Formalizing Interruptible Algorithms for Human over-the-loop Analytics

12/03/2017 ∙ by Austin Graham, et al. ∙ 0 ∙ share

Traditional data mining algorithms are exceptional at seeing patterns in data that humans cannot, but are often confused by details that are obvious to the organic eye. Algorithms that include humans "in-the-loop" have proved beneficial for accuracy by allowing a user to provide direction in these situations, but the slowness of human interactions causes execution times to increase exponentially. Thus, we seek to formalize frameworks that include humans "over-the-loop", giving the user an option to intervene when they deem it necessary while not having user feedback be an execution requirement. With this strategy, we hope to increase the accuracy of solutions with minimal losses in execution time. This paper describes our vision of this strategy and associated problems.

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Formalizing Interruptible Algorithms for Human over-the-loop Analytics

Authors

Human-in-the-Loop Design Cycles – A Process Framework that Integrates Design Sprints, Agile Processes, and Machine Learning with Humans

A psychophysics approach for quantitative comparison of interpretable computer vision models

Sampling Humans for Optimizing Preferences in Coloring Artwork

Randomized Algorithms for the Loop Cutset Problem

Semantic Human Matting

Human-Centric Data Cleaning [Vision]

User Ex Machina : Simulation as a Design Probe in Human-in-the-Loop Text Analytics

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