Human in the Loop: Importance and benefits of HITL automation

There are limitations that AI and machine learning cannot overcome despite IDP software. For success, they must be coupled with human feedback in the form of human-in-the-loop automation.

What is Human-in-the-Loop?

Human-in-the-loop (HITL) describes a mechanism that uses human interaction to train, refine, or test specific systems such as AI models or machines to achieve the most accurate results.

A simple example of this is the self-scanning machines in supermarkets. Although customers can scan their products themselves, there is always an employee on site to help with problems and prevent fraud attempts. The approach is similar with HITL and AI.

Application areas

Technology is not flawless. That's why people need to be involved in automation and constantly align it with current goals and needs. Not only does the AI need to be trained by humans at the beginning in order to make correct decisions, humans also need to intervene and correct errors. This is known as a feedback loop and serves to improve the accuracy of the AI.

You can use HITL in the following application areas:

• OCR software
• self-driving cars
• Document & Email Processing
• Processing of receipts for loyalty actions
• Invoice processing for accounts payable
• Anonymization of sensitive information for compliance 
• Badge verification for KYC processes

With HITL, you can quickly identify problems and make improvements through a feedback loop (also called HITL annotation). 

This process is explained below.

Annotations

When AI models are developed, the labeling of data by humans is usually part of the process. To achieve reliable results, AI models require large amounts of data that need to be annotated, tagged and organized by humans, which requires a lot of time, money and effort. 

A data annotator and human-in-the-loop help AI models focus on specific data fields to make the best predictions. For example, companies may need to feed thousands of labeled receipts to achieve reliable results. 

Although there are a variety of solutions that can achieve 97 % accuracy, HITL automation is the better option to obtain a labeled dataset for AI model training.

The advantages of HITL automation

There is no solution that can achieve an error rate of 0 % for complex processes using fully automated solutions without human support. To get as close as possible to this error rate and at the same time reduce the manual workload, the combination of AI with automation through the human-in-the-loop process has proven its worth. Using HITL to train AI models or improve workflows offers various advantages, including

• Risk reduction - Reduction of financial risks resulting from incorrect data, e.g. invoice amounts, invoice addresses, credit amounts, etc.
• Simplification of exception handling - Simple introduction of a workflow for human verification and exception handling.
• Efficient personnel deployment - Manage, monitor and improve the productivity of staff performing human verification.
• Cost control - Control the cost of human verification with configurable filters.
• Data completeness - Ensure that the extracted data is complete for downstream business applications.

The application of HITL leads to improved precision in prediction, extraction, classification and validation as well as increased quality of results. Human input can be used to gradually improve algorithms and thus make AI usable for more areas of application. The efficiency of AI models is not limited by the quality of the data on which they are trained.

Challenges

If you are going to use HITL, be aware of the challenges and limitations that come with it:

• Identification of the human-in-the-loop - Companies need to find out who will operate which part of the automation process and which interface in order to identify the human-in-the-loop system.
• Large amounts of data - HITL cannot always handle large volumes of data efficiently, as there is a greater need for human involvement in the automation loop. The successive expansion of the solution through to the final comprehensive AI solution is particularly crucial here in order to plan the company's strategic goals in an operationally feasible manner.
• Limited scalability - When a human is involved in a process, scalability can become a problem. The challenge is to adjust the confidence so that human verification is only required for uncertain cases.

However, compared to the challenges and drawbacks of the same workflow in manual form, these limitations are minor and should not prevent you from using AI in your business.

When should human-in-the-loop take place?

It makes the most sense to use human-in-the-loop either at the beginning of the cycle or at the end.

HITL at the beginning

If there is no standard solution, you should incorporate HITL right at the beginning of the loop. If you don't currently have AI models or algorithms to automate certain processes, but have a significant amount of raw data, you can use human-in-the-loop to label and clean (remove or correct inaccurate data) this data. 

Once the data is labeled, you can use it to train your own AI models to recognize invoices or extract data from them. For example, you can label many different invoices to train AI models to recognize invoices. So you can go from a 0 % automation to a +80 % automation. 

So, in the following situations, it makes sense to put the human at the beginning of the cycle:

• Structure of data sets
• Create your own AI models
• No or low automation with the target of +80 % Automation
• In-house data annotators and AI experts available

HITL at the end

The use of "human-in-the-loop" to complete the process is common in many cases. This approach combines automation to complete repetitive tasks and human intelligence to ensure that everything is done correctly. Often 80 % of the workflow is already automated and 20 % is done by a human. So when is it worth choosing this approach over the previous one?

• They strive for maximum precision in data retrieval, prediction, validation, anonymization, etc.
• You want to reduce the need for human intervention by 20 % to reduce overhead costs
• You want to reduce costly errors (e.g., inaccurate data, duplicates, etc.).
• You want to optimize execution time while maintaining high precision.

External vs. self-managed HITLS.

There are two different ways to take the HITL approach:

• Externally managed HITL - Human-in-the-loop provided by an external party (e.g. SaaS provider, provider of data annotation services)
• Self-managed HITL - Companies that integrate a person into the cycle themselves

Conclusion - Optimizing AI with human-in-the-loop

With human-in-the-loop automation, you can achieve the following:

• Increase the accuracy of data extraction
• Acceleration of the processing time
• Reduction of overhead costs 
• Improved employee engagement
• Minimizes costly human errors through advance work by AI
• 4-eyes principle through the combination of an AI and a human being

You can find the right provider by answering the following questions:

• Does your organization need to achieve near 100 % accuracy in data extraction?
• Do you need externally or internally managed HITL?
• Do you have in-house AI experts? 
• How important is the fact that data remains 100 % in your internal infrastructure?
• What is important for your use case?
• Want to build your own data sets?

The advantage of Artificial Intelligence is that it can perform functions like a human to quickly and accurately develop and understand key insights. 

Regardless of your business model, an OCR solution using AI can help you make data work for you.

More articles on the topic:

• The best OCR software for intelligent process digitization
• Document verification with AI: Relieving the burden on your company
• Document Understanding: Techniques for understanding documents
• Document Capture: Innovative, digital document management