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Human in the Loop (HITL)

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Human-in-the-loop (HITL) machine learning is a collaborative approach that integrates human input and expertise into the lifecycle of machine learning (ML) and artificial intelligence systems. Humans actively participate in the training, evaluation, or operation of ML models, providing valuable guidance, feedback, and annotations. Through this collaboration, HITL aims to enhance the accuracy, reliability, and adaptability of ML systems, harnessing the unique capabilities of both humans and machines.
Human in the Loop is a principle for responsible AI use. It's a really good way to keep humans in the loop or keep yourself in the loop with how your agent is doing a task or working. If you define a list of steps or workflow for your agent, then you're not blind to how it's completing a task or what steps it's taking. 

You know exactly which steps it's taking. So it's a really helpful way to keep yourself in the loop of what my agent is up to, what changes has it made. Adding a step-by-step workflow to your AI Agent is a really good tool to align with the Human in the Loop concept.
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