What does 'Human-in-the-Loop' refer to in machine learning?

'Human-in-the-Loop' refers to the integration of human feedback in model training and deployment. This concept emphasizes the importance of incorporating human insights and expertise during various stages of machine learning processes to improve model accuracy, performance, and user acceptance. By enabling human interactions, the system can benefit from real-world knowledge that enhances the model’s ability to learn from complex, nuanced scenarios that might not be captured solely through automated data processing.

This approach is particularly useful in situations where decisions have significant implications, allowing for adjustments based on human judgment. It creates a collaborative framework where humans and machines complement each other, enhancing the overall effectiveness of machine learning applications. The involvement of human feedback can lead to better-defined objectives, context-awareness, and responsiveness to user needs, ultimately resulting in more reliable and trustworthy AI systems.