What is Human-in-the-Loop?

Summary

This video explains the importance of Human-in-the-Loop (HITL) for enhancing the accuracy of document processing with Google Cloud's Document AI. It discusses how machine learning models, though powerful, benefit from human review to ensure precision in document handling. HITL allows human reviewers to verify and correct model outputs based on confidence scores. The video highlights key steps in the document processing workflow, such as labeling, classification, and entity extraction, while showing how human intervention improves accuracy. Viewers are encouraged to explore hands-on guides for using HITL in their own workflows.

Takeaways

• 😀 Human in the Loop (HITL) is a process where humans review and verify AI-generated document results for accuracy.
• 😀 HITL is crucial when high accuracy is needed for applications that rely on document processing.
• 😀 Google Cloud's HITL allows you to send documents to a human review team for labeling or verification.
• 😀 AI models, including Document AI, are trained with labeled data, and humans can assist in labeling data or verifying AI results.
• 😀 HITL can be triggered either manually or automatically based on the confidence score generated by the model.
• 😀 The confidence score (between 0 and 1) indicates how sure the model is about its results, but a low score doesn't always mean the result is wrong.
• 😀 You can set a confidence threshold to determine which documents get reviewed by humans, balancing between speed and accuracy.
• 😀 After review, the updated document data is saved as a JSON file in Google Cloud Storage for further use.
• 😀 Document AI workflows can involve multiple steps, including document preparation, classification, entity extraction, and review, with HITL integrating into many of these stages.
• 😀 Labeling corrected documents can contribute to improving the model by feeding them into the uptraining process, helping the model become more accurate over time.
• 😀 Google provides step-by-step guides and documentation for setting up and using HITL with Document AI, making it easier to implement for users.

Q & A

What is the role of Human in the Loop (HITL) in document AI?

HITL in document AI allows for human intervention to verify and correct results when AI processes documents. It ensures higher accuracy by having humans review the data, especially when the model's confidence score is low.

Why is human review necessary in AI-based document processing?

Human review is essential because no machine learning model is perfect. Even though AI can automate document processing, there are cases where human judgment is needed to ensure accuracy, especially in complex or unclear documents.

How does the confidence score impact the decision to send documents for human review?

The confidence score, ranging from 0 to 1, reflects how sure the AI model is about its results. Documents with a low confidence score (below a set threshold) will be sent for human review to verify or correct the extracted data.

What are some of the use cases where HITL is critical?

HITL is used in scenarios where AI models need human intervention for accuracy, such as in self-driving cars, automated contact centers, and document processing systems that require high accuracy for critical applications.

What is the difference between confidence and accuracy in the context of document AI?

Confidence refers to how certain the AI model is about its results, indicated by a score between 0 and 1. Accuracy, however, measures how correct the model's results actually are. A high confidence score doesn't always guarantee accurate results.

Can humans adjust the results of processed documents in the review process?

Yes, during the human review process, reviewers can verify the AI output and make corrections if necessary. If the AI result is inaccurate, reviewers can manually update the data and increase the confidence score.

How is human review integrated into the full workflow of document AI?

Human review fits into the document AI workflow by verifying documents at various stages, including document preparation, classification, and entity extraction. It also feeds into model uptraining, ensuring that the AI becomes more accurate over time.

What happens after a document is reviewed by a human?

After human review, the verified or corrected data is saved in a Google Cloud Storage bucket as a JSON file. This updated data is then part of the document object structure, with revision history reflecting the review steps.

How can the confidence threshold be set for human review?

The confidence threshold is configurable and determines which documents should be reviewed by humans. Documents with a confidence score below this threshold are sent for review. The higher the threshold, the fewer documents will be reviewed.

What is the role of label data in improving document AI models?

Label data is used to train the AI model by providing examples of how documents should be processed. This labeled data can be reviewed by humans to improve the accuracy of the model, helping it to better understand and process documents over time.