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:算法优化有限资源的分配在各个领域是司空见惯的,从医疗保健到贷款。优化可以导致有效的资源配置,但是,如果没有额外审查,也可能加剧不平等。关于可接受的公平和效率权衡,人们的偏好很少被了解,这使得难以设计响应社区需求和愿望的算法。在此,我们考虑这种权衡和并存偏好,其所伴随的情况是GetCalFresh的情景。GetCalFresh是加利福尼亚州补充营养辅助计划(SNAP,以前称为食品券)的在线服务,可以简化其申请过程。GetCalFresh运行在线广告,以提高其多语种SNAP申请服务的知晓度。我们首先证明了当广告被优化以获得每美元最多的注册时,由于非英语语言广告的相对高成本,西班牙语使用者的注册数量不成比例地少。将这些结果嵌入对不同种族的美国人的调查(N = 1,532)中,我们发现广泛的民意支持依据公平和效率优化资源分配:调查受访者通常倾向于减少总注册以促进西班牙语使用者的增加。这些结果支持最近呼吁重新评估算法资源分配中广泛流行的以效率为中心的范式。