Human-in-the-loop AI In A Nutshell

Human-in-the-loop (HITL) is a subset of artificial intelligence that utilizes human and machine intelligence to develop machine learning models.
HITL can be integrated into supervised or unsupervised machine learning algorithms. The iterative and collaborative nature of building a model is not unlike the process that occurs in agile software development.
HITL is particularly suited to instances where the data is rare or unavailable. It is also useful when costly development errors need to be avoided.

Aspect	Explanation
Definition	Human-in-the-Loop AI (HITL AI) is an approach to artificial intelligence in which human expertise and judgment are integrated into AI systems. It involves a human operator or expert who interacts with the AI system, providing guidance, oversight, or making decisions in conjunction with the AI algorithms. HITL AI combines the strengths of both human intelligence and machine learning to tackle complex tasks and improve AI system performance.
Key Concepts	– Human Collaboration: HITL AI emphasizes the collaboration between humans and AI systems, leveraging the unique strengths of each. – Feedback Loop: It involves a continuous feedback loop where human operators validate AI decisions, correct errors, and train the AI system to improve over time. – Complex Problem Solving: HITL AI is particularly useful for tasks that require human judgment, contextual understanding, and nuanced decision-making. – Quality Assurance: Human oversight ensures the AI system’s output meets quality and safety standards.
Characteristics	– Human Intervention: A human operator is actively involved in the AI system’s decision-making process. – Real-Time Interaction: The interaction between humans and AI occurs in real-time or near real-time. – Training and Learning: Human feedback is used to train and refine the AI model. – Error Detection and Correction: Humans can identify and correct errors made by the AI system. – Ethical Considerations: HITL AI enables ethical considerations and bias detection by human operators.
Implications	– Improved Accuracy: Human oversight enhances the accuracy of AI systems, reducing errors and false positives/negatives. – Ethical Safeguards: It helps in identifying and mitigating biases, ensuring ethical AI outcomes. – Adaptability: HITL AI systems can adapt to changing conditions and handle scenarios that were not part of their original training data. – Complex Decision Support: Useful for complex decision-making tasks, such as medical diagnoses, legal analysis, and autonomous vehicles. – Reduced Training Data: AI models may require less initial training data, as humans provide additional guidance.
Advantages	– Enhanced Accuracy: Human oversight reduces AI errors and ensures reliable results. – Ethical Compliance: Helps in aligning AI decisions with ethical and regulatory standards. – Flexibility: HITL AI can handle unforeseen situations and adapt to novel scenarios. – Learning and Improvement: Continuous learning from human feedback improves AI performance over time. – Accountability: Clear attribution of decisions to human operators can establish accountability.
Drawbacks	– Cost and Time: Human involvement can increase the cost and time required for AI processes. – Scalability: It may not be scalable for tasks that demand immediate decisions at a large scale. – Human Error: Human operators can introduce errors or biases into the process. – Complexity: Managing the human-AI interaction and feedback loop can be intricate. – Resource Dependency: Relies on the availability of human expertise.
Applications	– Medical Diagnosis: HITL AI assists doctors in diagnosing diseases, interpreting medical images, and recommending treatment options. – Autonomous Vehicles: Human operators monitor and intervene in self-driving cars when needed. – Content Moderation: For platforms like social media, human reviewers work with AI to identify and remove inappropriate content. – Financial Fraud Detection: Human analysts validate and improve AI models for fraud detection in banking and finance. – Legal Analysis: In legal research, AI systems provide recommendations, but lawyers make final decisions.
Future Trends	HITL AI is expected to play a crucial role in the development of trustworthy AI systems, ensuring ethical use and accountability. It will continue to evolve in tandem with advances in AI technology, particularly in complex and high-stakes domains.
Conclusion	Human-in-the-Loop AI represents a hybrid approach that leverages both human expertise and AI capabilities to address complex tasks and improve the accuracy, ethics, and adaptability of AI systems. While it comes with challenges and costs, it holds significant potential for enhancing decision-making in various domains and ensuring the responsible use of AI.

Table of Contents

Understanding human-in-the-loop AI

Despite the unlimited potential of artificial intelligence, around 80% of all AI projects fail and never make a return on investment.

To reduce the likelihood of failure, teams are now utilizing the human-in-the-loop approach to rapidly deploy models with fewer data and with better quality predictions.

The failure rate of an AI model is due to a statistics-based understanding of the world which means the model can never predict anything with absolute certainty.

To account for this uncertainty, some models enable humans to interact with them via direct feedback which is then used by AI to adjust its “view of the world”.

With this preamble out of the way, we can now define HITL with more clarity. In essence, it refers to an AI system that allows for direct human feedback to a model where predictions fall below a certain confidence level.

Human-in-the-loop can be thought of as greater than the sum of its parts. In other words, it strives to achieve what neither a human nor machine could achieve on their own.

