Interdisciplinary Research in Artificial Intelligence: Challenges and Opportunities
Résumé
The use of artificial intelligence (AI) in a variety of research fields 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, the consequences 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 this need, 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) AI research must be accompanied by decision explainability, dataset bias transparency aswell as development of evaluation methodologies and creation of regulatory agencies to ensure 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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