Best Practices for Transparency in Machine Generated Personalization

Laura Schelenz, Avi Segal, Kobi Gal

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations

Abstract

Machine generated personalization is increasingly used in online systems. Personalization is intended to provide users with relevant content, products, and solutions that address their respective needs and preferences. However, users are becoming increasingly vulnerable to online manipulation due to algorithmic advancements and lack of transparency. Such manipulation decreases users' levels of trust, autonomy, and satisfaction concerning the systems with which they interact. Increasing transparency is an important goal for personalization based systems and system designers benefit from guidance in implementing transparency in their systems. In this work we combine insights from technology ethics and computer science to generate a list of transparency best practices for machine generated personalization. We further develop a checklist to be used by designers to evaluate and increase the transparency of their algorithmic systems. Adopting a designer perspective, we apply the checklist to prominent online services and discuss its advantages and shortcomings. We encourage researchers to adopt the checklist and work towards a consensus-based tool for measuring transparency in the personalization community.

Original languageEnglish
Title of host publicationUMAP 2020 Adjunct - Adjunct Publication of the 28th ACM Conference on User Modeling, Adaptation and Personalization
PublisherAssociation for Computing Machinery, Inc
Pages23-28
Number of pages6
ISBN (Electronic)9781450367110
DOIs
StatePublished - 14 Jul 2020
Event28th ACM International Conference on User Modeling, Adaptation, and Personalization, UMAP 2020 - Genoa, Italy
Duration: 14 Jul 202017 Jul 2020

Publication series

NameUMAP 2020 Adjunct - Adjunct Publication of the 28th ACM Conference on User Modeling, Adaptation and Personalization

Conference

Conference28th ACM International Conference on User Modeling, Adaptation, and Personalization, UMAP 2020
Country/TerritoryItaly
CityGenoa
Period14/07/2017/07/20

Keywords

  • checklist
  • ethics
  • guideline
  • personalization
  • recommendation
  • system design
  • transparency

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