@inproceedings{fdi:010085546,
  title = {{C}hallenges in {KDD} and {ML} for sustainable development},
  author = {{B}erti-{E}quille, {L}aure and {D}ao, {D}. and {E}rmon, {S}. and {G}oswami, {B}.},
  editor = {},
  language = {{ENG}},
  abstract = {{A}rtificial {I}ntelligence 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 {U}nited {N}ations ({UN}) {S}ustainable {D}evelopment {G}oals ({SDG}s) by 2030. {I}n 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. {F}or 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. {D}espite recognized successes, there are many areas where {ML} is not applicable, performs poorly or gives insights that are not actionable. {T}his 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.},
  keywords = {},
  numero = {},
  pages = {4031--4032},
  booktitle = {{KDD} '21 : proceedings of the 27th {ACM} {SIGKDD} conference on knowledge discovery and data mining},
  year = {2021},
  DOI = {10.1145/3447548.3470798},
  ISBN = {978-1-4503-8332-5},
  URL = {https://www.documentation.ird.fr/hor/fdi:010085546},
}