The Need for Ethical, Responsible, and Trustworthy Artificial Intelligence for Environmental Sciences

TL;DR

This paper emphasizes the importance of ethical and responsible AI use in environmental sciences, highlighting potential societal risks and advocating for precautions to prevent unintended consequences while leveraging AI for positive impact.

Contribution

It raises awareness of ethical issues in AI for environmental sciences and provides examples demonstrating potential societal risks, urging the community to adopt responsible practices.

Findings

AI can introduce societal biases in environmental data analysis

Responsible AI use can help reduce environmental injustice

Awareness and precautions are essential to prevent unintended consequences

Abstract

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.