AI Ethics

Solutions supported by Artificial Intelligence are more and more popular and ubiquitous. We meet them in business and everyday life. MAZAJ creates great things that generate income and reduce costs, but we also understand our responsibility while implementing artificial intelligence.

Here ethics becomes the highest priority for us.


Explainability means understanding how machine learning models predict labels in different ways throughout the entire life cycle of an AI application.


Fairness stands for researching, reporting, and mitigating bias and discrimination in machine learning models across the entire life cycle of an AI application.


Robustness indicates development in the resilience to the contradictory nature of machine learning models, thus accelerating the deployment of secure AI applications.


Transparency means delivering complete knowledge to increase the understanding and management of how artificial intelligence was created and implemented.


Privacy respects full disclosure of what data is collected, who has access to it, how it will be used, stored. It also means collecting and storing only the minimum data necessary.