What is human-in-the-loop?

Machine learning (ML) has made astonishing strides in recent years, but even the most advanced deep learning models can struggle with ambiguity, bias or edge cases that deviate from their training data. Human feedback can help both improve models and serve as a safeguard for when AI systems perform at insufficient levels. HITL inserts human insight into the “loop,” the continuous cycle of interaction and feedback between AI systems and humans.

The goal of HITL is to allow AI systems to achieve the efficiency of automation without sacrificing the precision, nuance and ethical reasoning of human oversight.

Human-in-the-loop machine learning allows humans to provide oversight and input into AI workflows. Here are the primary benefits of human-in-the-loop:

• Accuracy and reliability

• Ethical decision-making and accountability

• Transparency and explainability

The goal of automating workflows is to minimize the amount of time and effort humans have to spend managing them. However, automated workflows can go wrong in many ways. Sometimes models encounter edge cases that their training has not equipped them to handle. An HITL approach allows humans to fix incorrect inputs, giving the model the opportunity to improve over time. Humans may be able to identify anomalous behaviors using their subject matter expertise, which can then be incorporated into the model’s understanding.

In high-stakes applications, humans can impose alerts, human reviews and failsafes to help ensure that autonomous decisions are verified. They can catch biased or misleading outputs, preventing negative downstream outcomes. Continuous human feedback helps AI models to adapt to changing environments.

Bias is an ongoing concern in machine learning, and although human intelligence is known for being quite biased at times, an additional layer of human involvement can help identify and mitigate bias that is embedded into the data and algorithms themselves, which encourages fairness in AI outputs.

When a human is involved in approving or overriding AI outputs, responsibility doesn’t rest solely on the model or its developers.

Some decisions require ethical reasoning that may be beyond the capabilities of a model. For example, an algorithmic hiring platform’s recommendations might disadvantage certain historically marginalized groups. While ML models have made major strides over the last few years in their ability to incorporate nuance in their reasoning, sometimes human oversight is still the best approach. HITL allows humans, who have better understanding of norms, cultural context and ethical gray areas, to pause or override automated outputs in the event of complex dilemmas.

A human-in-the-loop approach can provide a record of why a decision was overturned with an audit trail that supports transparency and external reviews. This documentation allows for more robust legal defense, compliance auditing and internal accountability reviews.

Some AI regulations mandate certain levels of HITL. For example, the EU AI Act’s Article 14 says that “High-risk AI systems shall be designed and developed in such a way, including with appropriate human-machine interface tools, that they can be effectively overseen by natural persons during the period in which they are in use.”

According to the regulation, this oversight should prevent or minimize risks to health, safety or fundamental rights, with methods including manual operation, intervention, overriding and real-time monitoring. The humans involved must be “competent” to do so, understanding the system’s capabilities and limitations, trained in its proper use and with authority to intervene when necessary. This oversight is intended to encourage the avoidance of harm and proper functioning.

By catching errors before they cause harm, HITL acts as a safety net, especially in high-risk or regulated sectors like healthcare or finance. HITL approaches help to mitigate the “black box” effect where the reasoning behind AI outputs is unclear. Embedding human oversight and control into development and deployment processes helps practitioners identify and mitigate risk, whether that’s technical, ethical, legal or operational risk.

HITL is a great approach for enhancing the performance of machine learning systems, but it’s not without its drawbacks.

• Scalability and cost

• Human error and inconsistency

• Privacy and security

Human annotation can be slow and expensive, especially for large datasets or iterative feedback loops. As the volume of data or system complexity increases, relying on humans can become a bottleneck. Labeling millions of images for a computer vision model with high precision, for example, may require thousands of hours of human labor. Some domains like medicine or law might require even more expensive subject matter experts in the loop. A mis-labelled tumor on a medical imagine scan could result in a serious mistake.

While humans can provide greater accuracy, in some ways they can be more biased and error prone than machines. Humans may interpret data or tasks differently, especially in domains with no clear right or wrong answer. Human annotators, being human, can get tired, distracted or confused when labelling data. They also hold various perspectives on subjective problems, which can lead to inconsistencies in labelling.

Involving humans in internal review processes can raise privacy concerns, and even well-intentioned annotators might unintentionally leak or misuse sensitive data they access during feedback.

Introducing targeted, high-quality human feedback before, during and after training creates a feedback loop that accelerates learning and makes machine learning models more robust, interpretable and aligned with real-world needs. Here are a few ways that human interaction can be embedded into AI workflows.

• Supervised learning

• RLHF

• Active learning

Supervised learning applications require data scientists to correctly label data. This data annotation results in datasets then used to train a machine learning algorithm. This is a workflow where human input is essential and foremost.

For example, a supervised approach in a natural language processing context might involve humans labeling text “spam” or “not spam” in order to teach a machine to successfully make such distinctions. In a computer vision use case, a supervised approach could involve humans labeling a series of images “car” or “bus” or “motorcycle,” so that a model can perform object detection tasks.

In another example, reinforcement learning from human feedback (RLHF) uses a “reward model” trained with direct human feedback, which is then used to optimize the performance of an artificial intelligence agent through reinforcement learning. RLHF is uniquely suited for tasks with goals that are complex, ill-defined or difficult to specify.

In active learning, the model identifies uncertain or low-confidence predictions and requests human input only where needed. This concentrates labeling effort on the hardest or most ambiguous examples, leading to faster and more accurate learning.