Curating AI-Ready Datasets for Equity and Environmental Justice: A Data-Centric AI Case Study

An equitable and environmentally just community is essential in order to avoid disproportionate burden borne by vulnerable communities. This need becomes pressing in the aftermath of an extreme event such as disaster or hazard when it is difficult for the governing bodies to implement resource allocation as per the need. Artificial Intelligence (AI) algorithms can help surface Equity and Environmental Justice (EEJ) issues when trained on EEJ datasets. However, curating AI-ready EEJ training datasets is challenging due to differences in factors such as heterogeneity, resolution, modality, and level of expertise in labeling. Additionally, EEJ issues involve sensitive information where uncertainties and errors could degrade the performance of AI algorithms. For eg. error in seasonal crop yield information can highly effect the prediction of annual crop yield. To address these challenges, Data-centric AI (DCAI) methods are employed, which enhance AI algorithm performance even with limited training samples. DCAI prioritizes data quality, thereby reducing the adverse effects of uncertainties and errors during the model training process. This research proposes a novel dataset and benchmark for analyzing the effect of the Maui Wildfire of 2023 for Equity and Environmental Justice (EEJ) issues. The proposed dataset aligns with the concepts of DCAI such as annotation quality, data preprocessing, privacy, feature engineering, governance and provenance. The proposed AI-ready dataset is available on HuggingFace at https://huggingface.co/datasets/nasa-impact/ml4ej-maui-wildfire.