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To Err is Algorithm: Algorithmic fallibility and economic organisation

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  • Mateos-Garcia, Juan

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.

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  • Mateos-Garcia, Juan, 2017. "To Err is Algorithm: Algorithmic fallibility and economic organisation," SocArXiv xuvf9, Center for Open Science.
    • Handle: RePEc:osf:socarx:xuvf9
    DOI: 10.31219/osf.io/xuvf9
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    References listed on IDEAS

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