In many real-world applications, machine learning models are not designed to make decisions in an all-or-nothing manner. Instead, there are situations where it is more beneficial for the model to flag certain predictions for human review — a process known as human-in-the-loop. This approach is particularly valuable in high-stakes scenarios such as fraud detection, where the cost of false negatives is significant. By allowing humans to intervene when a model is uncertain or encounters complex cases, businesses can ensure more nuanced and accurate decision-making.
In this article, we will explore how thresholding, a technique used to manage model uncertainty, can be implemented within a deep learning setting. Thresholding helps determine when a model is confident enough to make a decision autonomously and when it should defer to human judgment. This will be done using a real-world example to illustrate the potential.
By the end of this article, the hope is to provide both technical teams and business stakeholders with some tips and inspiration for making decisions about modelling, thresholding strategies, and the balance between automation and human oversight.
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