What is human-in-the-loop machine learning?
We all know that AI-based systems are getting more and more robust with each passing day. They are exceptionally good at learning and making accurate decisions, particularly when it has access to a quality data set.
Unfortunately, in the real world, such high-quality data sets are pretty rare to find. This hugely limits the capabilities of the AI engine. This is where the importance of human intelligence surfaces. Unlike machines, humans have the capability to recognize patterns even within a small and low-quality data set.
When you combine the intelligence of machines and humans to form a feedback loop, you get to make accurate decisions. And that’s where human-in-the-loop machine learning comes into the picture.
In this post, we will look into everything about this popular concept.
Table of Contents
- What is human-in-the-loop machine learning?
- Working of HITL
- When to use HITL?
- Handy tips to master HITL
What is human-in-the-loop machine learning?
In layman’s terms, Human-in-the-loop (HITL) is a subset of AI that combines the power of humans and AI to build powerful machine-learning models. HITL is all about letting humans offer feedback to an ML model, mainly to handle predictions below a certain level of confidence.
HITL’s goal is straightforward - to achieve what neither a human nor a machine can achieve on its own. There are cases when a machine is unable to solve a problem, and a human has to step in. This builds a continuous feedback loop where the algorithm consistently learns from the human intervention and generates improved results every time.
Working of HITL
Here is a simplified working of a HITL-
Step 1: Labeling
In this, humans have to provide high-quality training to the ML model with the help of data annotation and labeling. This equips the algorithm to learn and make accurate decisions next time.
Step 2: Tuning
This step can be performed in multiple ways, but all of them focus on humans scoring the data set to account for any errors. This way, they can teach the model on edge cases or cover any recent groups that fall under the scope of the ML model.
Step 3: Test and confirm
This is dependent on the scoring of the model, where the model focuses on all those segments it is not sure of or thinks the chances are high for making a mistake.
When to use HITL?
Here are some scenarios when you need HITL -
- Whenever there is an absence of a labeled dataset, it becomes a necessity to create one. The HITL method can be effortlessly used to create such a dataset.
- Whenever the dataset evolves rapidly, the ML model must evolve at a similar pace. The HITL model can ensure that the model keeps itself updated with respect to the validation datasets.
- Whenever the dataset becomes hard to label through machines or any automated methodology, HITL can be used.
- Whenever you want to annotate different types of data labeling, for example, machines might not be able to differentiate between text, image, voice, etc. Humans can help by annotating the input and helping the model recognize it.
- Whenever you want to eliminate bias in the model. ML models can often become biased. This is understandable because they have been trained on raw and biased data. HITL can easily identify biases even during the early stages of model development. This can improve the accuracy of the model to a great extent.
Limitations of HITL
Every process, including a highly successful one like HITL, has its fair share of disadvantages. For starters, HITL is a costly and time-consuming process. This is mainly because tasks like data labeling and model fine-tuning require highly specialized workers. Finding them and training them takes a lot of time and can burn a hole in your pocket.
Handy tips to master HITL
Here are some tips and guidelines you can follow while adopting HITL -
- Make sure that everyone who handles (including those who collect, label, and train) is involved in the development of the application.
- Deploy humans strategically at every stage of the HITL process so that the model becomes optimized to a great extent.
- Hire only trained data specialists to collect, label, and train data. This will eliminate unwanted data reworks.
- Hire human resources even before your model development kickstarts.
- Avoid poor utilization of humans across the lifecycle of the ML model, as it can result in poor data quality and model failure.
In summary, HITL is the ultimate solution to improve the accuracy of ML models through the use of a continuous improvement feedback loop that exists between machines and humans. Building or finding high-quality data sets in the business world is extremely difficult, and the best way to overcome this issue is by leveraging human intelligence to identify patterns in poor-quality datasets and thus improving the overall accuracy of the ML model.