BlueCheck's Report on the Value of Human-in-the-Loop in Identity Verification
The implementation of AI is on the rise. McKinsey's survey revealed that it rose by 50% between 2020 to 2021, and it positively influenced the profits for these companies, too, 22% higher than the year before.
Are you one of those companies already making use of AI and automation? If so, kudos to you! An array of technologies available can take care of various fatiguing, vulnerable-to-errors, and oft-repeated tasks.
Still, tech like this may only cover some situations or conditions. You don't necessarily need a wholly automated process, either. Take data extraction, for instance - even with the most sophisticated tooling in place, and there is no guarantee that 100% accuracy is achievable each time. In specific industries, being wrong by a mere 1% can cost millions in dollars.
That’s why Human-In-The-Loop (HITL) automation might prove beneficial in many cases as it merges the best of AI and humans – we will explain more about it soon throughout this blog post. So, keep reading if you haven’t heard about HITL yet, or jump straight to its advantages if you are already familiar.
Automated vs. Manual workflows
Automating workflows can create operational efficiency that manual workflows can only sometimes attain. Through automation, businesses can save time, lessen mistakes, and decrease overhead expenses. Manual tasks that are tedious, prolonged, and prone to inaccuracies pose a high cost in terms of overhead expenses. Consider a corporation with abundant documents to process to demonstrate the impact of automation on adoption rates. Verifying each document followed by scanning it for record-keeping, extracting data for entry into a system, and validating its accuracy - such operations can easily be automated preventing excessive expenditure on overhead costs.
This manual document workflow needs to be more scalable, and many things can go wrong. As a result, organizations often search for solutions that automate these kinds of document workflows. Intelligent Document Processing (IDP) can eliminate manual tasks.
IDP might make it seem like a fantastic opportunity to automate manual workflows. However, there are still issues that even AI and machines still need to address. For example, when dealing with complex operations or low-quality data input, machines may not be able to deliver satisfactory results.
What is human-in-the-loop?
Human-in-the-loop (HITL) is a technique used to incorporate human input and interactivity to tune, test, or train AI models. An example of HITL can be seen in supermarkets where self-scanning machines are utilized but also monitored by staff nearby. This ensures customers get assistance and validate scanned items to prevent fraud or misuse. Using these machines helps reduce the number of employees required, in addition to waiting time. However, these machines are unreliable enough to be left unsupervised, thus making the human-in-the-loop approach an ideal solution for such settings.
Human-in-the-loop & AI
It is impossible to “perfect” automation, which is why human-in-the-loop automation is so important. How does HITL function within the context of AI? Human-in-the-loop enables AI models to be trained to recognize, classify, and predict objects better. A human would need to label images of these shapes correctly if you wanted to train AI algorithms to recognize shapes (i.e., squares, circles, triangles).
Having human intelligence step in can be incredibly beneficial for ensuring accuracy when AI models make a mistake with a prediction or identification. The feedback loop, or HITL annotation, improves the model's accuracy and provides better results. Although some OCR software offers an accuracy of up to 97-99%, the average data extraction accuracy is still around 80%. That leaves 20% of the data that may need to be corrected, which could pose a significant issue. By leveraging human-in-the-loop capabilities, organizations can fill in this gap and avoid potential problems caused by inaccurate data.
Annotated Human-in-the-loop annotation or data labeling is often part of developing AI models.
AI models need substantial uncategorized data (e.g., documents, pictures, text files, and other materials) to precisely distinguish objects and make forecasts. Collecting, annotating, and creating data sets requires much effort, time, and money. How is it done? This is where the human-in-the-loop (HITL) steps in; they assign tags to datasets, enabling AI models to concentrate on certain data fields regularly until they can recognize and provide the most accurate predictions. Say your business wishes for its AI model to recognize and extract line items from receipts; you may have to present the model with thousands of labeled docs to acquire satisfactory results. To have a labeled dataset suitable for teaching AI models, the raw material must be gathered, and an annotation team must be constructed. So why choose HITL automation when solutions offering 97% accuracy are available? Answers are given below.
The benefits of HITL automation
Why do we still require human involvement? It is because both manual and automated workflows are not free from errors. We all know that perfection is an unattainable goal. Hence, the human brain is still in high demand in situations when data or information is scarce. For instance, if we observe the tail of a tiger, it would be enough for us to recognize it as such - something AI can only do with extensive development. This is where HITL (Human-in-the-Loop) automation comes into play; this approach offers numerous advantages such as increased prediction, extraction, and validation accuracy, capacity to work with complex datasets, saving time for developers, better dealing with incomplete and challenging datasets, etc. Nevertheless, considering its limitations should also be taken into account.
Human-in-the-loop automation combines the best of both worlds, it also has some limitations. Organizations must identify who will interact with which interface and section within the automation loop. This limitation is still minimal when compared to those with manual or fully automated workflows. The effectiveness of the HITL is valid if when you address them correctly.
Human at the beginning or the end of a loop?
Do you need help determining when to utilize human-in-the-loop in your workflows? The best time to have a human in the loop is either at the beginning or at the end of a loop. Here is a review:
- HITL at the beginning of a loop
- HITL at the end of a loop
In the beginning of a loop, HITL
The HITL approach should be considered when there are no out-of-the-box solutions available. Why? Suppose you don't have any AI models or algorithms to automate specific processes, but you have a lot of raw data. This raw data can be transformed into labeled data with a human-in-the-loop, who ensures the data is cleaned (inaccurate data is removed or corrected) and labeled correctly. As soon as the data is labeled, you can use it to train your own AI models to recognize identifications or even extract data. You could train AI models to recognize identifications by labeling your data if you have many different identifications.
By starting at 0% automation, you can move up to 80% automation. In which situations should a human be placed at the beginning of the loop? You want to build your datasets, and you want to create your own AI models. You don't have any automation in place yet, but you want to move to +80% automation. You have data annotators & AI experts in-house.
HITL at the end of a loop
Human-in-the-loop at the end of a loop is common in many business cases. This approach uses automation for repetitive tasks and human intelligence for error detection.
By automating 80% of a workflow, you can experience improved accuracy and lower overhead costs. This approach is an ideal choice when you are aiming for maximal accuracy with minimal manual input, thus minimizing potential errors and streamlining turnaround time. There are market options that will automate most of the tasks involved, leaving only a tiny portion of human work. If you would like to have real-life examples of the differences between a human at the beginning and end of the loop, we can provide them.
Many known brands use HITL automation to improve their systems. Below, we provide a use case from Meta of human-in-the-loop examples in action.
Brands like Facebook have leveraged human-in-the-loop (HITL) automation to improve their systems. Facebook applied this strategy to its DeepFace algorithm, which can now reach an accuracy of 97.35% thanks to users confirming or rejecting facial recognition in photos.
External HITL by BlueCheck
You now see the value of that human-in-the-loop automation. Now, what do you do?
Contact Bluecheck today.