资源分配中平衡公平与效率的民意支持:以网络广告提高福利计划意识为例

Allison Koenecke, Eric Giannella, Robb Willer, Sharad Goel
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引用次数: 1

摘要

从医疗保健到贷款,用算法优化有限资源的供应在各个领域都很常见。优化可以导致有效的资源分配,但如果没有进行额外的审查,也可能加剧不平等。人们对可接受的效率-公平权衡的普遍偏好知之甚少,这使得设计能够响应社区需求和愿望的算法变得困难。在这里,我们在GetCalFresh的背景下研究这种权衡和伴随的偏好,GetCalFresh是一种简化加州补充营养援助计划(SNAP,以前称为食品券)申请流程的在线服务。GetCalFresh通过在线广告来提高他们的多语言SNAP应用服务的认知度。我们首先证明,当广告被优化为每美元获得最多的注册人数时,由于非英语广告的成本相对较高,西班牙语使用者的注册人数不成比例地少。将这些结果嵌入一项针对不同美国人的调查(N = 1532)中,我们发现,除了效率之外,人们普遍支持重视公平:受访者普遍倾向于减少总入学人数,以促进西班牙语入学人数的增加。这些结果支持了最近重新评估算法资源分配中流行的以效率为中心的范式的呼吁。
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Popular Support for Balancing Equity and Efficiency in Resource Allocation: A Case Study in Online Advertising to Increase Welfare Program Awareness
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.
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