Earth and space sciences association the American Geophysical Union (AGU) has published a community report on the ethical and responsible use of AI and machine learning (AI/ML) in the Earth, space and environmental sciences.
The report’s six modules outline principles for researchers and scholarly organizations and address topics such as transparency, documentation, interpretation, replication, risk, bias, participatory methods and organizational practices.
Brooks Hanson, executive vice president of science at AGU, said, “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. 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 and ML can be used to 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.
Shelley Stall, vice president of open science leadership at AGU, said, “When it comes to determining bias and uncertainty in data sets and models, our researchers are increasingly improving how to prepare documentation to make these details available. 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.”
According to the AGU report, 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 and 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.”
To view the community report in full, click here.