When the machine cannot solve a problem, a human provides help in the form of continuous feedback which, over time, produces better results. Conversely, humans turn to machines for assistance when smart decisions need to be made from vast datasets.

Where is human-in-the-loop integrated?

HITL can be integrated into two machine learning algorithms:

Supervised learning – where algorithms are trained with labeled data sets to produce functions that are then used to map new examples. In this way, the algorithm can subsequently determine functions for data that are unlabeled.
Unsupervised learning – where algorithms take unlabeled data sets and work to find structure and memorize the data in their own way. This can be categorized as a deep learning HITL approach.

Either way, humans check and evaluate the results to validate the machine learning algorithm. If these results are inaccurate, humans refine the algorithm or verify the data once more before feeding it back into the algorithm.

HITL is an iterative approach to building a model that is not unlike agile software development.

The model is trained from the first bit of data, and no more.

More data is then added and the model is continually updated with subject matter experts who build, adapt, and improve the model or adjust tasks or requirements as needed.

When can HITL be used?

HITL is most effective in machine learning projects characterized by a lack of available data. In this situation, people are more capable (at least initially) of making an accurate judgment compared to a machine.

Put differently, they are better able to recognize high-quality training data and feed it into the algorithm to produce better results.

With that in mind, HITL is useful in the following situations:

When algorithms do not understand the input or when data is interpreted incorrectly.
When algorithms are not aware of how to perform a task.
When costly errors need to be avoided during machine learning development, and
When the data are rare or not available. If an algorithm is learning to translate English into a language only a few thousand people speak, for example, it may have trouble sourcing accurate examples to learn from.

How is human-in-the-loop playing a key role in the current AI paradigm?

“This is not a race against the machines. If we race against them, we lose. This is a race with the machines. You’ll be paid in the future based on how well you work with robots. Ninety percent of your coworkers will be unseen machines.”

This is what Kevin Kelly said in The Inevitable, published in 2016. Those words seem spot-on right now!

The technological paradigm which brought us here, moves along a few key concepts to understand, and that enabled AI to move from very narrow, to much more generalized.

And it all starts with unsupervised learning.

Indeed, GPT-3 (Generative Pretrained Transformer 3) is the underlying model, which has been used, to build ChatGPT, with an important layer (as I’ll cover in the coming days) on top of it (InstructGPT) that used a human-in-the-loop approach to smooth some of the key drawbacks of a large language model (hallucination, factuality and bias).

For now, the premise is, GPT-3, launched as a large language model – developed by OpenAI that uses the Transformer architecture – which was the precursor of ChatGTP.

As we’ll see, the turning point for the GPT models was the Transformer architecture (a type of neural network designed specifically for processing sequential data, such as text).

The interesting part of it?

A good chunk of what made ChatGPT incredibly effective is a kind of architecture called “Transformer,” which was developed by a bunch of Google scholars at Google Brain and Google Research.

The key thing to understand is that the information on the web might move away from a crawl, index, rank model to a pre-train, fine-tune, prompt, and in-context learn model!

In that context, human-in-the-loop plays a key role in various parts of this whole process.

Some examples comprise:

Fine-tuning: the fine-tuning process is instrumental in making the AI able to perform very specific tasks. This is a supervised learning approach in the context of large language models, and it’s human-in-the-loop. Here humans label the data and show specific and desired outputs to the AI model, to make it much better at a specific task. The main take here is that the fine-tuning process relies on a much much smaller dataset and sample to make the AI model much much better at specific tasks.
Reinforcement learning: this is simply the generic term to comprise all the aspects of a supervised learning approach, which leverages humans, to make the AI model way better.
Prompt engineering: this is one of the most exciting aspects of the current AI paradigm, where AI models can be made to perform any task, by understanding the context, in a way which makes it possible for them to be both general purpose and specialized.
In-context learning: this is another human-in-the-loop approach, where thanks to the in-context learning the result and output of the AI assistant can get way more relevant.

