Klu raises $1.7M to empower AI Teams  

Human in the Loop (HITL)

by Stephen M. Walker II, Co-Founder / CEO

What is Human in the Loop (HITL)?

Human-in-the-loop (HITL) is a blend of supervised machine learning and active learning, where humans are involved in both the training and testing stages of building an algorithm. This approach combines the strengths of AI and human intelligence, creating a continuous feedback loop that enhances the accuracy and effectiveness of the system. HITL is used in various contexts, including deep learning, AI projects, and machine learning.

Implementing Human in the Loop

To implement Human in the Loop (HITL) in AI systems, follow these steps:

  1. Identify tasks for human intervention — Determine which parts of the AI process can benefit from human judgment, such as data annotation, model training, or quality control.
  2. Integrate human feedback mechanisms — Create interfaces or tools that allow human operators to provide feedback to the AI system effectively.
  3. Set up a continuous learning loop — Ensure that the AI system can learn from human inputs and improve over time, creating a dynamic system that evolves with each interaction.
  4. Monitor and evaluate performance — Regularly assess the performance of the HITL system to ensure that it meets the desired standards and continues to improve.
  5. Iterate and refine — Use insights gained from monitoring to refine the human-AI interaction process, optimizing for efficiency and accuracy.

By carefully integrating human expertise at strategic points within the AI workflow, HITL systems can achieve higher levels of performance and reliability.

Human in the Loop (HITL) Benefits

Benefits of human-in-the-loop include:

  1. Data annotation — Human data annotators label the original data, which includes both input data and the corresponding expected output.
  2. Training — Human machine learning teams input the correctly labeled data to train the algorithm, allowing the algorithm to uncover insights, patterns, and relationships within the dataset.
  3. Testing and evaluation — In this stage, humans focus on correcting any inaccurate results that the machine produced, actively participating in the learning process.

HITL has been applied in various industries, such as content moderation systems, autonomous vehicles, and healthcare. For example, in content moderation systems, human reviewers oversee and make decisions on flagged or potentially objectionable content. In autonomous vehicles, HITL allows humans to intervene and take control when needed, ensuring the vehicle's safety and efficiency.

Human-in-the-loop AI approaches combine human and machine intelligence to create more accurate and effective AI systems. By involving humans in the training and testing stages, the system can leverage their expertise and understanding of complex tasks, leading to better outcomes and improved decision-making.

What are some examples of Human-in-the-loop Systems?

Human-in-the-loop (HITL) AI systems are designed to incorporate human interaction and feedback, combining the efficiency of automation with the nuanced understanding and decision-making capabilities of humans.

At its core, Klu.ai is a human-in-the-loop platform that enables AI teams to manage various aspects of AI development and large language models. It provides a suite of features including real-time collaboration tools for team-based projects, Klu Context for integrated retrieval augmented generation (RAG), evaluation metrics for model performance, and Klu Studio, a playground for testing and optimization.

Klu.ai also offers customization options for LLM prompts, automated and human data labeling, A/B testing capabilities for iterative improvements, and robust user feedback mechanisms to incorporate human preferences back into your models. To ensure the safety and privacy of data, Klu.ai incorporates stringent data privacy measures and security protocols (GDPR, SOC2, and more).

Here are some examples of HITL AI systems:

  1. Interactive Machine Learning — Dr. Rebecca Fiebrink, a professor at the Creative Computing Institute at University of the Arts London, developed Wekinator, a software for real-time, interactive machine learning. This software allows humans to iteratively train tools by example, refining the system by showing it new examples of control mappings for tasks like musical instruments or video games.

  2. Healthcare — A 2018 Stanford study found that HITL AI models outperformed both AI-only and human-only models in the healthcare sector. These systems can improve accuracy while maintaining human-level standards of work, which is particularly important in fields like healthcare where precision is critical.

  3. Content Moderation Systems — In these systems, human reviewers oversee and make decisions on flagged or potentially inappropriate content. This allows for the efficient processing of large amounts of data, while still maintaining human oversight to catch errors or nuanced cases that the AI might miss.

