Introducing contextual transparency for automated decision systems

Mona Sloane, Ian René Solano-Kamaiko, Jun Yuan, Aritra Dasgupta, Julia Stoyanovich

Research output: Contribution to journalArticlepeer-review

4 Scopus citations

Abstract

As automated decision systems (ADS) get more deeply embedded into business processes worldwide, there is a growing need for practical ways to establish meaningful transparency. Here we argue that universally perfect transparency is impossible to achieve. We introduce the concept of contextual transparency as an approach that integrates social science, engineering and information design to help improve ADS transparency for specific professions, business processes and stakeholder groups. We demonstrate the applicability of the contextual transparency approach by using it for a well-established ADS transparency tool: nutritional labels that display specific information about an ADS. Empirically, it focuses on the profession of recruiting. Presenting data from an ongoing study about ADS use in recruiting alongside a typology of ADS nutritional labels, we suggest a nutritional label prototype for ADS-driven rankers such as LinkedIn Recruiter before closing with directions for future work.

Original languageEnglish (US)
Pages (from-to)187-195
Number of pages9
JournalNature Machine Intelligence
Volume5
Issue number3
DOIs
StatePublished - Mar 2023

All Science Journal Classification (ASJC) codes

  • Software
  • Human-Computer Interaction
  • Computer Vision and Pattern Recognition
  • Computer Networks and Communications
  • Artificial Intelligence

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