Berti-Equille Laure, Dao D., Ermon S., Goswami B. (2021). Challenges in KDD and ML for sustainable development. In :
KDD '21 : proceedings of the 27th ACM SIGKDD conference on knowledge discovery and data mining. New York : ACM, 4031-4032. ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 27., [En ligne] Singapour (SGP), 2021/08/14-18. ISBN 978-1-4503-8332-5.
Titre du document
Challenges in KDD and ML for sustainable development
KDD '21 : proceedings of the 27th ACM SIGKDD conference on knowledge discovery and data mining
Source
New York : ACM, 2021,
4031-4032 ISBN 978-1-4503-8332-5
Colloque
ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 27., [En ligne] Singapour (SGP), 2021/08/14-18
Artificial Intelligence and machine learning techniques can offer powerful tools for addressing the greatest challenges facing humanity and helping society adapt to a rapidly changing climate, respond to disasters and pandemic crisis, and reach the United Nations (UN) Sustainable Development Goals (SDGs) by 2030. In recent approaches for mitigation and adaptation, data analytics and ML are only one part of the solution that requires interdisciplinary and methodological research and innovations. For example, challenges include multi-modal and multi-source data fusion to combine satellite imagery with other relevant data, handling noisy and missing ground data at various spatio-temporal scales, and ensembling multiple physical and ML models to improve prediction accuracy. Despite recognized successes, there are many areas where ML is not applicable, performs poorly or gives insights that are not actionable. This tutorial will survey the recent and significant contributions in KDD and ML for sustainable development and will highlight current challenges that need to be addressed to transform and equip engaged sustainability science with robust ML-based tools to support actionable decision-making for a more sustainable future.
Plan de classement
Sciences fondamentales / Techniques d'analyse et de recherche [020]
;
Informatique [122]
