Value-sensitive algorithm design: Method, case study, and lessons

Haiyi Zhu, Bowen Yu, Aaron Halfaker, Loren G Terveen

Research output: Contribution to journalArticlepeer-review

25 Scopus citations

Abstract

Most commonly used approaches to developing automated or artificially intelligent algorithmic systems are Big Data-driven and machine learning-based. However, these approaches can fail, for two notable reasons: (1) they may lack critical engagement with users and other stakeholders; (2) they rely largely on historical human judgments, which do not capture and incorporate human insights into how the world can be improved in the future. We propose and describe a novel method for the design of such algorithms, which we call Value Sensitive Algorithm Design. Value Sensitive Algorithm Design incorporates stakeholders’ tacit knowledge and explicit feedback in the early stages of algorithm creation. This increases the chance to avoid biases in design choices or to compromise key stakeholder values. Generally, we believe that algorithms should be designed to balance multiple stakeholders’ needs, motivations, and interests, and to help achieve important collective goals. We also describe a specific project “Designing Intelligent Socialization Algorithms for WikiProjects in Wikipedia” to illustrate our method. We intend this paper to contribute to the rich ongoing conversation concerning the use of algorithms in supporting critical decision-making in society.

Original languageEnglish (US)
Article number194
JournalProceedings of the ACM on Human-Computer Interaction
Volume2
Issue numberCSCW
DOIs
StatePublished - Nov 2018

Keywords

  • Algorithmic Intervention
  • Online Communities
  • Online Recruitment
  • Peer Production
  • System Buildings
  • Value-Sensitive Algorithm Design
  • WikiProjects
  • Wikipedia

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