Communication Dans Un Congrès Année : 2021

Challenges in KDD and ML for sustainable development

D. Dao
  • Fonction : Auteur
S. Ermon
  • Fonction : Auteur
B. Goswami
  • Fonction : Auteur

Résumé

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.

Fichier non déposé

Dates et versions

hal-03970049 , version 1 (02-02-2023)

Identifiants

Citer

Laure Berti-Equille, D. Dao, S. Ermon, B. Goswami. Challenges in KDD and ML for sustainable development. KDD '21: Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining, ACM, Aug 2021, Singapore, Singapore. pp.4031-4032, ⟨10.1145/3447548.3470798⟩. ⟨hal-03970049⟩
116 Consultations
0 Téléchargements

Altmetric

Partager

  • More