Contracting, Pricing, and Data Collection Under the AI Flywheel Effect
研究了缺乏机器学习专长的企业如何利用AI飞轮效应，在将算法外包时通过合同、定价和数据收集策略管理激励问题，发现数据量对算法改进的影响方向决定了定价扭曲方向，且数据收集并非越多越好。
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 are not necessarily better). This paper was accepted by Jayashankar Swaminathan, operations management. Supplemental Material: The data files and e-companion are available at https://doi.org/10.1287/mnsc.2022.4333 .