Explainability for experts: A design framework for making algorithms supporting expert decisions more explainable

Auste Simkute*, Ewa Luger, Bronwyn Jones, Michael Evans, Rhianne Jones

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract / Description of output

Algorithmic decision support systems are widely applied in domains ranging from healthcare to journalism. To ensure that these systems are fair and accountable, it is essential that humans can maintain meaningful agency, understand and oversee algorithmic processes. Explainability is often seen as a promising mechanism for enabling human-in-the-loop, however, current approaches are ineffective and can lead to various biases. We argue that explainability should be tailored to support naturalistic decision-making and sensemaking strategies employed by domain experts and novices. Based on cognitive psychology and human factors literature review we map potential decision-making strategies dependant on expertise, risk and time dynamics and propose the conceptual Expertise, Risk and Time Explainability framework, intended to be used as explainability design guidelines. Finally, we present a worked example in journalism to illustrate the applicability of our framework in practice.
Original languageEnglish
Article number100017
JournalJournal of Responsible Technology
Early online date19 Nov 2021
Publication statusE-pub ahead of print - 19 Nov 2021

Keywords / Materials (for Non-textual outputs)

  • explainability
  • decision support systems
  • journalism
  • human-in-the-loop
  • expertise


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