The Science of Rejection: A Research Area for Human Computation
Burcu Sayin, Jie Yang, Andrea Passerini, Fabio Casati
TL;DR
This paper emphasizes the importance of understanding how to reject incorrect model predictions and advocates for human computation to lead this research area, aiming to improve machine learning reliability.
Contribution
It highlights the significance of rejection in ML and proposes human computation as a key approach to advance this research area.
Findings
Rejection is crucial for reliable ML systems.
Human computation can enhance rejection strategies.
The paper advocates for focused research on rejection in ML.
Abstract
We motivate why the science of learning to reject model predictions is central to ML, and why human computation has a lead role in this effort.
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Taxonomy
TopicsAdversarial Robustness in Machine Learning · Explainable Artificial Intelligence (XAI) · Machine Learning and Data Classification