Replacing human efforts by machine learning algorithms is widely accepted as inevitable and as a threat to people’s jobs. But these views miss the fundamental point that people and machines work best together, enhancing each others’ capabilities.
When most people think of machine learning, they often only consider the machine aspect. However, for an ML algorithm to function, it has to be trained by people. Humans are also the ones that maintain them, retrain them and improve them. Every successful AI project includes humans-in-the-loop (HITL) who do everything from training and testing the algorithms to labelling data, conducting quality control, and validation.
This is where HITL are critical. The concept refers to people involved in the AI model development process to apply human expertise and judgment during its development, training and across the AI lifecycle. High-performing AI systems rely on humans in the loop.
So why is HITL critical for the success of AI systems? Here are a few reasons why!
One of the aims of AI is to be able to replicate human decisions at scale. For AI to be effective, it needs to convert often intuitive decisions and human reactions into rule-based actions in the form of code that a machine can understand. In other words, it depends on human judgement and expertise in the form of intricately prepared data sets and rules. Such proficiency can only be achieved if the training sets are accurately and correctly prepared by HITL. The model needs to know how to react in any given situation and it needs humans to teach it.
People play a key role in helping the AI learn and improve through validating and reviewing the model’s output and feeding the feedback back into it. Starting from the initial training dataset, HITL are responsible for the quality of the model as a function of the quality of the data. As the saying goes — the model is only as good as the data that feeds into it.
Quality control continues throughout the production stage to ensure the model can make accurate decisions in unusual or edge case situations. For example, an AI model trained to detect and recognize supermarket goods will need to be constantly refreshed, given that product packaging changes all the time.
Once an AI model has been trained initially, the role of the HITL changes but it just as crucial. What follows next is validation of the model output and if necessary, correction of the result by the human expert to ensure the model keeps improving, while the output is accurate.
Annotation tools are great for increasing annotation speed and accuracy and overall efficiency of the data lifecycle. However, assisted and automated annotation requires quality control. If an ML model encounters input it cannot identify or is ‘unsure’ of i.e. confidence level is not as high as needed, humans have to step in to make corrections. The HITL can also provide crucial feedback (data or tool-related) as they are the most experienced when it comes to the nitty gritty of the data. Combined with a managed workforce, such tools and annotation platforms can help scale humans-in-the-loop and drive AI forward.