Beyond Visual Analytics: Human-Machine Teaming for AI-Driven Data Sensemaking

John Wenskovitch, Corey Fallon, Kate Miller, Aritra Dasgupta

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Detect the expected, discover the unexpected was the founding principle of the field of visual analytics. This mantra implies that human stakeholders, like a domain expert or data analyst, could leverage visual analytics techniques to seek answers to known unknowns and discover unknown unknowns in the course of the data sense-making process. We argue that in the era of AI-driven automation, we need to recalibrate the roles of humans and machines (e.g., a machine learning model) as teammates. We posit that by realizing human-machine teams as a stakeholder unit, we can better achieve the best of both worlds: automation transparency and human reasoning efficacy. However, this also increases the burden on analysts and domain experts towards performing more cognitively demanding tasks than what they are used to. In this paper, we reflect on the complementary roles in a human-machine team through the lens of cognitive psychology and map them to existing and emerging research in the visual analytics community. We discuss open questions and challenges around the nature of human agency and analyze the shared responsibilities in human-machine teams.

Original languageEnglish (US)
Title of host publicationProceedings - 2021 IEEE Workshop on TRust and EXpertise in Visual Analytics, TREX 2021
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages40-44
Number of pages5
ISBN (Electronic)9781665418171
DOIs
StatePublished - 2021
Event2021 IEEE Workshop on TRust and EXpertise in Visual Analytics, TREX 2021 - Virtual, Online, United States
Duration: Oct 24 2021 → …

Publication series

NameProceedings - 2021 IEEE Workshop on TRust and EXpertise in Visual Analytics, TREX 2021

Conference

Conference2021 IEEE Workshop on TRust and EXpertise in Visual Analytics, TREX 2021
Country/TerritoryUnited States
CityVirtual, Online
Period10/24/21 → …

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Computer Vision and Pattern Recognition
  • Human-Computer Interaction
  • Safety, Risk, Reliability and Quality
  • Media Technology
  • Sensory Systems

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