When working with generative artificial intelligence (AI), users may see productivity gains, but content generated with the help of AI may not match their preferences exactly. The boost in productivity may come at the expense of users' idiosyncrasies, such as personal style and tastes, preferences we would naturally express without AI. To let users express their preferences, many AI systems let users edit their prompt (e.g., Midjourney) or allow more natural interactions (e.g., ChatGPT), and users can always review and edit the AI-generated output themselves. However, aligning a user's intentions with an AI's output can take time and may not always be worth it if the AI's first or default output "does the job." In short, users face a trade-off between AI output fidelity and communication cost. The purpose of this work is to examine the impact of this human-AI interaction on the AI-generated content we produce as a society.

Nonprofit fundraising websites often display a set of donation amounts, allowing prospective donors to effortlessly select an amount from this menu of suggestions instead of manually inputting their ideal donation. Although this strategy is effective at shaping behavior, it can also backfire: suggested amounts ("defaults") attract donors with both lower and higher ideal donations, potentially leading to a net decrease in revenue. To address this challenge, we present a comprehensive framework for designing a menu of defaults to maximize fundraising revenue in the presence of heterogeneous donors.

We study the matching of jobs to workers waiting in a queue, for example a ridesharing platform dispatching drivers to pick up riders at an airport. Under FIFO dispatching, the heterogeneity in earnings from different trips incentivizes drivers to cherrypick, increasing riders' waiting times for a match, and resulting in poor reliability for riders, low average earnings for drivers, and a loss of throughput and revenue for the platform. Simple fixes by limiting dispatching transparency or drivers' flexibility are neither desirable nor fully effective. Optimal origin-destination based prices are incentive aligned in theory, but are hard to implement in practice due to operational constraints.

We study the classic sequential screening problem under ex-post participation constraints. Thus the seller is required to satisfy buyers' ex-post participation constraints. A leading example is the online display advertising market, in which publishers frequently cannot use up-front fees and instead use transaction-contingent fees.