AyGLOO - Explainable AI woman explaining IA

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 standard and proprietary algorithms, such as SHAP-ISA, to explain how AI models

work and, why they get their results. It allows a much

more powerful and intuitive analysis of the model and dataset.

Turn AI models into
a complete and reliable business engine with

Explainability & Fairness

TECHNIQUES

We use a mixture of proprietary and standard algorithms:

  • Transform complex AI models (neural networks, statistical models, clustering...) into an easy-to-understand natural language rule set
     

  • Proprietary explainable AI algorithms to structure and present analysis to the non-technical user in an intuitive way, such as Shapley Value Analysis, and summarization by critical segments identified by our AI algorithm. This is an own technique developed by AyGLOO called “SHAP Intelligent Segment Analysis” (SHAP-ISA). This allows a much more powerful and intuitive analysis of the model

  • Explain the most relevant variables in the prediction and their weights
     

  • Explain how each instance is processed by the model and its counterfactuals
     

  • Show fairness tools (model and dataset biases)

  • Interactive “what-if” analysis by variable or group of variables

  • Automatic generation of explainability report

Features

Continuously monitor your algorithms with a dashboard:

  • Easy to use and highly interactive dashboard 

  • Results are displayed with simple and intuitive graphs

  • Allows the non-technical user to understand the model from a global and detailed point of view

  • Proprietary explainable AI algorithms to structure and present analysis to the non-technical user in an easy way

 

  • Allows you to select variables to analyze from the model or from the dataset

  • Performs analysis by critical segments identified by the model

  • Drag and drop tool

  • Permits a wide variety of technologies and models including Reinforcement Learning

  • Customizable product according to client requirements

Following, some of the graphics
of the product AyGLOO XAI

(Pixelated graphics for confidentiality reasons).

conjuntoreglas.png AyGLOO - Explainable AI

Set of rules

Sesgos_discriminación.png AyGLOO - Explainable AI

Bias/discrimination

contribution.png AyGLOO - Explainable AI

Contribution

importancias_atributos.png AyGLOO - Explainable AI

Importances

causality_graph.png AyGLOO - Explainable AI

Causality graph

what-if.png AyGLOO - Explainable AI

What-if Analysis

And much more…

 

Configurable product according to your needs so that you can use the models you need in your analysis just by dragging and dropping.

 

The first and unique product on the market designed to be used by non-technical users.

 

At AyGLOO we are experts in AI with more than 25 years doing machine learning and deep learning

We have a rigorous data governance program to meet your strictest needs, but if you need it, we can find a public dataset similar to yours so that the beginning of the PoC does not take forever.

 

If you need it, we can also build you the model with embedded interpretability

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

  • 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

The more critical the process, the more necessary is explainability​

  • Clinical medicine

 

  • Churn prediction

  • Fraud detection

  • Risk management

  • Forecasting

  • Healthcare management

  • Human Resources

  • Predictive maintenance

  • etc