Good for the Many or Best for the Few?: A Dilemma in the Design of Algorithmic Advice
Good for the Many or Best for the Few?: A Dilemma in the Design of Algorithmic Advice
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对多数人有利还是对少数人有利？：算法建议设计中的困境
DOI：
10.1145/3415239
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发表时间：
2020
影响因子：
--
通讯作者：
Nov, Oded
中科院分区：
文献类型：
--
作者：
Dove, Graham;Balestra, Martina;Mann, Devin;Nov, Oded
包括路线规划和健康在内的一系列领域的应用程序根据先前用户的聚合活动中可用的社交信息提供建议。在设计这些应用程序时，提供以下建议是否更好：a）如果严格遵守，则更有可能导致个人成功实现其目标，即使很少用户会选择采用它？或B）可能被大量用户采用但对于实现其目标的任何特定个人而言是次优的建议？我们发现了这种困境，其特征是目标导向与采用导向的建议，并通过在四个建议领域（金融投资、做出更健康的生活方式选择、路线规划、5公里训练）进行的在线实验来调查它提出的设计问题。运行），有三种用户类型，跨越两个不确定性水平。我们报告的调查结果表明，偏好的建议有利于个人目标的实现更高的用户采用率，虽然有显着的变化，建议域，并讨论其设计的影响。
Applications in a range of domains, including route planning and well-being, offer advice based on the social information available in prior users' aggregated activity. When designing these applications, is it better to offer: a) advice that if strictly adhered to is more likely to result in an individual successfully achieving their goal, even if fewer users will choose to adopt it? or b) advice that is likely to be adopted by a larger number of users, but which is sub-optimal with regard to any particular individual achieving their goal? We identify this dilemma, characterized as Goal-Directed vs. Adoption-Directed advice, and investigate the design questions it raises through an online experiment undertaken in four advice domains (financial investment, making healthier lifestyle choices, route planning, training for a 5k run), with three user types, and across two levels of uncertainty. We report findings that suggest a preference for advice favoring individual goal attainment over higher user adoption rates, albeit with significant variation across advice domains; and discuss their design implications.