This article has been reviewed according to Science X's editorial process and policies. Editors have highlighted the following attributes while ensuring the content's credibility: AI tools are increasingly replacing human judgments in some settings. For instance, resume-screening algorithms are often used in hiring, where they may improve efficiency and consistency in decision-making.
But some scholars have raised concerns that the adoption of automated systems could eventually result in one algorithm being used to make all decisions in a particular industry. They worry that so-called algorithmic monoculture could have negative consequences. For example, in hiring, the thinking goes that algorithmic monoculture might result in systematic exclusion—a situation in which a job candidate rejected by one firm's algorithm would likely also be rejected by every other firm's algorithm.
However, MIT researchers now argue that algorithmic monoculture may not always be as bad as some scientists have suggested. They systematically evaluated major objections to algorithmic monoculture, including systematic exclusion, and concluded that this and many other arguments either fail or aren't decisive against all forms of monoculture. Instead, they show mathematically that monoculture tends to create informational echo chambers that can hinder exploration.
In hiring, this could make it less likely that the best candidates would get jobs. However, bundling various hiring algorithms into a single "ensemble" can overcome this limitation, the researchers show. This could sometimes enable monoculture to perform as well as, if not better than, a polyculture in which different firms use different algorithms.
"A trend toward algorithmic monoculture is a realistic scenario, and a really important issue that is being brought about by the use of AI, but it is hard to say in the abstract whether monoculture would be a bad thing. It depends on the details, like the domain we are talking about and the accuracy of the algorithm itself," says study co-author Brian Hedden, a professor in the Department of Linguistics and Philosophy who holds an MIT Schwarzman College of Computing shared position with the Department of Electrical Engineering and Computer Science (EECS) and is also a principal investigator in the Laboratory for Information and Decision Systems (LIDS). Hedden is joined on the paper by co-author Manish Raghavan, the Drew Houston (2005) Career Development Professor at the MIT Sloan School of Management and in EECS, as well as a LIDS principal investigator.
The research appears in Philosophical Perspectives. Algorithmic monoculture, in which all decisions across a certain domain are made using the same algorithm, is not a new phenomenon. For instance, lending decisions were once made by independent bankers at individual banks, but now all bankers use the same information based on a borrower's standardized credit scores, which are derived from the Fair Isaac Corporation (FICO) algorithm.
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