Human-In-The-Loop: What, How and Why

Introduction

Estimated reading time: 1 minute

From processing invoices to medical diagnoses, the most advanced AI systems still need a secret ingredient to succeed: humans. The human element ensures AI systems are intelligent and aligned with our values and needs, enabling more trustworthy and robust outcomes. This is where the concept of “human-in-the-loop” comes into play.

What is human-in-the-loop (HITL)? Come along as I explore this concept and its importance in machine learning. We’ll discuss the definitions and real-world applications of HITL, analysing its benefits and challenges to understand its role in the future of AI.

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What Is the Human-In-The-Loop Approach

Human-in-the-loop (HITL) combines human intelligence with machine learning capabilities. It involves active and continuous human participation, integrating humans into the AI process flow. The goal is to use human input to maximise the potential of AI while mitigating its risks.

The concept emerged from artificial intelligence (AI) and human-computer interaction (HCI). As AI systems grew more complex, researchers and developers recognised the need for human intervention and guidance to ensure accuracy, safety, and ethical considerations.

Does this mean other AI systems do not involve humans? This is where it gets a bit more complicated, and to understand it, we need to look into a related concept: “human-on-the-loop”.

Human-In-The-Loop vs Human-on- (and Outside) The-Loop

The human-on-the-loop (or human-over-the-loop) approach also combines human intelligence with machine learning to improve results, much like the human-in-the-loop approach. Think of it as a system where a human acts as a supervisor, intervening when necessary. We still have the human element, but the end-user can see the system’s results before human verification. In this approach, humans may not be involved in every decision but can step in to correct errors, adjust parameters, or handle exceptions.

In the human-in-the-loop approach, end users can’t see the results until a human verifies them. Humans are actively and continuously involved in the decision-making process. HITL implies a more hands-on approach, where humans provide feedback, correct errors, and guide the AI model throughout the process.

Here is a table comparing the two models:

Approach	Human-in-the-Loop (HITL)	Human-on-the-Loop
Human Involvement	Active and continuous involvement. Humans are integrated into the flow of the AI process.	Monitors the AI system. Only intervenes when necessary.
Timing of Intervention	Human input is required before presenting the results to the end-user. AI results are blocked until human review.	AI results are presented directly to the end-user, even if not perfect. Human intervention happens afterwards.
Nature of Involvement	Humans provide feedback, correct errors, and guide the AI model throughout the process. Human input shapes the AI’s output and future processing.	Humans have a more supervisory role, correcting the results after they have been generated and presented to users.
Use Cases	Suitable for use cases where high accuracy is crucial. Examples: credit loan risk assessment, medical diagnosis.	Suitable for use cases where some errors are acceptable and speed is a priority. Examples: labelling documents and content moderation.
Error Tolerance	Low tolerance for errors.	Higher tolerance for errors.
Goal	To maximise accuracy, reliability, and ethical considerations, while enabling continuous learning of the AI model.	To quickly provide AI results, with some human supervision.
Feedback	Human feedback actively modifies the AI’s output to be more correct.	Human feedback corrects errors in the output after its generation.

In summary, while both approaches involve human input, they do so in different ways. The human-in-the-loop approach integrates humans directly into the AI process to refine results continuously, while human-on-the-loop systems use human oversight to correct results after they are produced. In the second approach, human feedback can also be used for retraining, but it is not inherently part of the process. The choice between the two depends on the specific needs and constraints of the use case.

Human-out-of-the-loop

But what if we remove the human from the loop? Human-out-of-the-loop systems operate without human intervention. These systems make decisions and take actions independently based on their programming and training data. Humans may have designed the system and set its initial parameters, but they are not actively involved in its ongoing operation. These systems require autonomous decision-making, where excessive human intervention in the process is neither practical nor desirable.

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How Does the Human-In-The-Loop System Work and How to Implement It

The human-in-the-loop (HITL) process is iterative, with a continuous cycle of data collection, model training, human feedback, and model refinement.

