GEN-CE Counterfactual Explanation (CE) techniques have garnered attention as a means to provide insights to the users engaging with AI systems. While extensively researched in domains such as medical imaging and autonomous vehicles, Graph Counterfactual Explanation (GCE) methods have been comparatively under-explored.

GRETEL: Our main goal is to create a generic platform that allows the researchers to speed up the process of developing and testing new Graph Counterfactual Explanation Methods. GRETEL provides all the necessary building blocks to create bespoke explanation pipelines.

FAIR-EDU: When a bias impacts human beings as individuals or as groups characterized by certain legally-protected sensitive attributes (e.g., gender), the inequalities reinforced by search and recommendation algorithms can lead to severe societal consequences, such as discrimination and unfairness.

SoBigData.it: The project aims to strengthen the SoBigData research infrastructure (www.sobigdata.eu), coordinated by CNR-ISTI, with the goal of enhancing interdisciplinary and innovative research on the multiple aspects of social complexity by combining data and model-driven approach. SoBigData emphasizes the concept of “responsible data science”, considering the ethical values as one of the pillars of reliable use of big data analytics and artificial intelligence technologies.