Introduction

The rapid increase in both the quantity and complexity of data that are being generated daily in the fields of environmental science, environmental engineering, and environmental monitoring and protection is leading to exciting advancements in our understanding of not only environmental change, but its impacts on health, equity, and justice in vulnerable populations. As environmental data streams are used in areas beyond environmental science, there will be a continued need for methods that can fuse diverse data types and analyze these complex systems. This is especially relevant for health and sociodemographic datasets, which are often not as spatially and temporally resolved as environmental data, are prone to increased measurement error, and have uneven availability among regions, especially in the developing countries. Advanced data analytic approaches, such as artificial intelligence (AI), have become indispensable tools for revealing hidden patterns or deducing latent dependencies for which conventional analytic methods face major limitations or challenges. Importantly, there is potential for improving AI models to support transfer learning for domain-specific studies, including environmental impacts on health and equity. Moreover, although climate change is a global phenomenon, its impact in developing countries is anticipated to be substantially greater than in developed countries. However, development of the appropriate AI tools for evaluating environmental risk factors and climate equity in developing countries is further hindered by limited, noisy, or even nonexistent data records, especially in the healthcare sector. The analysis of complex environmental data in conjunction with health and demographics raises several interesting questions for development of future AI tools for social good that yet remain largely understudied both in AI, environmental and public health communities.

The goal of this workshop is to provide an interdisciplinary forum for addressing the critical questions on the role of AI for promoting climate justice, with the particular focus on healthcare applications. The workshop will offer a systematic linkage between the recent methodological advances in AI, climate studies, and health sciences, as well as will explore various fundamental challenges that arise when using AI for evaluating equity in health outcomes due to environmental exposure in developing countries. This workshop encourages submissions of innovative solutions for a broad range of climate change and health equity analysis problems. Some topics of interest include, but are not limited to:

• Domain-specific metrics for evaluation and integration of AI with environmental, health, and equity data

• Innovative feature engineering to explore the relationship between urban environment, communities, and health outcome

• Using and impact of AI on climate change impacts in Asia

• Assessing AI’s impact on greenhouse gas emissions and climate change adaptation

• Novel analysis and modeling approaches to integration of environmental, health, and equity data

• Quantifying uncertainty in equity modeling

• Tackling climate change and environmental justice with machine learning

• Reducing data bias and increasing explainability of AI models for health and environmental justice

• Transfer learning from data rich to data poor environments

• Up and down sampling for data integration across data domains (e.g., health, climate, demographics)

• Ethical issues of predicting climate change impacts

This workshop will be an in-person event at IJCAI 2023, taking place on August 20, 2023 in Macao S.A.R. The session will cover invited keynote talks, contributed talks, and a panel discussion.

Organizing Committee

 Program Committee

• Guofeng Cao  (University of Colorado Boulder)

• Yao-Yi Chiang  (University of Minnesota)

• Lelia Marie Hampton  (Massachusetts Institute of Technology)

• Nick LaHaye  (Jet Propulsion Laboratory, California Institute of Technology)

• Kyo Lee  (Jet Propulsion Laboratory, California Institute of Technology)

• Calvin Tribby  (Beckman Research Institute, City of Hope National Medical Center)