Human In The Loop AI

What is Human In The Loop AI?

Human-In-The-Loop AI (or HITL), is a system where the AI does the heavy data work, but humans review, correct, and guide the results. This human oversight ensures the final output stays accurate, safe, and higher-quality than what the AI could produce on its own.

HITL ensures that humans have meaningful oversight in the AI systems role, the data that is uses and the outputs it generates in any given task. Essentially HITL is a collaboration between human intelligence, machine learning and AI systems to achieve a goal.

Why is human in the loop important?

In HITL, humans play an important role in supervising, guiding or correcting the AI machines actions. This means that there is meaningful human oversight in the use of any AI systems.

This is important since, whilst machines can process vast amounts of data, and identify patterns that humans might miss, there are some tasks that machines currently struggle with. For instance, machines cannot yet provide human level critical thinking, judgement, expertise and real world experience.

For Immovision, we identified HITL as important when searching for a real-estate broker for your project. This is for several reasons:

• Machines can make mistakes, humans provide the safety net
• Solving addressing bias and fairness
• Making results understandable

Let’s take a quick look at each of these points now.

Machines can make mistakes, humans provide the safety net

No one wants to blindly follow the advice of a machine. This is especially the case when it comes to a large financial decision. For instance, when buying or selling a house worth several hundred thousands of dollars.

Therefore, whilst the AI we use can quickly identify trends and patterns in real-estate data. Having human oversight will improve accuracy and trustworthiness of the AI results. For example, an AI might recommend an agent who appears strong on paper because they closed many transactions last year. But a human reviewer may notice that most of those deals were low-complexity rental transactions and not the kind of $900k family home sale you’re planning. Or the AI might miss that the agent recently switched teams or changed their name after receiving regulatory fines.

By adding a human safety layer, we make sure the recommendations align with real-world expertise, not just raw data.

Solving addressing bias and fairness

“Addressing bias” occurs when the AI systems inherit bias’ from the data they are trained on. For example, if an AI only looks at public online reviews, it may unfairly favor agents who are good at marketing themselves, not necessarily the ones who perform well. In real-esate, we have seen addressing bias creep into the AI systems in three ways:

• Popularity bias: Agents with lots of reviews or strong social media presence get ranked higher, even if their actual sales performance is poor.
• Activity bias: Agents who post more online appear “more active,” even if they haven’t closed any real transactions recently.
• Data-visibility bias: Strong agents who don’t publish their sales online may be underestimated simply because the AI doesn’t see the full picture.
• Name-change bias: Agents who rebrand or change brokerages can “erase” negative reviews, and a naive AI might treat them as new, clean profiles.

In our system, the HITL layer corrects these biases.

Our consultants review the AI’s recommendations, add missing context (like off-market transactions, neighbourhood expertise, or regulatory history), and make sure the final shortlist reflects actual agent quality, not just data quirks. In short, we actively catch and correct issues, so that the AI doesn’t produce misleading results.

Making results understandable

AI systems often make decisions in ways that aren’t obvious from the outside. This is because they can process massive amounts of data and spot patterns that humans miss. For instance, our AI Agent Matching tool ranks agents based on dozens of signals. It considers transaction history, neighbourhood activity, pricing performance, behavioural indicators, all of which are contextualized against the real-estate market at this precise moment. However, this does not mean the reasoning is easy to follow.

For example, our AI Agent Matching tool might recommend three top-performing Montreal realtors. But unless you know how to read the data it can be hard to understand or verify why these agents were recommended.

That’s why we include a human in the loop. In our case this is a real-estate consultant, who will walk you through the results and explains why each agent was recommended. They will also highlight the strengths and potential risks the AI has identified, and checks that the shortlist truly fits your specific situation.

The result of this process is that you don’t just get a list of realtors. You understand why this list has been created, so that you can trust the results and can make a confident decision backed by both data and expert judgment.

Final remarks

By combining human expertise with advanced AI, Immovision dramatically improves the way you discover real-estate opportunities.

Whether you’re trying to find the best realtor for your project, identify high-potential investment properties, or time the sale of your home for maximum return, our Human-In-The-Loop systems give you an edge that pure AI, or pure human judgment, can’t achieve alone.

If you want to experience this for yourself, start with our Agent Matching Tool.

This tool scans 17,000+ licensed realtors in Quebec, filters them based on your exact project, and delivers a tailored shortlist of top performers, all in under a minute. And right now, the entire service is completely free to use.