Artificial intelligence and renegotiation of commercial lease contracts affected by pandemic-related contingencies from Covid-19. The project A.I.A.Co.
Parton M., Angelone M., Metta C., D'Ovidio S., Massarelli R., Moscardelli L., Amato G.
Computer Science - Computers and Society Artificial Intelligence equitative algorithms commercial lease contracts Human-Computer Interaction (cs.HC) computational law Predictive justice Machine Learning Computers and Society (cs.CY) K.4 predictive justice FOS: Computer and information sciences Artificial Intelligence (cs.AI) Predictive Justice Computational Law Computational law Machine learning Computer Science - Human-Computer Interaction Computer Science - Artificial Intelligence
This paper aims to investigate the possibility of using artificial intelligence (AI) to resolve the legal issues raised by the Covid-19 emergency about the fate of continuing execution contracts, or those with deferred or periodic execution, as well as, more generally, to deal with exceptional events and contingencies. We first study whether the Italian legal system allows for ''maintenance'' remedies to cope with contingencies and to avoid the termination of the contract, while ensuring effective protection of the interests of both parties. We then give a complete and technical description of an AI-based predictive framework, aimed at assisting both the Magistrate (in the course of litigation) and the parties themselves (in out-of-court proceedings) in the redetermination of the rent of commercial lease contracts. This framework, called A.I.A.Co. for Artificial Intelligence for contract law Against Covid-19, has been developed under the Italian grant ''Fondo Integrativo Speciale per la Ricerca''.
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@inproceedings{oai:it.cnr:prodotti:492326,
title = {Artificial intelligence and renegotiation of commercial lease contracts affected by pandemic-related contingencies from Covid-19. The project A.I.A.Co.},
author = {Parton M. and Angelone M. and Metta C. and D'Ovidio S. and Massarelli R. and Moscardelli L. and Amato G.},
doi = {10.48550/arxiv.2210.09515},
year = {2023}
}