  4. Quality Control and Assurance Checks — In industries like vehicle or airplane manufacturing, HITL systems can be used to ensure the safety and accuracy of critical components. While machine learning can be helpful for inspections, human oversight is essential to ensure that the equipment meets the necessary standards.

  5. Data Annotation — In the process of training machine learning models, human annotators play a crucial role in labeling and annotating datasets. This human feedback allows the models to learn faster and more effectively than they would on their own.

These examples illustrate the broad range of applications for HITL AI systems, from creative endeavors to critical safety checks. The common thread is the combination of human expertise and AI efficiency to achieve better results than either could on their own.

How are leading AI teams using human in the loop approaches?

Leading AI teams employ Human-in-the-Loop (HITL) to improve AI systems by integrating human expertise with AI capabilities. This method ensures that AI and human intelligence complement each other. In decision-making, HITL combines pattern-recognition algorithms with human decision-makers, improving efficiency and ensuring more effective outcomes. HITL is vital for both supervised and unsupervised learning, with human input crucial for identifying model and data issues.

Generative AI benefits from HITL, where human interpretation of AI insights is crucial for recognizing progress and identifying micro-opportunities. HITL mitigates bias in AI programs by providing human oversight to detect and correct prejudices that may arise from historical data.

In operations and incident management, HITL ensures human oversight of AI-generated automations, promoting collaboration and transparency, and enabling team members to comprehend AI processes. In manufacturing, particularly in vehicle and airplane part production, HITL augments AI inspections with human monitoring to bolster part reliability.

For computer vision, HITL enhances AI pipelines, such as in industrial product manufacturing, where humans make final judgments on defects or abnormalities detected by AI.

What are some common applications of Human in the Loop in AI?

Human-in-the-loop (HITL) is a machine learning approach that combines human and artificial intelligence to improve the accuracy and efficiency of AI systems. Some common applications of HITL in AI include:

  1. Data augmentation — HITL can be used to enhance the quality of datasets, especially when they are rare or of low quality. Humans can provide labeled data for model training, helping the AI system to better understand and learn from the data.

  2. Active learning — In this approach, humans handle low-confidence units and feed them into the AI system. This helps improve the accuracy and reliability of the model.

  3. Labeling and annotation — Humans can be involved in the process of labeling and annotating data for AI systems. This helps the AI system to better understand and learn from the data.

  4. Tuning and testing — Humans can help tune AI models for higher accuracy. For example, human annotators can score decisions made by the AI system, providing valuable feedback for improving the model's performance.

  5. Quality assurance and oversight — In critical applications, such as autonomous vehicles or medical devices, human oversight is essential for ensuring safety and reliability. HITL can be used to provide continuous feedback and monitoring of AI systems.

  6. Content moderation — HITL can be applied in content moderation systems, where human reviewers oversee and make decisions on flagged or potentially objectionable content.

These applications demonstrate the value of combining human intelligence with AI systems, allowing for better decision-making and improved performance in various domains.

How does Human in the Loop differ from other forms of reasoning?

Human-in-the-loop (HITL) systems combine the strengths of both humans and machines to perform tasks more effectively than either party could alone. In the context of AI and Large Language Models (LLMs), HITL can enhance the decision-making process by leveraging human expertise and experience. Here are some key aspects of how HITL differs from other forms of reasoning:

  1. Human expertise — HITL systems rely on human knowledge and understanding, which can be difficult for machines to replicate. For example, humans are better at recognizing faces in crowds or understanding context-specific information.

  2. Communication — HITL emphasizes the importance of communication between humans and machines, allowing for a more nuanced understanding of the system's output and potential improvements.

  3. Active involvement — In HITL systems, humans are not passive observers but actively participate in the decision-making process, providing guidance and intervention to ensure accurate and ethically sound outcomes.

  4. Feedback loop — HITL allows for a feedback loop between humans and machines, enabling continuous improvement and adaptation of the AI model based on human input.

