Understanding User Perceptions of Trustworthiness in E-recruitment Systems

Gideon Ogunniye, Benedicte Legastelois, Michael Rovatsos, Liz Dowthwaite, Virginia Portillo, Elvira Perez Vallejos, Jun Zhao, Marina Jirotka

Research output: Contribution to journalArticlepeer-review

Abstract / Description of output

Algorithmic systems are increasingly deployed to make decisions that people used to make. Perceptions of these systems can significantly influence their adoption, yet, broadly speaking, users understanding of the internal working of these systems is limited. To explore users perceptions of algorithmic systems, we developed a prototype e-recruitment system called Algorithm Playground where we offer the users a look behind the scenes of such systems, and provide how and why explanations on how job applicants are ranked by their algorithms. Using an online study with 110 participants, we measured perceived fairness, transparency and trustworthiness of e-recruitment systems. Our results show that user understanding of the data and reasoning behind candidates rankings and selection evoked some positive attitudes as participants rated our platform to be fairer, more reliable, transparent and trustworthy than the e-recruitment systems they have used in the past.
Original languageEnglish
Pages (from-to)23-32
Number of pages10
JournalIEEE Internet Computing
Issue number6
Early online date27 Sept 2021
Publication statusPublished - 6 Dec 2021

Keywords / Materials (for Non-textual outputs)

  • AI Ethics
  • Algorithms
  • Digital Platforms
  • Trust Building


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