human-in-the-loop (HITL)

How does the Human-in-the-Loop Approach Enhances ML Model Performance?

Machine learning models are not made perfect – they are perfected over time, with training and testing. An ML algorithm, to be able to produce accurate predictions, should be trained on massive quantities of highly-accurate training data. And overtime and after a series of trial and error testing, it will be able to come up with the desired output.

Ensuring greater accuracy in predictions depends on the quality of training data you feed into the system. Training data is of high quality only when it is accurate, organized, annotated, and relevant to the project. It is critical to involve humans to annotate, label, and tune the model.

Human-in-the-loop approach allows human involvement in labeling, classifying the data, and testing the model. Especially in cases when the algorithm is underconfident in deriving an accurate prediction or overconfident about an incorrect prediction and out-of-range predictions. 

Essentially, the human-in-the-loop approach relies on human interaction to improve the quality of training data by involving humans in labeling and annotating data and using thus annotated data to train the model.

Why is HITL Important? And to What Degree Should Humans be in the Loop?

Human-In-The-Loop Artificial intelligence is quite capable of handling simple stuff, but for edge cases, human interference is required. When machine learning models are designed using both human and machine knowledge, they can deliver enhanced results as both elements can handle the limitations of the other and maximize the performance of the model.

Let’s look at why the human-in-the-loop concept works for most ML models.

  • Increases accuracy and quality of predictions
  • Reduces the number of errors 
  • Capable of handling edge cases
  • Ensures safe ML systems

For the second part of the question, how much human intelligence is needed, we have to ask ourselves some critical questions.

  • The complexity of the decisions
  • The amount of domain knowledge or specialist involvement needed for the model
  • The number of damage errors and wrong decisions could cause

Let’s discuss your AI Training Data requirement today.

5 Key elements of HITL

With HITL, it is possible to create massive quantities of accurate data for unique use cases, enhance it with human feedback and insight, and retest the model to achieve accurate decisions.

  1. SME or Subject Matter Experts

    Regardless of the model, you are building – a healthcare bed allocation model or a loan approval system, your model will do better with human domain expertise. An AI system can leverage technology to prioritize bed allocation based on diagnosis, but to accurately and humanely determine who deserves the bed should be decided by the human doctors.

    Subject matter experts with domain knowledge should be involved at every stage of training data development in identifying, classifying, segmenting, and annotating information that can be used to further the proficiency of the ML models.

  2. QA or Quality Assurance

    Quality assurance forms a critical step in any product development. To be able to meet the standards and required compliance benchmarks, it is important to build quality into the training data. It is essential that you put in place quality standards that ensure adherence to performance standards to achieve the preferred outcomes in real-world situations.

  3. Feedback

    Constant Feedback Feedback, especially in the context of ML, from humans helps reduce the frequency of errors and improves the learning process of machines with supervised learning. With constant feedback from human subject matter experts, the AI model will be able to refine its predictions.

    During the process of training the AI models, it is bound to make errors in predictions or provide inaccurate results. However, such errors lead to improved decision-making and iterative improvements. With a human feedback loop, such iterations can get greatly reduced without compromising on accuracy.

  4. Ground Truth

    Ground truth in a machine learning system refers to the means of checking for the accuracy and reliability of the ML model against the real world. It refers to the data that closely reflect reality and that is used to train the ML algorithm. To make sure your data reflects the ground truth, it has to be relevant and accurate so that it can produce valuable output during real-world application.

  5. Tech Enablement

    Technology assists in creating efficient ML models by providing validation tools and workflow techniques and making it easier and faster to deploy AI applications.

Shaip has in place an industry-leading practice of incorporating a human-in-the-loop approach to developing machine learning algorithms. With our experience in providing best-in-class training data, we are able to accelerate your advanced ML and AI initiatives.

We have on-board a team of subject matter experts and have put in place stringent quality benchmarks that assure impeccable quality training datasets. With our multi-linguistic experts and annotators, we have the expertise to give your machine learning application the global reach it deserves. Get in touch with us today to know how our experience helps build advanced AI tools for your organization.

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