Neural network emulators have become an invaluable tool for a wide variety of
climate and weather prediction tasks. While showing incredibly promising
results, these networks do not have an inherent ability to produce equitable
predictions. That is, they are not guaranteed to provide a uniform quality of
prediction along any particular class or group of people. This potential for
inequitable predictions motivates the need for explicit representations of
fairness in these neural networks. To that end, we draw on methods for
enforcing analytical physical constraints in neural networks to bias networks
towards more equitable predictions. We demonstrate the promise of this
methodology using the task of climate model emulation. Specifically, we propose
a custom loss function which punishes emulators with unequal quality of
predictions across any prespecified regions or category, here defined using
human development index (HDI). This loss function weighs a standard loss metric
such as mean squared error against another metric which captures inequity along
the equity category (HDI), allowing us to adjust the priority of each term
before training. Importantly, the loss function does not specify a particular
definition of equity to bias the neural network towards, opening the door for
custom fairness metrics. Our results show that neural climate emulators trained
with our loss function provide more equitable predictions and that the equity
metric improves with greater weighting in the loss function. We empirically
demonstrate that while there is a tradeoff between accuracy and equity when
prioritizing the latter during training, an appropriate selection of the equity
priority hyperparameter can minimize loss of performance.

神经网络模拟器在气候和天气预测任务中已成为一种非常有价值的工具，但其无法保证提供公正的预测结果，因此需要在神经网络中采用明确的公平性表示方法。本研究提出一种自定义损失函数，通过惩罚具有不同质量预测结果的模拟器，在人类发展指数 (HDI) 等区域或类别上捕捉不平等，从而实现更加公正的气候模型模拟预测。实验结果表明，使用这种损失函数训练的神经气候模拟器提供更加公正的预测结果，平衡性度量随着权重增加而改善。在训练过程中，公平性和准确性之间存在权衡，但通过适当选择公平性优先超参数，可以最小化性能损失。

在神经气候模拟器中强制执行公平性

Enforcing Equity in Neural Climate Emulators

Machine learning has rapidly become a tool of choice for the astronomical
community. It is being applied across a wide range of wavelengths and problems,
from the classification of transients to neural network emulators of
cosmological simulations, and is shifting paradigms about how we generate and
report scientific results. At the same time, this class of method comes with
its own set of best practices, challenges, and drawbacks, which, at present,
are often reported on incompletely in the astrophysical literature. With this
paper, we aim to provide a primer to the astronomical community, including
authors, reviewers, and editors, on how to implement machine learning models
and report their results in a way that ensures the accuracy of the results,
reproducibility of the findings, and usefulness of the method.

尽管机器学习方法在天文学领域得到了广泛应用且不断发展，但目前天体物理文献中对于实施机器学习模型和报告结果的最佳实践、挑战和局限性，往往尚未完全报道。因此，本文旨在为天文学界的作者、审稿人和编辑提供入门指南，解决该问题，并确保结果的准确性、研究结果的可重复性和方法的实用性。