  5. Task-specific roles — HITL systems can leverage human expertise in specific tasks, such as content moderation or security-critical functions, where humans can provide valuable insights and judgments that machines may not be able to replicate.

Human-in-the-loop systems offer a unique approach to reasoning by combining the strengths of both humans and machines. This approach allows for more accurate and ethically sound decision-making, particularly in tasks that require human expertise and understanding.

What are some benefits of using Human in the Loop in AI?

Human-in-the-Loop (HITL) is a crucial aspect of AI and Machine Learning (ML) projects, as it involves incorporating human judgment and feedback into the algorithms. Some benefits of using HITL in AI include:

  1. Automation of Complex Tasks — Human experts can provide input and guidance at various stages of the machine-learning process, ensuring that the models are trained on the most relevant data.

  2. Enhanced Decision-Making Capabilities — HITL helps identify and correct errors in AI systems, improving their overall performance and reliability.

  3. Active Learning — HITL allows for continuous feedback and improvement of AI models, ensuring that they learn from their mistakes and become more effective over time.

  4. Continuous Feedback Loop — HITL creates a continuous feedback loop between humans and machines, ensuring that AI systems can adapt and improve based on human input.

  5. Unsupervised Learning — HITL can be used in conjunction with unsupervised learning strategies, allowing AI systems to learn from human expertise without the need for labeled data.

  6. Increased Efficiency — HITL can save time and resources by focusing on specific tasks and allowing machines to handle the rest, leading to more efficient systems.

  7. Reduced Bias — HITL helps detect and correct biases in AI systems, ensuring that they produce accurate and fair results.

  8. Safe and Stimulating Jobs — HITL creates safe and stimulating jobs for human workers, as they can focus on more intellectually challenging tasks and contribute to the development of AI systems.

  9. Higher Job Satisfaction — HITL can lead to higher job satisfaction among human workers, as they can take on more challenging roles and contribute to the improvement of AI systems.

  10. Improved AI System Performance — HITL can improve the performance of AI systems in various industries, such as healthcare, cybersecurity, natural language processing, and transportation.

However, there are also some challenges associated with using HITL in AI. One challenge is that it can be difficult to determine when HITL is appropriate. In some cases, it may be more appropriate to use another type of reasoning. Additionally, HITL can sometimes lead to incorrect conclusions.

What are the challenges of using Human in the Loop systems?

Challenges of using Human-in-the-Loop (HITL) systems include:

  1. Scalability — HITL systems often struggle with scalability, as they rely on human participation for decision-making and oversight. This can be particularly problematic when dealing with a large number of decisions or when the system needs to adapt to changing conditions.

  2. Time constraints — The time available for humans to make decisions in HITL systems may be insufficient, as they often need to balance their own tasks and responsibilities with the demands of the HITL system.

  3. Data quality — Ensuring that the data provided to humans for decision-making is accurate and comprehensive is crucial for the success of HITL systems. However, this can be challenging due to the complex and dynamic nature of human-in-the-loop interactions.

  4. Regulatory and legal challenges — Implementing HITL systems may raise questions about accountability, oversight, and compliance with relevant laws and regulations. This can create challenges for organizations that want to adopt HITL approaches but are unsure about the implications of doing so.

  5. Ethical issues — HITL systems can raise various ethics concerns, such as potential conflicts of interest, privacy concerns, and the risk of algorithmic biases. These issues need to be addressed to ensure the responsible development and implementation of HITL systems.

  6. Performance — HITL systems can be slow and cumbersome, as humans need to verify the accuracy of machine predictions and provide feedback for improvement. This can lead to delays and inefficiencies in the decision-making process.

  7. Human expertise — While HITL systems can benefit from human expertise, it is essential to ensure that the humans involved have the necessary skills and knowledge to contribute effectively to the decision-making process. This can be challenging to achieve, especially when dealing with complex tasks or specialized domains.

What are some strategies for overcoming the challenges of implementing human-in-the-loop ai systems?

