Explainable AI Project

Using Artificial Intelligence to detect and understand global uncertainty, identifing the most critical factors which drive industrial and geopolitical risks.

THE AI PROJECT

The complex nature of data-driven models often makes it hard to understand the information fully and apply it effectively. In our research, we suggest a way to use an Artificial Intelligence (AI) system that can warn us earlier about potential conflicts.

UNITO Research Group

The AI research group of the Department of Economics and Statistics of the University of Turin was selected with a research project titled: ‘XAI with Temporal Transformers for Conflict Prediction: An Integrated Gradients Approach’.

University of Notre Dame

The ‘Rome Call for AI Ethics: A Global University Summit’ was held on October 2022, at the University of Notre Dame. The event was organised to discuss how to implement the principles of the Rome Call in the university.

Background

The ‘Rome Call for AI Ethics: A Global University Summit’ was held on 26-27 October 2022 at the University of Notre Dame in Notre Dame, IN, USA. The event was organised by the Pontifical Academy for Life, IBM and the University of Notre Dame to discuss the challenges and opportunities of implementing the principles of the Rome Call for AI Ethics in the university context. During the event, the Notre Dame-IBM Technology Ethics Lab announced, for the 36 Universities that participated in the summit, a grant of 5.000 $ for selected projects based on one or more of the six Rome Call principles (transparency, inclusion, accountability, impartiality, trustworthiness and privacy).
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International Goals

The University of Notre Dame’s goal is the publication of a white paper on the selected research and a virtual workshop to discuss opportunities for further
collaboration.

The AI research group of the Department of Economics and Statistics of the University of Turin was selected with a research project titled: ‘XAI with Temporal Transformers for Conflict Prediction: An Integrated Gradients Approach’.

University of Notre Dame

University of Turin

Department of Economics and Statistics

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