Data is the lifeblood of machine learning, yet for many firms, obtaining datasets of sufficient quality remains a challenge, with them being naturally distributed amongst owners with heterogeneous characteristics (e.g., privacy preferences). This has motivated several developments in the field of collaborative analytics, also known as federated learning (Figure 1a), where models are trained on local servers without the need for data centralization, thereby preserving privacy and distributing the computational burden (Kairouz et al., 2019). However, this framework provides only an incentive-free means for data sharing, relying on the critical assumption that owners are willing to collaborate (i.e., by sharing their private information) altruistically. This rather strong assumption may be violated if owners are competitors in a downstream market environment (Gal-Or, 1985). Consequently, a fruitful area of research has emerged that proposes to instead commoditize data within a market-based framework, where compensation (e.g., remuneration) can be used as an incentive for collaboration (Bergemann and Bonatti, 2019).

Today, the practical synergies between economics and computer science are flourishing. We outline some of the many opportunities for the two disciplines to engage more deeply through the new MIT Schwarzman College of Computing." Nancy L. Rose is the Charles P. Kindleberger Professor of Applied Economics and head of the MIT Department of Economics, where her research and teaching focus on industrial organization, competition policy, and the economics of regulation. David Autor is the Ford Professor of Economics and co-director of the MIT Task Force on the Work of the Future. His scholarship explores the labor market impacts of technological change and globalization, earnings inequality, and disability insurance and labor supply.

The next decade will see an abundance of new intelligent systems, many of which will be market-based. Soon, users will interact with many new markets, perhaps without even knowing it: when driving their car, when listening to a song, when backing up their files, or when surfing the web. We argue that these new systems can only be successful if a new approach is chosen towards designing them. In this paper we introduce the general problem of "Hidden Market Design." The design of a "weakly hidden" market involves reducing some of the market complexities and providing a user interface (UI) that makes the interaction seamless for the user. A "strongly hidden market" is one where some semantic aspect of a market is hidden altogether (e.g., budgets, prices, combinatorial constraints). We show that the intersection of UI design and market design is of particular importance for this research agenda. To illustrate hidden market design, we give a series of potential applications. We hope that the problem of hidden market design will inspire other researchers and lead to new research in this direction, paving the way for more successful market-based systems in the future.