Implementing Human-in-the-Loop (HITL) AI systems can be challenging, but several strategies can be employed to overcome these challenges:

  1. Active Learning — This Machine Learning (ML) technique involves an algorithm actively selecting the most informative examples from a pool of unlabeled data for annotation or labeling by a human expert. This approach can primarily overcome the challenge of human annotation, which is integral to the HITL ML process as it helps train models and improve their accuracy.

  2. Leveraging Human Expertise — HITL ML acknowledges the cognitive abilities of humans in comprehending complex or abstract concepts and handling ambiguous scenarios. The ML model can learn from human insights and improve their overall performance. This collaborative process improves the accuracy and efficiency of AI models, can address potential biases and ethical concerns, and allow for ongoing refinements.

  3. Confidence Scoring — Having a confidence score alongside the ML model's predictions captures how likely predictions are to be correct. This makes it possible to separate predictions into "trivial, no human intervention necessary until QA" and "the model isn't sure; a human should probably look into this." By doing so, a business can release humans from routine cases while avoiding catastrophic failure should there be any previously unseen data.

  4. Human Appeals of AI/ML Decisions — This approach proposes that human expert judges be included via appeals processes for review of algorithmic decisions. Thus, the human intervenes only in a limited number of cases and only after an initial AI/ML judgment has been made. Human reviewers can add more nuanced clinical, moral, or legal reasoning, and they can consider case-specific information that is not easily quantified and, as such, not available to the AI/ML at an initial stage.

  5. Continuous Feedback Loop — HITL aims to achieve what neither a human being nor a machine can achieve on their own. When a machine isn’t able to solve a problem, humans need to step in and intervene. This process results in the creation of a continuous feedback loop. With constant feedback, the algorithm learns and produces better results every time.

  6. Pareto Principle — The idea is that ML models may have trouble getting above 80% accuracy. The hardest 20% of examples are responsible for 80% of the errors made. By combining human and machine intelligence, humans can address the difficult few. This still allows for an 80% reduction in human work, with greater improvements as the model learns from feedback.

The key advantage of HITL strategies is leveraging the advantages of both human intelligence and ML. These strategies allow for the efficient use of ML while maintaining the accuracy of human input.

What are some strategies for overcoming the challenges of implementing human-in-the-loop LLM-powered systems?

Implementing human-in-the-loop (HITL) systems in large language models (LLMs) can help overcome challenges such as ensuring accuracy, maintaining safety, and addressing trust issues. Some strategies for overcoming these challenges include:

  1. Identify critical use cases — Determine which tasks and decisions are most crucial to the success of your organization and require the highest level of human oversight.

  2. Establish a process for validation — Develop a clear and systematic procedure for human experts to review and validate AI-generated outputs, ensuring accuracy and maintaining safety.

  3. Facilitate sharing insights and improvements — Encourage open communication between human experts, allowing them to share stories of AI-driven successes and build confidence in the technology.

  4. Ensure accuracy — Human experts can verify the accuracy of AI-generated responses, helping to maintain a high level of accuracy and avoid costly errors.

  5. Improve safety and precision — In situations where human-level precision is required for safety, such as manufacturing critical parts for vehicles or airplanes, LLMs can be monitored by humans to ensure quality.

  6. Use HITL in conjunction with other fine-tuning methods — Combining HITL with other fine-tuning techniques can help improve the performance of language models while addressing potential biases and improving overall reliability.

  7. Set clear guidelines and processes — Establish clear boundaries for AI systems, define their intended use cases, and ensure that they don't act beyond their intended scope. Introduce effective governance mechanisms to oversee the AI system and maintain its alignment with human-centered goals.

By implementing these strategies, organizations can successfully integrate HITL systems into their LLMs, addressing the challenges of accuracy, safety, and trust while harnessing the power of AI technology.

How can Human in the Loop be used to improve AI applications?

