Pay With Your Data: Optimal Data-Sharing Mechanisms for AI Services

Problem definition: In this paper, we examine how firms offering AI services can effectively acquire large volumes of training data from their consumers to improve the accuracy of the machine learning (ML) models that drive these services. Since consumers often incur privacy costs when sharing sensitive information, it is essential to design data-sharing mechanisms that balance data acquisition needs with consumers’ privacy. Methodology/results: Inspired by practice, we examine two fundamentally distinct data-sharing mechanisms: manual data-sharing, where consumers control the amount of data they share, and algorithmic data-sharing, where the firm’s algorithm redacts sensitive segments of data before using it to train the ML model. We obtain revenue-maximizing mechanisms for each approach and compare their impact on firm revenue, consumer surplus, and the volume of data collected. Our analysis highlights the conditions under which each mechanism yields superior outcomes in terms of revenue and consumer surplus. Managerial implications: Based on the comparative performance of the two mechanisms, we provide managerial guidelines that help firms choose the preferred data-sharing mechanism for different types of AI services and consumer characteristics.