monocultural system is in biological settings. To investigate this concern, we develop a model of selection under monoculture. We find that even without any assumption of shocks or correlated failures—i.e., under “normal operations”—the quality of decisions may decrease when multiple firms use the same algorithm. Thus, the introduction of a more accurate algorithm may decrease social welfare—a kind of “Braess’ paradox” for algorithmic decision-making.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- May 25, 2021
- Source ID
- 10.1073/pnas.2018340118
Entities
People
- Jon Kleinberg
- Manish Raghavan
Organizations
- Air Force Office of Scientific Research
- Army Research Office
- Cornell University
- John D. and Catherine T. MacArthur Foundation
- Microsoft Research
- Office of Naval Research
- Simons Foundation