Human In the Loop for Machine Learning
The majority of machine learning models rely on human-created data. But the interaction between humans and machines does not end there; the most powerful systems are designed to allow both sides to interact continuously via a mechanism known as “Human in the loop” (HITL).
HUMAN-IN-THE-LOOP (HITL) machine learning necessitates human inspecting, validating, or changing some aspect of the AI development process. This philosophy extends to those who collect, label and perform quality control (QC) on data for machine learning.
We are confident that AI will not fire its most trusted employees anytime soon. In reality, AI systems supplement and augment human capabilities rather than replace them. The nature of our work may change in the coming years as a result of AI. The fundamental principle, however, is the elimination of mundane tasks and increased efficiency for tasks that require human input.
Recent advancements in the field of artificial intelligence (AI) have given rise to techniques such as active learning and cooperative learning. Data is the foundation of any machine learning algorithm, and these datasets are typically unlabeled (e.g. Images). During the training stage, a human must manually label this dataset (the output, such as a cat or dog). This data is then used to train the machine learning model, which is known as supervised learning. The algorithms in this technique learn from labeled data to predict previously unseen cases. Using what we already know, we can go deeper and develop more sophisticated techniques to uncover other insights and features in the training dataset, resulting in more accurate and automated results.
Human and machine expertise are combined during the testing and evaluation phase by allowing the human to correct any incorrect results that have been produced. In this case, the human will specifically correct the labels that the machine was unable to detect with high accuracy (i.e. classified a dog for a cat). When the machine is overly confident about a wrong prediction, the human takes the same approach. The algorithm’s performance will improve with each iteration, paving the way for automated lifelong learning by reducing the need for future human intervention. When such work is completed, the results are forwarded to a domain expert who makes decisions that have a greater impact.
Machine learning with a human-in-the-loop
When you have a large enough dataset, an algorithm can make accurate decisions based on it. However, the machine must first learn how to properly identify relevant criteria and thus arrive at the correct conclusion. Here is where human intelligence comes into play: Machine learning with human-in-the-loop (HITL) combines human and machine intelligence to form a continuous circle in which the algorithm is trained, tested, and tuned. With each loop, the machine becomes smarter, more confident, and more accurate.
Machine learning can’t function without human input. The algorithm cannot learn everything necessary to reach the correct conclusion on its own. For example, without human explanation, a model does not understand what is shown in an image. This means that, especially in the case of unstructured data, data labeling must be the first step toward developing a reliable algorithm. The algorithm is unable to comprehend unstructured data that has not been properly labeled, such as images, audio, video, and social media posts. As a result, along the way, the human-in-the-loop approach is required. Specific instructions must be followed when labeling the data sets.
What benefit does HITL offer to Machine Learning applications?
1. Many times data are incomplete and unambiguous. Humans annotate/label raw data to provide meaningful context so that machine learning models can learn to produce desired results, identify patterns, and make correct decisions.
2. Humans check the models for over-fitting. They teach the model about extreme cases or unexpected scenarios.
3. Humans evaluate if the algorithm is overconfident or low in confidence to determine correct decisions. If the accuracy is low, the machine goes through an active learning cycle wherein humans give feedback for the machine to reach the correct result and increase its predictability.
4. It offers a significant enhancement in transparency as application no longer appears as a Black box with humans involved in each and every step in the process.
5. It incorporates human judgment in the most effective ways and shifts pressure away from building “100% machine perfect ” algorithms to optimal models offering maximum business benefit. This in turn offers more powerful and useful applications.
At the end of the day, AI systems are built to help humans. The value of such systems lies not solely in efficiency or correctness, but also in human preference and agency. The Humans-in-the-loop system puts humans in the decision loop.
Three Stages of Human-in-the-Loop Machine Learning
Training – Data is frequently incomplete or jumbled. Labels are added to raw data by humans to provide meaningful context for machine learning models to learn to produce desired results, identify patterns, and make correct decisions. Data labeling is an important step in the development of AI models because properly labeled datasets provide a foundation for further application and development.
Tuning – At this stage, humans inspect the data for overfitting. While data labeling lays the groundwork for accurate output, overfitting occurs when the model trains the data too well. When the model memorizes the training dataset, it may generalize, rendering it unable to perform against new data. It allows for a margin of error to allow for unpredictability in real-world scenarios.
It is also during the tuning stage that humans teach the model about edge cases or unexpected scenarios. For example, facial recognition provides convenience but is vulnerable to gender and ethnicity bias when datasets are misrepresented.
Testing – Finally, humans assess whether the algorithm is overly confident or lacking in making an incorrect decision. If the accuracy rate is low, the machine enters an active learning cycle in which humans provide feedback to the machine in order for the machine to reach the correct result or increase its predictability.
Final Thoughts
As people’s interest in artificial intelligence and machine learning grows, it’s important to remember that people still play an important role in the process of creating algorithms. The human-in-the-loop concept is one of today’s most valuable. While this implies that you will need to hire people to do some work (which may appear to be the polar opposite of process automation), it is still impossible to obtain a high-performing, sophisticated, and accurate ML model otherwise.
TagX stands out in the fast-paced, tech-dominated industry with its people-first culture. We offer data collection, annotation, and evaluation services to power the most cutting-edge AI solutions. We can handle complex, large-scale data labeling projects whether you’re developing computer vision or natural language processing (NLP) applications.