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Concept for systematically integrating humans into automated decision and learning processes to improve quality, accountability and adaptability.
Human-in-the-loop describes the deliberate inclusion of people in automated decision or learning processes to improve quality, robustness and accountability. Common in ML workflows for labeling, review and error correction. It defines roles, feedback loops and interfaces between human actors and systems. Implementation requires escalation, training and performance measurement.
Percentage of correct human labels against a gold standard.
Average time between automatic flagging and final review.
Share of erroneous automated decisions corrected by human intervention.
Using Label Studio to coordinate human annotators and automate review steps.
Automated filters flag content and humans perform secondary review when uncertain.
Defined processes for how human experts are integrated into the decision flow.
Define goals, roles and escalation paths
Establish guidelines, training data and test sets
Introduce tools, monitoring and continuous feedback loops