Transparent-AI Blueprint: Developing a Conceptual Tool to Support the Design of Transparent AI Agents

Zhibin Zhou, Zhuoshu Li, Yuyang Zhang, Lingyun Sun

Research output: Journal article publicationJournal articleAcademic researchpeer-review

3 Citations (Scopus)

Abstract

With the increasing prevalence of artificial intelligence (AI) agents, the transparency of agents has become vital in addressing interaction issues (e.g., trust, usefulness, and understandability). However, determining the transparency of AI agents requires a systematic consideration of complex related factors, including stakeholders, algorithms, context, etc. Thus, in our study, we presented an overview of studies on the transparency of AI agents through multiple-stage bibliometric analysis, and identified an ontological framework of the key concepts relevant to transparent AI. We then built a Transparent-AI Blueprint prototype which is a diagram that visualizes the ontological framework of design concepts. In the subsequent pilot test, we updated Blueprint to the final version, and validated it in a workshop. Our work structurally summarized the design concepts related to the transparency of AI agents, and proposed a useful and practical conceptual design tool that effectively guides designers to operationalize the transparency of AI agents.

Original languageEnglish
Pages (from-to)1846-1873
Number of pages28
JournalInternational Journal of Human-Computer Interaction
Volume38
Issue number18-20
DOIs
Publication statusPublished - Apr 2022
Externally publishedYes

ASJC Scopus subject areas

  • Human Factors and Ergonomics
  • Human-Computer Interaction
  • Computer Science Applications

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