Machine learning (ML) suggests that machines can learn and perform relevant actions without assistance from humans. However, to learn, machines must have data supplied by humans. Essentially, ML uses a set of data to predict an outcome. The most common association comes from conditional if/then logic. Yet, if a specific algorithm fails, the outcome could lead to a string of mistakes.
Human in the Loop (HITL) machine learning recognizes that artificial intelligence (AI) is best used as a tool that assists humans instead of replacing them. Instead of relying on data ingestion, HITL leverages human intelligence throughout the entire feedback cycle to promote continuous improvement. The cycle begins when humans label data. As the machine utilizes the data, humans use data analytics to score how accurately the data (algorithm) is used. The gathered data tunes the model for improvement based on specific shortfalls.
The algorithm becomes more confident and accurate when humans continually feed information back into the model. While machines are great at digesting and using large amounts of data, humans are superior when it comes to the nuances that ultimately lead to the correct solution for complex choices. HITL combines machine capabilities with human capabilities to provide accurate results.
Consider what it takes to learn. You must have enough information to predict a specific situation, a way to determine if your prediction is right or wrong, and the ability to improve. Machines are fed data in different ways to mimic the natural learning process. How you introduce data and score the results of a machine’s response makes a key difference in the success of any AI tool.
Machine learning models typically fall into supervised or unsupervised learning categories. In supervised learning, experts use labeled data sets that provide specific information to guide the outcome of the resulting decision or task. In unsupervised learning, data sets are fed to the machine without the accompanying information forcing the machine to find structure in the unlabeled data. While supervised ML is typically more accurate, it requires more effort and time to reach its intended value.
Generally, the intended use will be the biggest determiner of which type of ML model fits your needs. Unsupervised learning uses powerful tools to ingest and analyze large amounts of data for tasks like anomaly detection, recommendation engines, and customer personas. Meanwhile, supervised learning uses targeted data ingestion for tasks like email classification, pricing predictions, and weather forecasting.
A pivotal part of machine learning and AI is how data is introduced to machines and how it is used. Consider the approaches that could be used for machine learning. A machine-based approach visualizes a world of machine autonomy where machines can handle tasks without human intervention because they’re theoretically free from the bias that’s ingrained in humans. Conversely, a human-centered approach to AI recognizes that machines can never replace human traits like creativity, emotional intellect, and judgment. When machine learning depends on AI in the loop, errors made by AI models can be used to predict incorrect actions and decisions. However, human in the loop ML uses human validation to identify a machine learning’s model predictions as right or wrong.
How do you incorporate the valuable, unique aspects of what makes us human into an application or system? Adding HITL puts humans in the loop to add context to data that reflects human values and needs. AI is reliant on training data to learn. There are aspects of learning that only humans can provide. When we use HITL to improve algorithms, humans can add human judgment and values such as responsibility and ethics to protect private and sensitive data, improve safety, increase accuracy, and remove bias. HITL recognizes that AI shouldn’t replace humans but augment human capabilities.
The HITL approach means that humans are involved throughout the training, testing, and tuning process of building an ML model. Humans label the initial data to develop an algorithm. They also can verify the accuracy of the model’s predictions and provide feedback when an error occurs. When humans are a part of the continuous feedback loop, advanced results are achieved.
HITL machine learning uses human intervention at the time of training to validate an ML model’s predictions as right or wrong. There are some instances when a machine can’t analyze data efficiently to reach an accurate conclusion, like the use of cameras to recognize the size of an object. HITL can be used in specific circumstances to allow for training on data that does not have any labels, is challenging to tag by automated means, or is constantly evolving. When a machine learning model makes predictions, the accuracy can be validated through an already tagged dataset or people who validate or negate the prediction. Humans offer critical benefits for machine learning in many ways.
Unlabeled data can be hard to classify in specific conditions. Consider how a Roomba vacuum, designed to clean, creates a bigger mess when it encounters pet waste. While dog excrement is indeed a mess, it’s not the kind of mess a Roomba is meant to clean. After considering various technologies, the solution was to include image recognition trained on iRobot’s dataset that can determine whether an obstacle is pet waste. This was accomplished through years spent building a library of pictures to identify what that looks like. As a result, the company is so confident in the new model’s ability to avoid pet waste that it will replace any model that fails to do so.
When it comes to safety, a single mistake can be deadly. For these types of applications, you never want the AI to fall below human-level precision for a task. For example, if you are manufacturing critical equipment for an aircraft, ML may effectively be used for inspections. However, the system should still be monitored by humans to ensure human-level precision is always achieved.
71% of consumers expect companies to deliver a personalized experience, and 76% get frustrated when this doesn’t happen. AI technology like intuitive search bars, chatbots, and even personalized emails are created by HITL. Personalization that considers human needs, wants, and behaviors can only be developed when humans provide such data to train the model.
Early predictions of ML to address inclusivity suggested that machine algorithms are the answer to solving the problem of human bias. However, algorithms can amplify biases. When the data used to create an algorithm is incomplete or representative of a discriminatory reality, results will become increasingly biased.
Model Improvement Through Human Review
Adding a human review to machine learning workflows can improve the quality of models. When humans review errors made by the models, they can assess where training data can be augmented, understand if the model is biased towards certain attributes, and how data guidelines could be modified to reduce annotation ambiguity or data bias.
While relying on algorithms alone seems predictable, the reality is that human-centered AI offers a more dependable solution. When human expertise is used to validate data and results, an AI solution’s overall reliability and accuracy is improved. Humans can interject critical information into the process by providing continual feedback during the training process, resulting in ML models that make more accurate predictions.
AI is intended to benefit humans and has to be built with this in mind. As automated and machine-driven AI evolves, the human-in-the-loop aspect becomes even more important. By incorporating human feedback into the training process of machine learning models, we can create more accurate and reliable models that better serve the needs of humans.
Contact us if you’re interested in human-in-the-loop machine learning and incorporating human feedback into your training process. Our experienced team can provide you with the annotation services and data strategy you need to improve the accuracy of your machine learning models in an ethical, efficient, and effective manner.