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Assessing whether artificial intelligence is an enabler or an inhibitor of sustainability at indicator level

Authors :
Gupta, S.
Langhans, S. D.
Domisch, S.
Nerini, Francesco Fuso
Felländer, A.
Battaglini, Manuela
Tegmark, Max
Vinuesa, Ricardo
Gupta, S.
Langhans, S. D.
Domisch, S.
Nerini, Francesco Fuso
Felländer, A.
Battaglini, Manuela
Tegmark, Max
Vinuesa, Ricardo
Publication Year :
2021

Abstract

Since the early phase of the artificial-intelligence (AI) era expectations towards AI are high, with experts believing that AI paves the way for managing and handling various global challenges. However, the significant enabling and inhibiting influence of AI for sustainable development needs to be assessed carefully, given that the technology diffuses rapidly and affects millions of people worldwide on a day-to-day basis. To address this challenge, a panel discussion was organized by the KTH Royal Institute of Technology, the AI Sustainability Center and MIT Massachusetts Institute of Technology, gathering a wide range of AI experts. This paper summarizes the insights from the panel discussion around the following themes: The role of AI in achieving the Sustainable Development Goals (SDGs); AI for a prosperous 21st century; Transparency, automated decision-making processes, and personal profiling; and Measuring the relevance of Digitalization and Artificial Intelligence (D&AI) at the indicator level of SDGs. The research-backed panel discussion was dedicated to recognize and prioritize the agenda for addressing the pressing research gaps for academic research, funding bodies, professionals, as well as industry with an emphasis on the transportation sector. A common conclusion across these themes was the need to go beyond the development of AI in sectorial silos, so as to understand the impacts AI might have across societal, environmental, and economic outcomes. The recordings of the panel discussion can be found at: https://www.kth.se/en/2.18487/evenemang/the-role-of-ai-in-achieving-the-sdgs-enabler-or-inhibitor-1.1001364?date=2020–08–20&length=1&orglength=185&orgdate=2020–06–30 Short link: https://bit.ly/2Kap1tE<br />QC 20220323

Details

Database :
OAIster
Notes :
English
Publication Type :
Electronic Resource
Accession number :
edsoai.on1312826063
Document Type :
Electronic Resource
Full Text :
https://doi.org/10.1016.j.treng.2021.100064