|Preference Elicitation and Aggregation to Aid with Patient Triage during the COVID-19 Pandemic||268 KB|
During the COVID-19 pandemic, committees have been appointed to make ethically difficult triage decisions, which are complicated by the diversity of stakeholder interests involved. We propose a disciplined, automated approach to support such difficult collective decision-making. Our system aims to recommend a policy to the group that strikes a compromise between potentially conflicting individual preferences. To identify a policy that best aggregates individual preferences, our system first elicits individual stakeholder value judgements by asking a moderate number of strategically selected queries, each taking the form of a pairwise comparison posed to a specific stakeholder. We propose a novel formulation of this problem that selects which queries to ask which individuals to best inform the downstream recommendation problem. Modeling this as a multi-stage robust optimization problem, we show that we can equivalently reformulate this as a mixed-integer linear program which can be solved with off-the-shelf solvers. We evaluate the performance of our approach on the problem of recommending policies for allocating critical care beds to patients with COVID-19. We show that asking questions intelligently allows the system to recommend a policy with a much lower regret than asking questions randomly. The lower regret suggests that the system is suited to help a committee reach a better decision by suggesting a policy that aligns with stakeholder value judgments.
Back to AI for Social Good event