Good for the Many or Best for the Few? A Dilemma in the Design of Algorithmic Advice

Graham Dove, Martina Balestra, Devin Mann, Oded Nov

Research output: Contribution to journalArticlepeer-review

5 Scopus citations

Abstract

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.

Original languageEnglish
Article number168
JournalProceedings of the ACM on Human-Computer Interaction
Volume4
Issue numberCSCW2
DOIs
StatePublished - 14 Oct 2020
Externally publishedYes

Keywords

  • advice applications
  • design dilemmas
  • empirical study

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