Let’s look at how to implement a human-in-the-loop system by analysing each cycle:

1. Initial Setup and Data Collection
   
   • Define the problem: Clearly define the challenge and the goals you want to achieve. Consider the desired outcome, whether improved accuracy, increased efficiency, or enhanced trust.
   • Gather initial data: Collect the data required for training the AI model. This can be existing labelled data or new data. If no labelled data exists, ask those responsible for the process to label a small initial data set.
   • Data preparation: Prepare the data for use by an AI. This may involve transforming text or other data formats into a format an AI algorithm can use.

2. Model Training and Initial Output
   
   • Train an initial model: Use the labelled data to train a preliminary AI model. This initial model does not need to be highly accurate.
   • Generate initial outputs: Use the AI model to generate outputs based on new data. At this stage, the model’s accuracy will likely be imperfect.

3. Human Feedback and Verification
   
   • Human review: Have humans review the model’s outputs, marking any errors and edge cases.
   • Active correction: In this stage, humans correct errors and provide guidelines for correct processing.
   • Feedback collection: Set up systems to collect feedback, corrections, and further information from humans in the loop.

4. Model Refinement and Iteration
   
   • Retrain the model: Incorporate the human-corrected data into the training set and retrain the model.
   • Active learning: Use active learning techniques to select the most informative examples for model refinement. For example, you can train the model using edge cases and outliers to improve accuracy in these areas.
   • Iterate: Repeat generating outputs, collecting feedback, and retraining the model. This is an ongoing cycle. With each iteration, the model should become more accurate and require less human intervention.

Implementing HITL Systems

We have several paths when choosing tools and platforms for HITL implementation. Cloud providers offer managed services and comprehensive tools for building scalable and flexible human-in-the-loop systems, making them ideal for large projects or those needing to integrate with existing cloud infrastructure. Specialised HITL platforms like Scale AI and Labelbox prioritize ease of use and provide end-to-end solutions, while open-source tools provide high flexibility and customization but require more technical expertise.

Devoteam partners with the leading cloud providers like AWS, Microsoft Azure and GCP, as well with cloud-based business platform ServiceNow. Our experts can help you develop human-in-the-loop systems integrated with your existing infrastructure, using readily available tools or designing a customised solution.

I consulted Devoteam’s AI experts to share an overview of implementing human-in-the-loop solutions for each platform.
Here is what the leading providers offer for human-in-the-loop implementation:

Why Do We Need Human-In-The-Loop

Now we know what human-in-the-loop is and how it works, but we also need to ask ourselves why we would use this approach. The most obvious answer is that it addresses several critical limitations of fully automated AI systems and enhances their overall performance and trustworthiness. For more on the potential downsides of AI and the importance of responsible AI practices, including the role of human oversight, see our article on the dark sides of AI.

Let’s look deeper into reasons why HITL is essential:

• Improved Measurement of Performance: With a human-in-the-loop, there is a structured way to measure a machine learning model’s performance. This is essential for calculating the return on investment of the AI use cases.
  
• Accuracy and Reliability: Human intervention compensates for AI limitations in nuanced decision-making, edge cases, and complex scenarios. For example, human oversight is crucial in sensitive areas such as medical diagnoses, legal reviews, and financial risk assessment.
  
• Ethical Considerations and Bias Mitigation: Humans can identify and correct biases in algorithms and training data, which ensures fairness and responsible AI deployment. AI models can inadvertently perpetuate existing biases in data, leading to discriminatory outcomes. Human involvement is crucial to ensure that AI systems are not used to reinforce societal inequalities.
  
• Adaptability and Continuous Learning: Humans provide feedback and labels to refine AI models, enabling ongoing improvement and adaptation to new data and unexpected situations. AI models are not static; they need to adapt to new information and evolving environments. Human feedback is essential for retraining the model and incorporating new and edge cases for improvement. This way, the human-in-the-loop approach facilitates continuous learning.
  
• Building Trust and User Acceptance: Human involvement increases the transparency and explainability of AI, which is crucial for fostering trust in AI systems, especially in sensitive domains. Explainable AI allows people to understand the reasoning behind the results produced by an AI system. Understanding why AI produced given results and knowing human judgment was part of the process makes people more likely to trust and accept them. It can also reduce the fear of fully automated systems.
  
• Optimisation of Workflows: The system streamlines processes and optimises workflows by balancing human and machine input. It can help reduce employees’ workload by automating routine and repetitive tasks, allowing them to focus on their core mission.
  

