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

Abstract

Rapid advances in Machine Learning (ML) have led to a proliferation of Artificial Intelligence (AI) services offered by firms. To develop a valuable AI service, a firm must build an accurate ML model which, in turn, requires a large amount of training data. Present-day firms usually obtain this data by offering incentives to consumers to share their data during the initial development phase of the AI service, and then use that data to retrain the ML models to improve the quality of the service. Consumers, on the other hand, incur privacy costs for sharing their data. In this paper, we analyze two popular data-sharing mechanisms that firms employ in practice: manual data-sharing and algorithmic data-sharing. In the former approach, consumers decide the amount of data to share with the firm, whereas in the latter, the firm uses algorithmic data redaction to identify and censor sensitive segments of data, and determine the amount of data collected from consumers. For both the data-sharing approaches, we obtain revenue-maximizing mechanisms for the firm and analyze the fundamental differences between the two approaches in terms of the revenue accrued by the firm, the consumer surplus, and the volume of data collected. 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.

Keywords: data-sharing mechanisms, artificial intelligence services, mechanism design

JEL Classification: D47, D40