Case Studies

Content Moderation on Social Media Platforms: Social media companies use HITL to moderate user-generated content for compliance with community guidelines. When AI algorithms flag potentially inappropriate content, human moderators review and make decisions about whether to remove or allow the content.
Medical Diagnosis and Radiology: In healthcare, HITL assists in medical image analysis and diagnosis. AI algorithms can highlight anomalies in medical images, and radiologists provide their expertise to validate the findings, reducing the risk of misdiagnosis.
Language Translation Services: Language translation services like Google Translate use HITL for improving translations. Users can suggest corrections, and AI models learn from these inputs to enhance the accuracy of translations over time.
Autonomous Vehicles: Self-driving cars employ HITL in challenging scenarios. When an autonomous vehicle encounters a situation it can’t confidently navigate, it may request human intervention. Human operators can take control temporarily until the AI system learns from the experience.
Email Filtering: Email providers use HITL to refine spam filters. AI algorithms flag suspicious emails, and users can mark false positives or negatives. This feedback improves the spam detection system’s accuracy.
Virtual Assistants and Chatbots: HITL plays a role in training virtual assistants and chatbots to handle user queries more effectively. When AI fails to understand or respond correctly, human operators can provide guidance and corrections.
Algorithmic Trading: Financial institutions utilize HITL in algorithmic trading. While AI algorithms execute most trades, human traders monitor the system and intervene when market conditions become unpredictable or require human judgment.
Language Model Development: In the development of large language models like GPT-3, HITL is essential. Human reviewers rate model outputs for various prompts, helping fine-tune the model and improve its responses.
Quality Assurance in Manufacturing: In manufacturing processes, AI-driven quality control systems identify defects in products. Human inspectors validate these findings and make judgments about the severity of defects.
Legal Document Analysis: Law firms use HITL for document analysis. AI systems can review large volumes of legal documents for relevant information, but human lawyers ensure the accuracy and relevance of the information extracted.
Customer Support Chatbots: Many businesses employ chatbots for customer support. HITL is used to train these chatbots and continuously improve their responses based on customer interactions.
Natural Disaster Response: In disaster response scenarios, AI systems analyze satellite images to identify affected areas. Human experts validate these findings, ensuring that aid and resources are directed to the right locations.
E-commerce Product Recommendations: Online retailers use HITL to enhance product recommendation algorithms. Customer feedback and interactions with the recommendations help fine-tune the AI system to provide more personalized suggestions.
Drug Discovery: In pharmaceutical research, AI models predict the potential efficacy of new drugs. Human chemists and researchers review these predictions and make informed decisions about which compounds to pursue for further testing.
Educational Technology: Adaptive learning platforms use HITL to customize educational content. When AI algorithms suggest lessons or exercises, educators review and refine these recommendations to better align with students’ needs.

Key highlights of HITL:

AI Project Success and Failure Rates: Many AI projects face significant challenges and have high failure rates, with approximately 80% failing to deliver a return on investment. HITL aims to mitigate these risks by integrating human expertise into the AI development process, increasing the chances of successful deployment.
Addressing Uncertainty: AI models operate based on statistical probabilities and patterns, meaning they can never predict outcomes with absolute certainty. HITL acknowledges this inherent uncertainty and enables humans to intervene when the model’s confidence falls below a certain threshold. This intervention allows for more accurate predictions and decisions.
Collaborative Synergy: HITL recognizes that the combined efforts of humans and machines can achieve results that neither could attain individually. When AI encounters problems it cannot solve effectively, humans step in to provide guidance, corrections, or context. Conversely, AI assists humans in processing vast datasets, making complex calculations, and identifying patterns that might be beyond human capacity.
Integration with Machine Learning Algorithms: HITL can be seamlessly integrated into both supervised and unsupervised machine learning algorithms.
- Supervised Learning: In supervised learning, where algorithms are trained using labeled datasets, HITL allows humans to check and evaluate the results. If the model’s predictions are inaccurate, humans can refine the algorithm or verify the data, leading to improved model performance.
- Unsupervised Learning: Unsupervised learning algorithms work with unlabeled data, seeking to find patterns and structures within it. HITL in this context involves human oversight and validation of the results, ensuring that the algorithm’s interpretation aligns with human understanding.
Iterative Model Development: HITL follows an iterative and collaborative approach, similar to agile software development. It begins with training the model with an initial dataset and then continually updates the model using feedback from subject matter experts. This ongoing collaboration ensures that the AI model improves over time.
Effective Data Utilization: HITL is particularly effective in situations where data is scarce, difficult to obtain, or subject to interpretation. Humans can use their judgment to identify high-quality training data and feed it into the algorithm, enhancing the quality of the model’s predictions.
Use Cases within the AI Paradigm: HITL plays a pivotal role in the evolving AI paradigm, with applications in various areas:
- Fine-Tuning: Fine-tuning is a supervised learning process that relies on HITL. Humans label data and provide specific desired outputs to improve the AI model’s performance for specific tasks. This process significantly enhances the model’s capabilities, even with a relatively small dataset.
- Reinforcement Learning: HITL contributes to reinforcement learning, a broader term encompassing supervised learning approaches that leverage human feedback to enhance AI model performance.
- Prompt Engineering: In contemporary AI, prompt engineering involves designing input prompts to obtain desired outputs. HITL ensures that prompts are effective and contextually relevant, enabling AI models to excel in a wide range of tasks.
- In-Context Learning: HITL facilitates in-context learning, where AI systems continually adapt and improve based on user interactions, making their responses more relevant and effective.