Formalizing Interruptible Algorithms for Human over-the-loop Analytics
This addresses the trade-off between accuracy and speed in data mining for users needing efficient human-aided analytics, but it is incremental as it builds on existing in-the-loop approaches.
The paper tackles the problem of slow execution times in human-in-the-loop algorithms by proposing a formal framework for 'over-the-loop' systems that allow optional human intervention, aiming to improve accuracy with minimal time loss.
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.