TY - GEN
T1 - Beyond Visual Analytics
T2 - 2021 IEEE Workshop on TRust and EXpertise in Visual Analytics, TREX 2021
AU - Wenskovitch, John
AU - Fallon, Corey
AU - Miller, Kate
AU - Dasgupta, Aritra
N1 - Publisher Copyright:
© 2021 IEEE.
PY - 2021
Y1 - 2021
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85123771002&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85123771002&partnerID=8YFLogxK
U2 - 10.1109/TREX53765.2021.00012
DO - 10.1109/TREX53765.2021.00012
M3 - Conference contribution
AN - SCOPUS:85123771002
T3 - Proceedings - 2021 IEEE Workshop on TRust and EXpertise in Visual Analytics, TREX 2021
SP - 40
EP - 44
BT - Proceedings - 2021 IEEE Workshop on TRust and EXpertise in Visual Analytics, TREX 2021
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 24 October 2021
ER -