AyGLOO XAI

AI transforms businesses by driving productivity, efficiency and innovation, but too often its solutions are 'black boxes' that offer little or no discernible information about how they achieve their results.


AyGLOO XAI is a product that uses proprietary algorithms to explain how AI models work and why they achieve results.

Turn AI models into a complete and reliable business engine with

Explainability

Understand why your model reach the outcomes and how it works

From the outputs of the black box model we want to explain, we create our model based on a mixture of proprietary and standard algorithms to produce interpretable models and metrics to quantify explainability.


Some of these are:

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  • Intuitive and highly interactive interface that allows the non-technical user to understand the model from both a global and detailed point of view. The results are displayed with simple and intuitive graphs.
     

  • Our tool allows a wide variety of models to be analysed.
     

  • Surrogate crystal box model to interpret the results. It is based on trees, rule lists, linear models and graphs.
     

  • Advanced SHAP analysis to explain the level of importance of each variable.
     

  • Breakdown plots to obtain the contribution of the different variables to the prediction.
     

  • Advanced contrastive explanation analysis to explain the individual decisions made by the AI model.

Monitoring

Continuously check your system performance to detect:

  • Performance drops

  • Outliers

  • Drifting data

Benefits of explainability for stakeholders

Benefits of explainability for your company

Use cases

Data scientist: Understand the model globally.

 

Decision makers and business leaders: Understand the recommendations of the ML model to take the best decisions and explain them.

 

Beneficiaries (customers, patients, etc.): Understand why the decision was made and what data, if different, would change the decision. For example, what customer data has been critical to deny a loan, and what data would have to be different to accept it.

  • Improve transparency and trust

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  • Address pressures such as adaptation to new AI regulations

 

  • Adopt good practices around accountability and ethics

 

  • Monitor and improve of performance 

 

  • Enhance process control

 

  • Prevent unwanted bias such as gender and racial discrimination

  • Credit scoring

 

  • Churn detection

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  • Product recommendation

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  • Fraud detection

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  • Forecasting