Contracting, pricing, and data collection under the AI flywheel effect

Huseyin Gurkan and Francis de Véricourt
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Huseyin Gurkan: ESMT European School of Management and Technology
Francis de Véricourt: ESMT European School of Management and Technology

ESMT Research Working Papers from ESMT European School of Management and Technology

Abstract: This paper explores how firms that lack expertise in machine learning (ML) can leverage the so-called AI Flywheel effect. This effect designates a virtuous cycle by which, as an ML product is adopted and new user data are fed back to the algorithm, the product improves, enabling further adoptions. However, managing this feedback loop is difficult, especially when the algorithm is contracted out. Indeed, the additional data that the AI Flywheel effect generates may change the provider's incentives to improve the algorithm over time. We formalize this problem in a simple two-period moral hazard framework that captures the main dynamics among ML, data acquisition, pricing, and contracting. We find that the firm's decisions crucially depend on how the amount of data on which the machine is trained interacts with the provider's effort. If this effort has a more (less) significant impact on accuracy for larger volumes of data, the firm underprices (overprices) the product. Interestingly, these distortions sometimes improve social welfare, which accounts for the customer surplus and profits of both the firm and provider. Further, the interaction between incentive issues and the positive externalities of the AI Flywheel effect has important implications for the firm's data collection strategy. In particular, the firm can boost its profit by increasing the product's capacity to acquire usage data only up to a certain level. If the product collects too much data per user, the firm's profit may actually decrease, i.e., more data is not necessarily better.

Keywords: Data; machine learning; data product; pricing; incentives; contracting (search for similar items in EconPapers)
Date: 2020-03-03, Revised 2021-08-17
New Economics Papers: this item is included in nep-bec, nep-big, nep-cmp and nep-isf
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http://static.esmt.org/publications/workingpapers/ESMT-20-01.pdf First version, 2020 (application/pdf)
http://static.esmt.org/publications/workingpapers/ESMT-20-01_R3.pdf Revised version, 2021 (application/pdf)

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Persistent link: https://EconPapers.repec.org/RePEc:esm:wpaper:esmt-20-01_r3

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