Limitations

While valuable, human-in-the-loop (HITL) systems have limitations. They require continuous monitoring and adjustment to maintain accuracy, which can be time-consuming. Also, HITL systems may be unnecessary for some tasks. In some use cases, human-over-the-loop or even full automation could be more beneficial. This is why we always choose the most fitting model for a specific use case.

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Use Cases and Applications of Human-In-The-Loop

Choosing the right model for a use case requires careful consideration of various factors, including the nature of the task, the required accuracy level, ethical implications, and practical constraints. This is usually identified during discovery sessions. Nevertheless, there are some use cases where the human-in-the-loop system is often suggested.

Here are some common cases where HITL approach is beneficial:

• Document Processing: HITL can automate the extraction of information from documents such as invoices. An initial AI model extracts the information, and human workers verify and correct the results, providing data for retraining and improving the AI model. See the project Devoteam completed for a fintech company that sought to automate the extraction of information from customer invoices or read how Document AI integrates human-in-the-loop
  
• Content Moderation: HITL is often used to filter out harmful or inappropriate content on online platforms. This involves humans reviewing content flagged by AI algorithms to ensure it violates community guidelines or legal regulations.
  
• Customer Service: HITL can improve customer interactions using chatbots and virtual assistants. In this case, AI can generate draft answers, but a human representative adds a personal touch by modifying the message according to the specific situation and user context. This ensures efficient and accurate customer service.
  
• Healthcare: HITL can assist in medical diagnosis and treatment planning. For instance, AI can analyse medical images and provide initial diagnoses, and doctors can review the results for accuracy and make the final decision, as well as identify edge cases. Read more on how AI is transforming the medicine of tomorrow and AI in healthcare.
  
• Self-Driving Cars: HITL plays a crucial role in training and validating autonomous driving systems. In this scenario, user data will be collected to understand how people react in different driving situations. This data collection could be part of a HITL system, requiring human input to ensure self-driving systems are safe and reliable.
  
• Finance: The human-in-the-loop system can be used for credit loan risk assessments, where a banker reviews the file before presenting it to the user. Read how Devoteam helped streamline water, gas and electric billing for a Real Estate Investment Trust (REIT) using the human-in-the-loop solution.

If you want to learn more about the transformative power of AI, check out our ebook with 50+ AI Use Cases across different industries.

Human-In-The-Loop and the Future of AI

I believe the future of AI is closely intertwined with the role of humans in the loop, with a focus on collaboration and continuous improvement. Here are some of my predictions about what that future might look like:

• Evolving Human Roles: Initially, humans are heavily involved in collecting, labelling, and correcting AI outputs. As models improve, the need for direct human intervention will likely decrease. However, the role of humans will evolve to higher-level monitoring, guidance, and complex decision-making.
• Generative AI: There will be an increased focus on incorporating human-in-the-loop into generative AI. This involves using human feedback to fine-tune generative models to gain additional accuracy and performance.
• Ethical Considerations: Is becoming more and more important to mitigate bias in AI by correcting data and feeding edge cases back to the model. This can increase the transparency and accountability of AI systems.
• Technological Advancements: Active learning techniques, where models are trained on the most informative data, will become more prevalent.
• Shift towards more specialised models: The future of AI is moving towards increased specialisation and efficiency. Large language models like Gemini and GPT are distilled into smaller, more focused models that are cheaper to run and tailored for specific tasks. This allows companies to leverage the strengths of different AI platforms while maintaining cloud platform independence.
  

To sum up, the future of AI involves shifting towards specialised models and greater emphasis on human oversight. While AI will become more autonomous, humans will play a crucial role in guiding, monitoring, and ensuring ethical AI development.

Conclusions

As AI systems become increasingly sophisticated, the need for human intelligence to guide, refine, and oversee their operation becomes even more critical. This collaborative approach ensures accuracy, allows performance measurement, mitigates bias, fosters trust, and allows AI to continuously learn and adapt. The result? AI at our service. Looking ahead, the future of AI is all about finding the optimal balance between human expertise and artificial intelligence, enabling us to use the full potential of both while prioritising human values and ethical considerations. I believe that the journey towards truly intelligent and beneficial AI is collaborative, where humans and machines work together.