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Preference elicitation leverages AI or optimization to learn stakeholderpreferences in settings ranging from marketing to public policy. The onlinerobust preference elicitation procedure of arXiv:2003.01899 has been shown insimulation to outperform various other elicitation procedures in terms ofeffectively learning individuals' true utilities. However, as with anysimulation, the method makes a series of assumptions that cannot easily beverified to hold true beyond simulation. Thus, we propose to validate therobust method's performance using real users, focusing on the particularchallenge of selecting policies for prioritizing COVID-19 patients for scarcehospital resources during the pandemic. To this end, we develop an onlineplatform for preference elicitation where users report their preferencesbetween alternatives over a moderate number of pairwise comparisons chosen by aparticular elicitation procedure. We recruit 193 Amazon Mechanical Turk (MTurk)workers to report their preferences and demonstrate that the robust methodoutperforms asking random queries by 21%, the next best performing method inthe simulated results of arXiv:2003.01899, in terms of recommending policieswith a higher utility.

Deploying a Robust Active Preference Elicitation Algorithm on MTurk:  Experiment Design, Interface, and Evaluation for COVID-19 Patient  Prioritization