To Err is Algorithm: Algorithmic fallibility and economic organisation

Juan Mateos-Garcia

No xuvf9, SocArXiv from Center for Open Science

Abstract: Algorithmic decision-making systems based on artificial intelligence and machine learning are enabling unprecedented levels of personalisation, recommendation and matching. Unfortunately, these systems are fallible, and their failures have costs. I develop a formal model of algorithmic decision-making and its supervision to explore the trade- offs between more (algorithm-facilitated) beneficial deci- sions and more (algorithm-caused) costly errors. The model highlights the importance of algorithm accuracy and human supervision in high-stakes environments where the costs of error are high, and shows how decreasing returns to scale in algorithmic accuracy, increasing incentives to ’game’ popular algorithms, and cost inflation in human supervision might constrain optimal levels of algorithmic decision-making.

Date: 2017-10-17
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Persistent link: https://EconPapers.repec.org/RePEc:osf:socarx:xuvf9

DOI: 10.31219/osf.io/xuvf9

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