AI Bibliography
AI Bibliography |
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Wenskovitch, J., Fallon, C., Miller, K., & Dasgupta, A. 2021, Beyond visual analytics: Human-machine teaming for ai-driven data sensemaking. Paper presented at 2021 IEEE Workshop on TRust and EXpertise in Visual Analytics (TREX). |
| Resource type: Proceedings Article BibTeX citation key: Wenskovitch2021 View all bibliographic details  |
Categories: Artificial Intelligence, Cognitive Science, Complexity Science, Computer Science, Data Sciences, Decision Theory, General Subcategories: Augmented cognition, Autonomous systems, Chaos theory, Deep learning, Human decisionmaking, Machine learning, Psychology of human-AI interaction, Systems theory Creators: Dasgupta, Fallon, Miller, Wenskovitch Publisher: Collection: 2021 IEEE Workshop on TRust and EXpertise in Visual Analytics (TREX) |
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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.
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