• ranking
• fairness
• bias
• algorithms
• clustering
• XAI
• counterfactual
• privacy
• data generation
• synthetic data

The use of artificial intelligence systems and machine learning algorithms is increasingly prevalent in everyday life, spanning various sectors. Given this widespread adoption, it is imperative to establish suitable frameworks for the analysis, verification, and study of this extensive field. The risk of inappropriate use of intelligent systems should not be underestimated. In this work a synthetic generator is implemented to populate a database of virtual candidates and job offers. The data are ranked using a specific algorithm. Finally, a counter-factual value extraction system is designed to conduct technical analyses related to fairness and bias issues of the developed algorithm. The implemented methodology proves to be adequately suited for the study and the verification of the system. The technique is also able to emphasize strengths and weaknesses of the ranking. Moreover, it is also able to provide feedback to the subjects in the database to improve their score.