Human-in-the-Loop (HITL) is a concept that combines human and machine intelligence to improve AI applications, ensuring accuracy and high-quality results. HITL can be used in various industries and applications, such as natural language processing (NLP), computer vision, and content moderation. Some benefits of using HITL include:

  1. Ensuring accuracy — HITL ensures that AI models learn from accurate and reliable data, as humans provide constant feedback and corrections.
  2. Improving safety and precision — In situations where high precision and safety are crucial, such as manufacturing or healthcare, HITL can help maintain human-level standards of work.
  3. Incorporating human judgment — HITL systems value human agency, incorporating human preference, taste, and judgment into the decision-making process.
  4. Continuous feedback loop — HITL creates a continuous feedback loop between humans and machines, allowing for iterative learning and improvement.

HITL can be applied in various stages of the AI development process, such as data annotation, training, and testing and evaluation. For example, in the financial industry, HITL machine learning can be used for loan processing, data analysis, and fraud detection, with human experts validating machine learning models on the fly. This approach helps to facilitate digital workflows and improve decision-making.

Human-in-the-Loop is an essential technique for improving AI applications by combining human and machine intelligence, ensuring accuracy, safety, and precision in various industries and applications.


The Premier Platform for AI Feature Development

Klu.ai is the premier human in the loop platform that merges software best practices with LLM requirements, empowering teams to enhance AI capabilities. It provides a unified workspace for PMs, Engineers, and Domain Experts to collaborate on AI features, streamlining the development process. Efficiently manage and evaluate LLM prompts, collaborate seamlessly across teams, and ensure robust evaluation and monitoring for enterprise-level deployment.

State-of-the-Art Playground for LLMs

Our state-of-the-art playground centralizes prompt management and iteration, allowing for comprehensive evaluation and monitoring. Teams can test and refine prompts, chains, or agents before production deployment, leveraging private data to fine-tune models for superior performance. The playground offers customization and optimization tools, prompt management with deployment controls, and a top-tier environment for prompt evaluation.

Collaborative AI Development

AI development is a collaborative endeavor, and Klu.ai recognizes that inefficient workflows hinder progress. Our platform addresses common challenges such as juggling prompts between OpenAI and code, tracking prompts in spreadsheets, and labor-intensive manual evaluations and workflows. We provide a collaborative playground with version history, backtest changes to confidently update models, gather feedback, conduct quantitative experiments, and seamlessly integrate with production applications.

Enterprise Empowerment and Security

Klu.ai empowers enterprises to implement AI safely and securely across organizations. We prioritize data privacy and security, ensuring that you retain full ownership of your data and models. Our platform offers responsive support and comprehensive AI application monitoring with reliable support from AI experts. Additionally, we facilitate knowledge sharing to adopt industry best practices and disseminate knowledge throughout your organization.

Leading the Way in LLM Fine-Tuning and Prompt Engineering

Stay ahead with fine-tuning capabilities for GPT-3.5 and GPT-3.5-Turbo, and empower domain experts in prompt engineering with version control. Our platform provides comprehensive model support, including OpenAI, Anthropic, Llama2, and custom models. Manage test datasets, create custom metrics, and integrate with CI/CD systems to maintain a competitive edge in LLM development.

What capabilities set Klu.ai apart?

  • AI features
  • LLM prompts
  • Klu.ai
  • Collaboration
  • Evaluation
  • Monitoring
  • Playground
  • Tools
  • Customization
  • Optimization
  • A/B testing
  • Model performance
  • User feedback
  • Data privacy
  • Security

More terms

What is OpenCog?

OpenCog is an artificial intelligence project aimed at creating a cognitive architecture, a machine intelligence framework and toolkit that can be used to build intelligent agents and robots. The project is being developed by the OpenCog Foundation, a non-profit organization.

Read more

What is machine listening?

Machine listening, also known as audio signal processing or computational auditory scene analysis, refers to the use of computer algorithms and models to analyze and extract information from audio signals. This field has applications in various areas such as speech recognition, music information retrieval, noise reduction, and biomedical engineering.

Read more

It's time to build

Collaborate with your team on reliable Generative AI features.
Want expert guidance? Book a 1:1 onboarding session from your dashboard.

Start for free