Using Augmented AI for human loop if you are reluctant to trust Machine in the first place
Augmented Artificial Intelligence (A2I) | Article
Quickly implement a human review of machine learning predictions.
Some machine learning applications need human oversight to ensure accuracy with sensitive data, provide continuous improvements, and retrain models with updated predictions. However, you’re often forced to choose between a machine learning-only or a human-only system in these situations. Companies are looking for the best of both worlds — integrating machine learning systems into your workflow while keeping a human eye on the results to guarantee needed precision.
Many decisions in our lives require a good forecast, and Artificial Intelligence (AI) and Machine Learning (ML) are almost always better at forecasting than their human counterparts. Yet, for all these technological advances, we still seem to lack confidence in AI predictions. Recent cases show that people don’t like relying on AI and prefer to trust human experts, even if they are wrong.
Why don’t we have the best out of both Worlds?
Understandably, people are apprehensive and reluctant to trust
the machine; how can we verify that the machine is not making mistakes doing its tedious job?
In the following paragraphs, we are going to explain this step-by-step.
‘AI-based document processing for multilingual litigation support’
Let’s take a look at some — Use Cases
Healthcare Sector
Medical insurance claims, intake forms, prescriptions and many other healthcare documents have valuable information locked inside, which needs to be extracted quickly and precisely. You can now use A2I and Textract to process documents, extract the data and have a human review the critical data. This saves time and money when document processing is done and allows humans to continuously review any nuanced or sensitive data or audit predictions.
Banking and Financial Services
Loan or mortgage applications, tax forms, and many other financial documents contain millions of data points that must be processed and extracted quickly and effectively. Using Textract and Amazon A2I, you can extract critical data from these forms, whether structured or unstructured and have a human review the output. DealNet Capital uses Textract and Amazon A2I to process their financial records, which has reduced the time spent manually reviewing documents by up to 80%.
Advantages and Benefits
Quickly implement a human review of ML predictions.
A2I allows you to incorporate human review into ML applications based on your specific requirements. Low-confidence predictions are sent to humans to review and take action. You can also require multiple reviewers to check a forecast to achieve consensus. Additionally, to audit models, you can randomly sample predictions for human review so that you can regularly evaluate if the model is still performing well. A2I helps people and machines do what they do best.
Integrate human oversight with any application.
A2I provides an easy way to integrate human oversight into your machine-learning workflows, with no machine-learning experience required. There is no need to go with an all-human review system instead of machine learning only. A2I combines machine learning and humans to provide you with automation while keeping a human eye on the results to offer needed precision. Amazon A2I makes it easy to integrate human judgement and AI into any ML application, regardless of whether it’s run on AWS or another platform.
Get to market quicker.
Deciding between machine learning and humans doing manual processes allows you to choose to market today rather than months from now. Integrating Amazon A2I into your workflow not only aids in getting to market with your machine learning quicker, but you can also update and retrain your models over time. As your business needs change, so can your workflows, and Amazon A2I can help you continuously improve your models at whatever stage you are in your machine-learning journey.
‘AI Verification & Augmentation Lead: Transforming the Landscape of Artificial Intelligence’
‘Automated extraction from international arbitration documents’
Examples of how others adapted this method successfully
T-Mobile US, Inc. is redefining how consumers and businesses buy wireless services through leading product and service innovation. Their advanced nationwide network delivers wireless experiences to 84.2 million customers unwilling to compromise quality and value.
“Providing relevant information, such as account details and available discounts, in real time to our customer care agents while they are in live conversations with customers is one of the ways T-Mobile uses machine learning to improve customer experience. Using A2I, we can ensure that our models continuously deliver top-quality insights by having humans validate random samples of model predictions. Trust is the hardest thing to build regarding machine learning, and A2I will allow us to ensure that our models are making the fewest mistakes.”
— Heather Nolis, Machine Learning Engineer, T-Mobile
Change Healthcare is a leading independent healthcare technology company that provides data and analytics-driven solutions to improve clinical, financial and patient engagement outcomes in the U.S. healthcare system.
“At Change Healthcare, we help accelerate healthcare’s transformation by innovating to remove inefficiencies, reduce costs and improve outcomes. We have a robust set of integrated artificial intelligence engines that bring new insights, impact, and innovation to the industry. Critical to our results is enabling humans in the loop to understand our data and automate workflows. Augmented AI (Amazon A2I) makes building the workflows required for human review of ML predictions easy. With A2I becoming HIPAA eligible, we can involve the human in the workflow and decision-making process, helping to increase efficiency with the millions of documents we process to create even more value for patients, payers, and providers.”
— Luyuan Fang, Chief AI Officer, Change Healthcare
‘AI-powered document comparison for legal compliance checks’
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Maksymilian Czarnecki
The Blog Post, originally penned in English, underwent a magical metamorphosis into Arabic, Chinese, Danish, Dutch, Finnish, French, German, Hindi, Hungarian, Italian, Japanese, Polish, Portuguese, Spanish, Swedish, and Turkish language. If any subtle content lost its sparkle, let’s summon back the original English spark.
