In-Depth Guide to Human in the Loop (HITL) Models in 2024

Human in the loop (HITL) is a machine learning method that combines the best parts of human intellect with the best features of artificial intelligence to develop an effective and modifiable algorithm for predictions. In this article, we explain HITL and its importance. We then evaluate the advantages and disadvantages of HITL and compare it with other machine learning strategies, namely reinforcement, supervised, and unsupervised machine learning models.

What is human in the loop?

HITL is a machine learning model that combines human and artificial intelligence to build effective ML models. AI systems are good at making optimal decisions when there is a large and high-quality data set. On the other hand, human intelligence is good at recognizing patterns within small and poor-quality datasets. In general human intellect provides following contributions:

• Data labeling: By adding appropriate tags to data, people helps ML to understand world.
• Feedback: ML models predict cases with a certain confidence interval. When the confidence of the model is below a threshold, data scientists provide feedback to the ML model to improve its performance.

How can HITL improve ML?

Machine learning with HITL benefits from data labeling and human feedback to AI algorithms to increase the operational efficiency of AI/ML models.

AI models can use human contribution to improve their algorithms. Data labeling is one of the steps that require human contribution.

The feedback mechanism is the unique feature of HITL that distinguishes it from supervised ML. Data scientists set a confidence interval threshold that represents the effective performance of the ML model. For the cases that remain below the threshold, engineers guide the model and help the ML models learn from them.

The feedback mechanism ensures that the machines continuously adjust their view of the world. Due to its adaptation capabilities HITL has the potential to develop more effective algorithms compared to other machine learning techniques. Also, HITL provides a larger playground for testing ML models, which is one of the most important MLOps practices.

Why is HITL important now?

As the Mckinsey study shows (Figure 1) AI/ML models are at the heart of today’s business operations. They are tools that drive revenue, profit, and efficiency for businesses. In this regard, the effectiveness of ML algorithms is the key business benefit that makes HITL machine learning model an important topic. 

What are the pros and cons of HITL machine learning?

To understand the advantages and disadvantages of HITL for machine learning practice, it is useful to make a comparison between HITL, reinforcement supervised and unsupervised machine learning.

As the above diagram shows, machine learning with HITL provides high accuracy in predictions. On the other hand, it involves higher costs and more manual tasks, as it requires feedback and data labeling.

Advantages of HITL

The main advantage of HITL is that it provides high quality results. This is because the quality of data is directly related to the performance of AI/ML models. In this regard, the data labeling process helps ML models make more accurate predictions.

Despite the process of data labeling, constant feedback on HITL output increases the accuracy of ML models and ensures the high quality of HITL’s results. Unlike AI, the human brain is relatively good in situations where the data is biased or limited. For example, if we observe the tail of a cat, that is enough to determine whether it is a cat or not. However, machines need development for such cases. Consequently, human feedback becomes a valuable input to HITL that improves accuracy.

(Figure 2) represents a situation where ML models may need human feedback to improve their prediction accuracy. For a ML model, it is difficult to determine the object within the images with a high accuracy. On the other hand, it is quite easy for humans to determine whether the image belongs to a Chihuahua or a muffin. By providing feedback HITL can improve its level of accuracy.

Disadvantages of HITL

Data labeling and constant feedback improve the quality of HITL output. However, these procedures are also costly, require human input, and take time.

First, data labeling requires people to annotate images, text, or audio files with a specific categorization. Such a task can be done internally, through outsourcing and crowdsourcing. Despite different levels of expenses, they are all costly.

Data labeling also requires a software program. Open source data labeling platforms offer free software, but require an IT team to operate and customize the code. On the other hand, closed-source and in-house options that replace open-source platforms already come at a cost.

Second, providing human feedback to HITL is again a labor-intensive task. Therefore, it is costly. Moreover, all these processes are time-consuming and require manual processes.

You can also read our article about human-in-the-loop automation. If you have other questions about HITL, we can help: