What is human in the loop?

It acknowledges that, while powerful, AI can make mistakes, lack context, or perpetuate hidden biases. The human in the loop approach reframes an automation problem as a human-computer interaction (HCI) design problem.

In machine learning, HITL involves humans strategically guiding the computer to make better decisions during the model-building process. This targeted approach improves upon random sampling by having humans identify and provide the most valuable data to refine the model, increasing accuracy and efficiency.

This interaction between humans and machines creates a continuous feedback loop where:

There are three essential stages when it comes to human in the loop AI training:

Data annotation: Human data annotators label the original data, including input and corresponding expected output data. If you want an AI to extract line items from receipts, you will provide thousands of receipts where data annotators have carefully highlighted each line item.

This labeled data then becomes the foundation for training the AI model.

Training: Then, human ML teams input the correctly labeled data to train the algorithm. The algorithm can discover the dataset’s insights, patterns, and relationships based on this data. The algorithm aims to make accurate decisions when presented with new data.

Testing and evaluation: In this stage, humans focus on correcting any inaccuracies that the machine produces.

Practical benefits of human in the loop design include:

Improved accuracy: Humans excel at tasks like understanding nuance, interpreting ambiguous data, and applying common sense reasoning. These skills can refine AI models that might produce incorrect or incomplete results.

Bias mitigation: AI systems can inherit biases from the data they’re trained on or the algorithms they employ. Human in the loop processes help identify and address these biases, promoting fairness and ethical AI.

Enhanced explainability: While AI systems can produce impressive results, their decision-making processes are often seen as a “black box.” HITL enables humans to understand the reasoning behind AI predictions, which is essential for building trust.

Domain expertise: Humans possess valuable knowledge and insights about their respective domains. HITL ensures this expertise helps shape and guide AI systems, leading to more relevant and actionable solutions.

Adaptability: Real-world situations are dynamic. Human input allows AI systems to handle new scenarios or adjust to changing environments that may differ from their training data.

A comparison table of the three different workflows:

There are various ways HITL is implemented, depending on the specific AI system and the task at hand:

Image recognition: AI models trained to detect specific objects in images (like tumors in medical scans) may flag uncertain cases for further review by a human radiologist. This helps avoid misdiagnosis and ensures accuracy.

Natural language processing: Sentiment analysis algorithms classifying customer reviews might present borderline cases to humans for validation. This helps ensure that the model captures subtle nuances of language and avoids misinterpretations.

Self-driving cars: While self-driving technology has advanced, HITL is critical for safe deployment. Humans might monitor the car’s decisions in real-time to intervene or gradually transfer more control to the AI system as its capabilities improve with human feedback.

Content moderation: Social media platforms rely on AI to flag potentially harmful content, but human moderators often make the final judgment calls. This balanced approach helps protect users by addressing the limitations of automated systems.

Fraud detection: HITL intelligent systems can combine AI anomaly detection with human expertise. Suspicious transactions flagged by AI are reviewed by analysts who can leverage their investigative skills and contextual knowledge to verify or dismiss the alerts.

Intuitive user interface: HITL relies on seamless interaction between humans and machines. User interfaces (UI) must be clear, efficient, and provide meaningful information to allow humans to make informed decisions.

Feedback mechanisms: Establish clear channels for humans to provide feedback to the AI system. This may involve simple labeling, explanations for their choices, and even direct modifications to the model’s output.

Trust and explainability: Explainable AI (XAI) techniques help humans understand how the AI system arrived at a decision. This transparency builds trust and facilitates productive collaboration.

Training for humans: Humans involved in HITL processes need to understand the strengths and limitations of the AI system they’re working with. This includes training on how to provide optimal feedback and avoid inadvertently introducing their own biases.

HITL can be strategically integrated at different points in a workflow. The best placement depends on your specific goals and current automation level.

The success of any HITL system hinges on the quality of human input. Training and clear guidelines for your human team are essential.

HITL at the beginning of a loop is ideal when you need more pre-existing AI models and need to create a solid data foundation for future automation. Humans are crucial in curating and labeling raw data, ensuring quality training sets for your AI.

