Most existing works require agents to receive high-quality supervision signals, e.g., reward or

Recent work used mathematical programming, which is computationally slow.

Motivated by the high discard rate of donated organs in the United States, we study an allocation problem in the presence of learning and strategic incentives. We consider a setting where a benevolent social planner decides whether and how to allocate a single indivisible object to a queue of strategic agents. The object has a common true quality, good or bad, which is ex-ante unknown to everyone. Each agent holds an informative, yet noisy, private signal about the quality.

Interpretable and explainable machine learning has seen a recent surge of interest.