extended-abstract

KDD 2024 Special Day - AI for Environment

Authors:

Karina Gibert,

Wee Hyong Tok,

Miquel Sànchez-MarrèAuthors Info & Claims

KDD '24: Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining

Pages 6695 - 6696

https://doi.org/10.1145/3637528.3673869

Published: 24 August 2024 Publication History

Get Access

Abstract

Environmental problems such as air pollution monitoring and prevention, flood detection and prevention, land use, forest management, river water quality, wastewater treatment supervision, etc. are more complex than typical real-world problems usually AI faces to. This added complexity rises from several aspects, such as the randomness shown by most of environmental processes involved, the 2D/3D nature of involved problems, the temporal aspects, the spatial aspects, the inexactness of the information, etc.

In fact, environmental problems belong to the most difficult problems with a lot of inexactness and uncertainty, and possibly conflicting objectives to be solved according to several classifications such as the one by Funtowicz & Ravetz (Funtowicz & Ravetz, 1999), which states that there are 3 kinds of problems. Also, they are non-structured problems in the classification proposed by H. Simon (Simon, 1966).

All this complexity means that to effectively solve those problems a lot of knowledge is needed. This knowledge can be theoretical knowledge expressed in mechanistic models, such as the Gravidity Newton's Theory, or it can be empirical knowledge that can be expressed by means of empirical models, originated by some data and observations (data-driven knowledge) or by the expertise gathered by people when coping with such problems (model-driven knowledge, particularly expert-based knowledge).

The KDD 2024 Special Day for AI for environment brings together researchers and practitioners to present their perspective on this very timely topic on how AI can be used for good, and improving the environment where we all live in.

Index Terms

Index terms have been assigned to the content through auto-classification.

Recommendations

Why "Artificial Intelligence" Should Not Be Regulated
Lawmakers all over the world have started to draft new regulations for Artificial Intelligence (AI). While the European Union is currently leading the way with its AI Act, many other legislators will follow and already positioned themselves with white ...
Benchmarking Generative AI Performance Requires a Holistic Approach
Performance Evaluation and Benchmarking
Abstract
The recent focus in AI on Large Language Models (LLMs) has brought the topic of trustworthy AI to the forefront. Along with the excitement of human-level performance, the Generative AI systems enabled by LLMs have raised many concerns about ...
Artificial Intelligence and the Future of Evaluation: from Augmented to Automated Evaluation
The recent developments in artificial intelligence (AI) are revolutionizing professional practices across various professional fields, including evaluation. With its advanced automation and learning capabilities, AI is bringing significant changing to the ...

Comments

Information & Contributors

Information

Published In

KDD '24: Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining

August 2024

6901 pages

ISBN:9798400704901

DOI:10.1145/3637528

General Chairs:
Ricardo Baeza-Yates
Northeastern University, USA
,
Francesco Bonchi
CENTAI / Eurecat, Italy

Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 24 August 2024

Check for updates

Author Tags

Qualifiers

Extended-abstract

Conference

KDD '24

Sponsor:

KDD '24: The 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining

August 25 - 29, 2024

Barcelona, Spain

Acceptance Rates

Overall Acceptance Rate 1,133 of 8,635 submissions, 13%

Upcoming Conference

KDD '25

Sponsor:
sigkdd
sigkdd

The 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining

August 3 - 7, 2025

Toronto , ON , Canada

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

0
Total Citations
71
Total Downloads

Downloads (Last 12 months)71
Downloads (Last 6 weeks)16

Reflects downloads up to 24 Dec 2024

Other Metrics

View Author Metrics

Citations

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Index Terms

Recommendations

Why "Artificial Intelligence" Should Not Be Regulated

Benchmarking Generative AI Performance Requires a Holistic Approach

Artificial Intelligence and the Future of Evaluation: from Augmented to Automated Evaluation