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cs.HC2026
Human-in-the-Loop User Feedback Affects Perceived Accuracy and Trust, but Task Subjectivity Matters
Donald R. Honeycutt, Mahsan Nourani, Eric D. Ragan
While ML can produce complex models beyond those that a human could produce manually, incorporating human input can often improve performance beyond purely data-driven models. Whil…
cs.HC2024
From Data Dump to Digestible Chunks: Automated Segmentation and Summarization of Provenance Logs for Communication
Jeremy E. Block, Donald Honeycutt, Brett Benda +2
Communicating one's sensemaking during a complex analysis session to explain thought processes is hard, yet most intelligence occurs in collaborative settings. Team members require…
cs.HC2020
Soliciting Human-in-the-Loop User Feedback for Interactive Machine Learning Reduces User Trust and Impressions of Model Accuracy
Donald R. Honeycutt, Mahsan Nourani, Eric D. Ragan
Mixed-initiative systems allow users to interactively provide feedback to potentially improve system performance. Human feedback can correct model errors and update model parameter…