Principles for both researchers and scholarly organizations offer a practical roadmap for ethical and effective implementation of new data practices
17 May 2023
AGU press contact:
Samson Reiny, +1 (202) 998-8654, [email protected] (UTC-4 hours)
WASHINGTON — AGU, the world’s largest Earth and space sciences association, has published a community report on the Ethical and Responsible Use of Artificial Intelligence and Machine Learning (AI/ML) in the Earth, Space and Environmental Sciences. The report’s six modules outline principles for both researchers and scholarly organizations that address topics such as transparency, documentation, interpretation, replication, risk, bias, participatory methods and organizational practices.
“We’re collecting more data than ever on every aspect of the universe—from Earth’s inner core to stars far outside our solar system, and increasingly analyzing these data together using computational approaches,” said Brooks Hanson, Executive Vice President of Science at AGU. “It’s an incredibly exciting time for science, but such meteoric change can bring ambiguity in how scientists carry out their work. As a trusted scientific organization, we must make sure that the endless possibilities posed by AI/ML are balanced by clear ethical standards to ensure researchers conduct their studies responsibly in a manner that will benefit the greater scientific community.”
AI/ML are powerful tools to evaluate diverse datasets, which can help Earth, space and environmental scientists uncover new insights about our planet and improve scientific predictions, including alerting communities to natural hazards, such as tornados and wildfires, or forecasting future climate-related risks, such as rising sea-levels. The new report acknowledges the importance of AI/ML protocols in science, while anticipating and mitigating the risks associated with these methods.
“When it comes to determining bias and uncertainty in datasets and models, our researchers are increasingly improving how to prepare documentation to make these details available,” said Shelley Stall, Vice President of Open Science Leadership at AGU. “Proportionally, we’re seeing a large uptick in Earth, space and environmental science research utilizing AI/ML methods. The principles identified in this report will provide ethical guidelines to inform researchers and their organizations on the importance of connecting known bias and uncertainty to decisions made about AI/ML configuration and workflows.”
Ethically using AI/ML in research requires a new way of thinking about methods. For example, validation and replication are core principles of science, but this can be complicated for research utilizing AI/ML, where the inner workings of models can be opaque. Traditionally, a study should explain in detail the entire scientific process, but a study utilizing AI/ML can only document the steps in the process, not the actual computation that results. Additionally, studies utilizing AI/ML should document potential biases, risks, and harms, especially as related to the promotion of justice and fairness.
“Trust is a critical topic for AI/ML research, but it’s not one we’re going to answer today,” said Guido Cervone, President of AGU’s Natural Hazard Section and Professor of Geography, Meteorology and Atmospheric Sciences, Associate Director of the Institute for Computational and Data Sciences and Director of the GEOvista Center at Penn State University. “Today’s AI/ML methods often represent knowledge in a form that is hard to verify and understand, and thus lacks some of the mechanisms that assess confidence in findings. Utilizing AI/ML requires a certain amount of trust generally not discussed with other analytical methods historically used in the Earth sciences. There are many opinions in this space so it’s clear we’ll need to continue the conversation around this in detail.”
AGU is committed to advancing the ethical use of AI/ML in research by implementing the principles outlined in this report and educating researchers about these principles. AGU members are governed by AGU’s Scientific Ethics and Integrity Policy when applying any research methods, including AI/ML. The new report is intended to supplement AGU’s policy by focusing on new ethical obligations, including more robust and inclusive research methods, new forms of documentation, new methods for replicability, continuing responsibility for the impacts of research, and proactive expectations of professional societies, funders, and other institutional actors.
The principles outlined in the new report were developed through a community working session facilitated by Joel Cutcher-Gershenfeld, Professor and Associate Dean of the Heller School for Social Policy and Management at Brandeis University. The effort was guided by a steering committee comprising scientists from the Academic Data Science Alliance; Emory University; NASA /ADNET Systems, Inc.; North Carolina Institute for Climate Studies; Penn State University and The Wharton School. This work was funded by NASA (Grant 80NSSC22K0734). The report is published in the Earth and Space Science Open Archive, AGU’s community server to accelerate the open discovery and dissemination of Earth, environmental and space research.
AGU (www.agu.org) is a global community supporting more than half a million advocates and professionals in Earth and space sciences. Through broad and inclusive partnerships, AGU aims to advance discovery and solution science that accelerate knowledge and create solutions that are ethical, unbiased and respectful of communities and their values. Our programs include serving as a scholarly publisher, convening virtual and in-person events and providing career support. We live our values in everything we do, such as our net zero energy renovated building in Washington, D.C. and our Ethics and Equity Center, which fosters a diverse and inclusive geoscience community to ensure responsible conduct.