No Location Left Behind: Measuring and Improving the Fairness of Implicit Representations for Earth Data
Implicit neural representations (INRs) exhibit growing promise in addressing Earth representation challenges, ranging from emissions monitoring to climate modeling. However, existing methods disproportionately prioritize global average performance, whereas practitioners require fine-grained insights to understand biases and variations in these models. To bridge this gap, we introduce FAIR-Earth: a first-of-its-kind dataset explicitly crafted to examine and challenge inequities in Earth representations. FAIR-Earth comprises various high-resolution Earth signals and uniquely aggregates extensive metadata along stratifications like landmass size and population density to assess the fairness of models. Evaluating state-of-the-art INRs across the various modalities of FAIR-Earth, we uncover striking performance disparities. Certain subgroups, especially those associated with high-frequency signals (e.g., islands, coastlines), are consistently poorly modeled by existing methods. In response, we propose spherical wavelet encodings, building on previous spatial encoding research. Leveraging the multi-resolution capabilities of wavelets, our encodings yield consistent performance over various scales and locations, offering more accurate and robust representations of the biased subgroups. These open-source contributions represent a crucial step towards the equitable assessment and deployment of Earth INRs.
Code (0)
등록된 구현이 없습니다.
Tasks
FairnessSimilar Papers 제목 키워드 기반
Guaranteeing Maximin Shares: Some Agents Left Behind
The maximin share (MMS) guarantee is a desirable fairness notion for allocating indivisible goods. While MMS allocations do not always exist, several approximation techniques have been developed to ensure that all agents…
FairnessEmergent Unfairness in Algorithmic Fairness-Accuracy Trade-Off Research
Across machine learning (ML) sub-disciplines, researchers make explicit mathematical assumptions in order to facilitate proof-writing. We note that, specifically in the area of fairness-accuracy trade-off optimization sc…
BIG-bench Machine LearningFairnessEthical AI on the Waitlist: Group Fairness Evaluation of LLM-Aided Organ Allocation
Large Language Models (LLMs) are becoming ubiquitous, promising automation even in high-stakes scenarios. However, existing evaluation methods often fall short -- benchmarks saturate, accuracy-based metrics are overly si…
FairnessFairness for distribution network operations and planning
The incorporation of fairness into the distribution network (DN) planning and operation has become a key goal of recent studies. The cost of implementing fairness, denominated the price of fairness (PoF), covers the effi…
A Data Envelopment Analysis Approach for Assessing Fairness in Resource Allocation: Application to Kidney Exchange Programs
Kidney exchange programs have significantly increased transplantation rates but raise pressing questions about fairness in organ allocation. We present a novel framework leveraging Data Envelopment Analysis (DEA) to eval…
Conformal PredictionFairnessPrediction Intervals