In machine learning, we often want to create models and algorithms that do amazing things for our applications. However, we quickly learn that most of the work is actually collecting, cleaning, and labeling data. This can be discouraging because we were attracted to machine learning for its excitement. But, we need to do this work because it’s crucial for accurate models that have a real impact on our applications.

Labeling a large amount of data can be difficult and costly. Additionally, labeling all data is not always necessary because not all data is of equal quality. High quality data can significantly improve a model, while low quality data may impede a model’s ability to learn.

The question then becomes, how do we efficiently identify the good data samples that we need for our model? One effective solution is active learning !

What is active learning ?

Collecting data has become easy these days, thanks to the internet. However, labeling a lot of data can be difficult and expensive. Not all data is equally important, so labeling everything is unnecessary. Good data can help a model learn better, while bad data can harm it.

Active learning is a type of machine learning where the algorithm selects the data points from which it should learn, as opposed to traditional machine learning, which treats all data points equally. Active learning is a form of semi-supervised learning with a human-in-the-loop. Active learning is a data-centric approach to machine learning in which we keep the code static and iterate on the data. The main objective of the active learning model is to maximize the information gained from as few data points as possible. In order to achieve this, the model usually selects the more complex and challenging data to learn from. Active learning is particularly useful when there is a limited amount of labeled data available because it allows the algorithm to focus on the most important data points.

How does human-in-the-loop AI use active learning sample data ?

The machine learning algorithm selects a small subset of data points for a human to label. The human then provides feedback to the algorithm, which uses this feedback to improve its accuracy. This process is repeated iteratively, with the algorithm selecting new data points for the human to label based on its current level of accuracy. The main difference from standard supervised learning is that instead of humans labeling all training data, an algorithm or model chooses which data should be labeled. Great, but now you are asking yourself how are the data selected ?

It is important to explain the sampling strategies also named query strategies.

  • Random Sampling
    The algorithm randomly selects a subset of data points from the unlabeled pool for human labeling. While this strategy is easy to implement and a good starting point, it has some limitations.
    Random sampling may not select the most informative or relevant data points for labeling, resulting in a slower learning process and requiring more labeled samples to achieve a certain level of accuracy. Additionally, random sampling may lead to redundant or irrelevant data points being labeled, which can be a waste of human labeling effort and resources.
    Therefore, while random sampling can be effective in some cases, other sampling methods may be more efficient in selecting the most informative and relevant data points for labeling.
  • Uncertainty sampling
    When the model is uncertain, the system is not sure about a classification and raises a question to the human. Near a decision boundary, the system takes a humble approach and requests human help. This strategy is also named “confidence-based sampling.”
    Confidence-based sampling can be more efficient than random sampling, as it selects the most informative and relevant data points for labeling, leading to a faster learning process and requiring fewer labeled samples to achieve a certain level of accuracy.
  • Diversity sampling
    Diversity sampling is a type of active learning strategy that focuses on exploring the data space and selecting data points that are different or rare. The goal is to ensure that the model is exposed to a diverse range of data points, rather than just focusing on the most informative or relevant ones. This can be particularly useful in situations where the data is highly imbalanced or the model needs to be trained on a wide range of scenarios. By incorporating diversity sampling into the human-in-the-loop AI using active learning approach, we can improve the accuracy and efficiency of our models by ensuring that they are trained on a diverse set of data points.

In human-in-the-loop active learning, it’s best to use a mix of different sampling methods to make the system more efficient. By using a combination of strategies such as random, uncertainty, and diversity sampling, the algorithm can select the most informative and relevant data points for labeling, while also exploring the data space to ensure that the model is trained on a diverse set of data points. This can help the system learn faster and need fewer labeled samples to achieve a certain level of accuracy, making the whole process more efficient and effective.

Why is Human-in-the-Loop AI Using Active Learning so Important?

Human-in-the-Loop AI offers several advantages over traditional machine learning approaches. Human-in-the-Loop AI using active learning offers even more advantages over traditional machine learning approaches.

  • First, Human-in-the-Loop AI using active learning provides greater transparency by involving humans in the learning process. This required interaction makes the model’s behavior clearer and allows us to understand its knowledge, doubts, and weaknesses.
  • Second, iterative active learning can improve the accuracy of models by providing a better augmented dataset. This dataset can be used to retrain the model and enhance its performance.
  • Finally, incorporating humans and interaction into the loop can alleviate the burden of building “perfect” models. Instead, the model can be guided and corrected throughout its life, allowing for continuous improvement.

The primary value of AI lies in the interaction between humans and machines. By incorporating active learning, we can maximize the potential of this partnership. It is crucial to emphasize the importance of human involvement in the learning process. As AI continues to evolve, the role of Human-in-the-Loop AI will become increasingly important.

What Are Potential Applications of Human-in-the-Loop AI Using Active Learning?

Human-in-the-Loop AI using active learning has a wide range of applications across industries (medical, retail, finance, geospatial, automotive, agricultural, and many more).

The possibilities are endless, and Human-in-the-Loop AI using active learning has the potential to revolutionize many industries. By incorporating humans, the model is guided throughout its life, becoming more and more performant.

Reviewing and Evaluating the Model’s Predictions Post-Deployment to Surface Potential Biases and Failure Modes of the AI System

In the healthcare industry, one way to review and evaluate a machine learning model’s predictions after deployment is by predicting the likelihood of a patient developing a particular disease based on their medical history. Once deployed, healthcare professionals can evaluate the model’s predictions to detect any potential biases or failure modes. If, for instance, the model fails to predict the disease consistently in a particular demographic group, this may suggest a bias in the data or model that requires attention.

Generating New Ground Truth by Labeling New Data to Compare the Model’s Predictions with This Ground Truth and to Identify False Predictions

In natural language processing, a machine learning model can classify text messages as spam or not spam. To create new ground truth, a human can label a new set of text messages as either spam or not spam. By comparing the model’s predictions to the human-labeled data, any false predictions can be identified, and the model’s accuracy can be improved.

Monitoring for Unusual Data to Surface Instances That Are Close to Decision Boundaries or That Are Out of the Standard Distribution – These Will Be Sent to Human Operators on a Regular Basis

Active learning can be used to monitor for unusual data, such as in fraud detection for credit card transactions. When the machine learning model flags transactions close to the decision boundary, it suggests that the transactions may be potentially fraudulent. These flagged transactions can be reviewed by human operators for further investigation, leading to more accurate identification of fraudulent transactions and continuous improvement of the model.

Human-in-the-loop AI, which uses active learning, is a promising approach to machine learning that enables the creation of more accurate and efficient models. By involving humans in the learning process, we can develop AI systems that are better suited to real-world applications. As AI continues to transform our world, the role of human-in-the-loop AI will become increasingly important.

Looking for humans who can continuously review model predictions so as to avoid data drift and close the model retraining loop? 

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