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

arXiv: https://arxiv.org/abs/2112.08453 · HTML: https://mlsanity.com/paper/2112.08453/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 83 references that are in the map.
- First posted on arXiv 15 Dec 2021.

## Builds on (most cited first, 8 of 8)

- [A Survey on Bias and Fairness in Machine Learning](https://mlsanity.com/paper/1908.09635/) (2019)
- [Green AI](https://mlsanity.com/paper/1907.10597/) (2019)
- [Skillful Precipitation Nowcasting using Deep Generative Models of Radar](https://mlsanity.com/paper/2104.00954/) (2021)
- [A Survey on Green Deep Learning](https://mlsanity.com/paper/2111.05193/) (2021)
- [Explanation Methods in Deep Learning: Users, Values, Concerns and Challenges](https://mlsanity.com/paper/1803.07517/) (2018)
- [Machine Learning for Stochastic Parameterization: Generative Adversarial Networks in the Lorenz '96 Model](https://mlsanity.com/paper/1909.04711/) (2019)
- [Adversarial Risk and Robustness: General Definitions and Implications for the Uniform Distribution](https://mlsanity.com/paper/1810.12272/) (2018)
- [Development and Interpretation of a Neural Network-Based Synthetic Radar Reflectivity Estimator Using GOES-R Satellite Observations](https://mlsanity.com/paper/2004.07906/) (2020)

## Built on by (most cited first, 12 of 12)

- [Application-Driven Innovation in Machine Learning](https://mlsanity.com/paper/2403.17381/) (2024)
- [A Primer on Topological Data Analysis to Support Image Analysis Tasks in Environmental Science](https://mlsanity.com/paper/2207.10552/) (2022)
- [Fairness and representation in satellite-based poverty maps: Evidence of urban-rural disparities and their impacts on downstream policy](https://mlsanity.com/paper/2305.01783/) (2023)
- [Segmenting across places: The need for fair transfer learning with satellite imagery](https://mlsanity.com/paper/2204.04358/) (2022)
- [SRViT: Vision Transformers for Estimating Radar Reflectivity from Satellite Observations at Scale](https://mlsanity.com/paper/2406.16955/) (2024)
- [Explainable AI-Based Interface System for Weather Forecasting Model](https://mlsanity.com/paper/2504.00795/) (2025)
- [Unlocking the Potential of Global Human Expertise](https://mlsanity.com/paper/2411.00156/) (2024)
- [Identifying Trustworthiness Challenges in Deep Learning Models for Continental-Scale Water Quality Prediction](https://mlsanity.com/paper/2503.09947/) (2025)
- [AIJIM: A Scalable Model for Real-Time AI in Environmental Journalism](https://mlsanity.com/paper/2503.17401/) (2025)
- [Knowledge-Guided Machine Learning: Illustrating the use of Explainable Boosting Machines to Identify Overshooting Tops in Satellite Imagery](https://mlsanity.com/paper/2507.03183/) (2025)
- [Conformal PM2.5 Mapping Under Spatial Covariate Shift: Satellite-Reanalysis Fusion for Africa's Green Industrial Transition](https://mlsanity.com/paper/2604.22787/) (2026)
- [The impact of feature engineering and an optimisation framework for ocean colour machine learning](https://mlsanity.com/paper/2608.19899/) (2026)

## Abstract (arXiv)

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.