Algorithmically optimizing the provision of limited resources is commonplace across domains from healthcare to lending. Optimization can lead to efficient resource allocation, but, if deployed without additional scrutiny, can also exacerbate inequality. Little is known about popular preferences regarding acceptable efficiency-equity trade-offs, making it difficult to design algorithms that are responsive to community needs and desires. Here we examine this trade-off and concomitant preferences in the context of GetCalFresh, an online service that streamlines the application process for California's Supplementary Nutrition Assistance Program (SNAP, formerly known as food stamps). GetCalFresh runs online advertisements to raise awareness of their multilingual SNAP application service. We first demonstrate that when ads are optimized to garner the most enrollments per dollar, a disproportionately small number of Spanish speakers enroll due to relatively higher costs of non-English language advertising. Embedding these results in a survey (N = 1,532) of a diverse set of Americans, we find broad popular support for valuing equity in addition to efficiency: respondents generally preferred reducing total enrollments to facilitate increased enrollment of Spanish speakers. These results buttress recent calls to reevaluate the efficiency-centric paradigm popular in algorithmic resource allocation.

翻译：通过算法优化有限资源配置在医疗、贷款等领域已十分普遍。优化虽能实现资源高效分配，但若未经额外审查便部署，也可能加剧不平等现象。目前关于公众对效率与公平可接受权衡的偏好知之甚少，这增加了设计响应社区需求与意愿的算法的难度。本研究以GetCalFresh（一项简化加利福尼亚州补充营养援助计划（SNAP，原称食品券）申请流程的在线服务）为背景，探讨了这一权衡问题及伴随性偏好。GetCalFresh通过投放在线广告提升其多语言SNAP申请服务的知晓度。我们首先证明，当广告优化目标为最大化每美元带来的注册量时，因非英语广告成本相对较高，西班牙语使用者注册比例显著偏低。将这一结果嵌入面向多元美国民众的问卷调查（N=1,532）后，我们发现公众普遍支持在效率之外兼顾公平：受访者普遍倾向于减少总注册量，以促进西班牙语使用者注册人数的增长。这些结果佐证了近期对算法资源配置中效率中心范式的重新审视呼吁。