Best suited for:

Example: You have thousands of unstructured invoices and want to train an AI model to extract essential data. HITL at the beginning, would involve humans labeling key data points (invoice number, date, total, etc.) within invoices. This labeled data forms the basis for training your AI model.

This approach maximizes existing automation and uses humans to control quality and refine results. It’s ideal for fine-tuning or tasks where maximum accuracy is critical.

Even if you initially choose HITL at the end, as your AI models mature, you may gradually require less human intervention.

Best suited for:

Example: Your workflow already uses an AI model to extract invoice data with 80% accuracy. Placing HITL at the end means humans review, verify, and correct any errors by the AI, aiming for >99% accuracy.

Combining HITL at the beginning (data creation) with HITL at the end (quality control) can achieve maximum benefit.

Humans initially curate raw data and create well-labeled datasets. This ensures that the AI model learns from high-quality, relevant examples.

The labeled data trains the AI model, allowing it to automate tasks and generate initial outputs or predictions.

Then, humans review the AI’s output, primarily focusing on uncertain cases or potential errors. They provide corrections, additional context, or refine the AI’s decisions.

Feedback from humans is used to retrain the AI model continuously. This creates a powerful loop where AI and humans improve at their respective tasks over time.

When incorporating HITL into your operations, a critical decision is to leverage an external provider or build an internal team. Each approach offers distinct advantages and trade-offs:

Scalability: Incorporating human input can create bottlenecks, especially when dealing with large volumes of data or real-time decision-making scenarios. HITL design should carefully balance the need for human input with efficiency.

Latency: Adding human involvement can introduce delays. Choosing HITL strategies that minimize response times is crucial, particularly in safety-critical or time-sensitive applications.

Potential disagreement: Involve multiple human annotators to reduce the influence of individual perspectives and mitigate issues with subjective judgments.

Cost: Human involvement adds cost and complexity to AI projects. Organizations must weigh potential gains in accuracy or ethical robustness against the resources required for HITL implementation.

Here’s a breakdown of the key differences between human in the loop (HITL) and human on the loop (HOTL):

Human out of the loop (HOOTL) describes a system where artificial intelligence (AI) operates with minimal or no human intervention.

Humans may have designed the system and set initial parameters but are not actively involved in ongoing decision-making processes. AI makes decisions and takes actions largely independently, based on its programming and the data on which it has been trained.

More advanced HOOTL systems could continuously learn and adapt their behavior without human input.

As AI systems grow increasingly complex and impact more aspects of human life, the importance of HITL will only increase. Here’s what the future holds:

Augmented intelligence: The focus will shift from replacing human judgment to augmenting it. HITL will become a framework for humans and AI to work together, each contributing their unique strengths for optimal outcomes.

Adaptive feedback loops: AI systems will better understand the type of human feedback most helpful and when to solicit it. This could lead to more dynamic and targeted HITL processes.

Human-centered design: Ethical AI and responsible innovation will make HITL a core component of the AI development process. This ensures that humans are always considered vital stakeholders.

New tools and platforms: We’ll see the emergence of specialized tools and platforms designed to facilitate seamless HITL interactions, providing tailored interfaces, feedback mechanisms, and explainability features.

Cross-domain collaboration: HITL success requires expertise in AI, machine learning, human factors, and user interface design. Interdisciplinary collaboration will be essential for creating compelling and human-aligned AI systems.

HITL isn’t just about better technology – it’s about the ethical use of AI. By intentionally integrating humans into AI processes, we ensure that critical decisions don’t solely rely on algorithms that may have blind spots or unintended consequences. While AI learns to understand the world in its data-driven way, humans must continuously remind these robust systems of our values, our unique capacity for empathy, and the importance of fairness.

HITL is a testament to the belief that the most significant advancements come not from humans versus machines but from harnessing distinct strengths in a continuous feedback loop. It empowers us to proactively shape the future of AI, ensuring that the powerful systems we create are trustworthy, aligned with human values, and ultimately serve to enhance our lives rather than control them.