In recent years, artificial intelligence (AI) has become a key component in many sectors. When talking about AI, it's easy to immediately think of tools like ChatGPT or virtual assistants, but the reality is that AI is also used more quietly but very powerfully to make business decisions every day: from how much to pay for an insurance policy, to how to plan electricity production based on expected demand.
In these cases, artificial intelligence works through machine learning models: systems that learn patterns from historical data to make predictions. They are very effective, but they can also be complex and difficult to understand for those who don't work directly with them.
This is where interpretable twin models come in, a tool that allows translating how the machine learning model "thinks" in a clear and adapted way to different profiles, whether technical or business.
This proprietary AyGLOO technique combines with other explainable AI methods and is displayed in an intuitive and simple dashboard, applicable to existing machine learning models in companies.
What are interpretable twin models?
Imagine you have a very advanced Machine Learning model that makes decisions based on dozens or even hundreds of variables. This model is accurate, but difficult to understand. An interpretable twin model is like a "simplified and temporary version" of the original model, built from a set of variables selected by the user, that mimics its behavior approximately.
This allows teams to:
- See which groups of factors influence decisions the most
- Use their own indicators or KPIs even if they are NOT part of the original model and see if they are really aligned with the model's decisions
- Get explanations in their own professional language, whether technical or business.
Additionally, these models come with an "explainability score" that indicates how faithfully that twin model reflects the behavior of the original model and the contribution of the variables of each twin model at a global level and for each case.
Example: insurance policy subscription
An insurance company uses Machine Learning to decide what premium to assign to each new customer based on their profile. The original model takes into account many variables: age, vehicle type, average claims in their area, digital app usage, etc.
However:
- The technical person wants to know if claims data and risk profile are really influencing.
- The business team wants to interpret the decision based on their usual indicators, such as product type, expected customer profitability, or seniority.
With interpretable twin models:
- The technician can build a twin model with only technical risk variables and see if they alone explain the original model's behavior well.
- The business can do the same with their profitability KPIs, and understand if the model is aligned with the commercial strategy.
Thus, both profiles can dialogue and make decisions with solid foundation, without needing to reinterpret the entire model or retrain anything.
Example: energy demand prediction
In the energy sector, AI is used to predict electricity demand for the coming days, which is key to knowing how much energy to generate or buy.
The original model can use complex data: sensors, weather, historical data, hourly consumption behavior... But:
- The technical team wants to understand what type of meteorological variables are most relevant.
- The operational planning team wants to check if the AI is responding correctly to their classic operational indicators, such as average temperature, day type (working or holiday), or hourly pattern.
Thanks to interpretable twin models:
- They can create simple models with separate variable groups (weather only, usage patterns only, historical only).
- See in seconds which group explains the model's prediction more.
- Use that information to adjust generation strategies or detect anomalies.
Why are they useful for all sectors?
The main advantage of these models is that they allow understanding and adapting AI to each type of professional, without sacrificing rigor or precision. Whether you work in insurance, energy, banking, industry or public sector, this tool allows:
- Technicians to audit, debug and validate the model in a modular way.
- Business to see if the model supports their hypotheses, KPIs and real objectives.
And all this without having to retrain the model or continuously depend on technical teams.
In summary
Interpretable twin models are a practical way to explain and adapt the functioning of machine learning models without sacrificing precision or rigor. They allow bringing artificial intelligence closer to those who really make decisions and need to understand how models work: professionals. Technical and business teams gain in understanding, agility and alignment.