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Interdisciplinary Research in Artificial Intelligence: Challenges and Opportunities

Remy Kusters 1 Dusan Misevic 1 Hugues Berry 2 Antoine Cully 3 Yann Le Cunff 4 Loic Dandoy 1 Natalia Díaz-Rodríguez 5, 6 Marion Ficher 1 Jonathan Grizou 1 Alice Othmani 7 Themis Palpanas 8 Matthieu Komorowski 3 Patrick Loiseau 9 Clément Moulin-Frier 5 Santino Nanini 1 Daniele Quercia 10 Michele Sebag 11 Françoise Soulié Fogelman 12 Sofiane Taleb 1 Liubov Tupikina 1, 13 Vaibhav Sahu 1 Jill-Jênn Vie 14 Fatima Wehbi 1 
2 BEAGLE - Artificial Evolution and Computational Biology
LIRIS - Laboratoire d'InfoRmatique en Image et Systèmes d'information, Inria Grenoble - Rhône-Alpes, LBBE - Laboratoire de Biométrie et Biologie Evolutive - UMR 5558
9 POLARIS - Performance analysis and optimization of LARge Infrastructures and Systems
Inria Grenoble - Rhône-Alpes, LIG - Laboratoire d'Informatique de Grenoble
11 TAU - TAckling the Underspecified
Inria Saclay - Ile de France, LRI - Laboratoire de Recherche en Informatique
14 Scool - Scool
Inria Lille - Nord Europe, CRIStAL - Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189
Abstract : The use of artificial intelligence (AI) in a variety of researchfields is speeding up multipledigital revolutions, from shifting paradigms in healthcare, precision medicine and wearablesensing, to public services and education offered to the masses around the world, to futurecities made optimally efficient by autonomous driving. When a revolution happens, theconsequences are not obvious straight away, and to date, there is no uniformly adaptedframework to guide AI research to ensure a sustainable societal transition. To answer thisneed, here we analyze three key challenges to interdisciplinary AI research, and deliverthree broad conclusions: 1) future development of AI should not only impact other scientificdomains but should also take inspiration and benefit from otherfields of science, 2) AIresearch must be accompanied by decision explainability, dataset bias transparency aswell as development of evaluation methodologies and creation of regulatory agencies toensure responsibility, and 3) AI education should receive more attention, efforts andinnovation from the educational and scientific communities. Our analysis is of interest notonly to AI practitioners but also to other researchers and the general public as it offers waysto guide the emerging collaborations and interactions toward the most fruitful outcomes.
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Submitted on : Friday, May 28, 2021 - 12:18:51 PM
Last modification on : Saturday, September 24, 2022 - 2:36:04 PM
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Remy Kusters, Dusan Misevic, Hugues Berry, Antoine Cully, Yann Le Cunff, et al.. Interdisciplinary Research in Artificial Intelligence: Challenges and Opportunities. Frontiers in Big Data, Frontiers, 2020, 3, ⟨10.3389/fdata.2020.577974⟩. ⟨hal-03111148⟩



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