Algorithms are increasingly integrated into various societal applications, often directly interacting with people and communities. This highlights the importance of understanding the interplay between algorithmic decisions and economic incentives when designing machine learning algorithms. In this talk, I will explore two examples of this dynamic through the lens of privacy in data markets and fairness in dynamic resource allocation.

The first part of the talk investigates a data marketplace involving users, platforms, and data buyers. Users benefit from platform services in exchange for data, incurring privacy loss when their data, albeit noisily, is shared with the buyer. The user chooses platforms to share data with, while platforms decide on data privacy levels and pricing before selling to the buyer. The buyer finally selects platforms to purchase data from. Using a multi-stage game-theoretic framework, I demonstrate how platform competition and buyer valuation shape user participation, platform viability, and overall welfare. I also discuss privacy regulatory interventions that can enhance user utility in mixed markets of high- and low-cost platforms.

The second part of the talk focuses on designing fair resource allocation algorithms through multi-round auctions, where an auctioneer sells indivisible goods to groups of buyers while adhering to group fairness constraints. I demonstrate that optimal mechanisms can be characterized using a dynamic programming approach and involve dynamic subsidization policies that balance revenue maximization with fairness guarantees. Additionally, I develop efficient approximations for computing these mechanisms, providing insights into the computational challenges of designing fair resource allocation algorithms.