The Need for Ethical, Responsible, and Trustworthy Artificial Intelligence for Environmental Sciences
This paper highlights a critical problem for researchers and practitioners in environmental sciences by warning against the naive assumption that AI is immune to ethical issues, though it is incremental as it extends discussions from other fields.
The paper argues that the use of AI in environmental sciences can lead to unintended societal consequences, such as bias and inequality, similar to issues seen in other domains, and calls for ethical and responsible AI practices to mitigate these risks and potentially reduce environmental injustice.
Given the growing use of Artificial Intelligence (AI) and machine learning (ML) methods across all aspects of environmental sciences, it is imperative that we initiate a discussion about the ethical and responsible use of AI. In fact, much can be learned from other domains where AI was introduced, often with the best of intentions, yet often led to unintended societal consequences, such as hard coding racial bias in the criminal justice system or increasing economic inequality through the financial system. A common misconception is that the environmental sciences are immune to such unintended consequences when AI is being used, as most data come from observations, and AI algorithms are based on mathematical formulas, which are often seen as objective. In this article, we argue the opposite can be the case. Using specific examples, we demonstrate many ways in which the use of AI can introduce similar consequences in the environmental sciences. This article will stimulate discussion and research efforts in this direction. As a community, we should avoid repeating any foreseeable mistakes made in other domains through the introduction of AI. In fact, with proper precautions, AI can be a great tool to help {\it reduce} climate and environmental injustice. We primarily focus on weather and climate examples but the conclusions apply broadly across the environmental sciences.