论文标题
对少数人有好处吗？设计算法建议设计的困境
Good for the Many or Best for the Few? A Dilemma in the Design of Algorithmic Advice
论文作者
论文摘要
包括路线规划和福祉在内的一系列领域中的应用程序，根据先前用户的聚合活动中可用的社会信息提供建议。在设计这些应用程序时，提供更好的提供：a）建议如果严格遵守的话，更有可能导致个人成功实现目标，即使使用者更少会选择采用它？或b）大量用户可能会采用的建议，但是对于任何特定的人实现其目标而言，这是次优的建议？我们确定了这一难题，其特征在于目标定向与采用指导的建议，并通过在四个建议领域进行的在线实验（财务投资，更健康的生活方式选择，路线计划，5K运行的培训）进行了在线实验，并具有三个用户类型，并具有三个用户类型，以及两个不确定的不确定性。我们报告的发现表明，偏爱有利于个人目标实现的建议而不是更高的用户采用率，尽管咨询域之间的差异很大；并讨论他们的设计含义。
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.
