Artificial Intelligence, and its rapid incursion into the (geo)sciences, was already impossible to ignore at last year’s EGU General Assembly. On Thursday, May 08, I attended the Great Debate on “The ethics of using AI in Geosciences: opportunities and risks”, a discussion spanning everything from scientific integrity and transparency to environmental costs, bias, and human responsibility.
You could use Generative AI to help refine and correct your grammar for a peer review written in a language that is not your first. You could also ask it to write the review for you. Technically, they both involve AI-generated text. Ethically, they are worlds apart.
That distinction matters because, despite frequent discussions about regulation, detection tools, and institutional guidance, much of scientific integrity still depends on individual choices. AI is not only changing what scientists can do, but potentially how scientists think, and how much thinking we choose to outsource to a co-worker that is always helpful, perhaps at times suspiciously so.
The recent report by the IUGS Commission on Geoethics approaches many of these issues directly, offering recommendations for the ethical use of AI in the geosciences. Going through every aspect of AI ethics in detail would be difficult in a single blog post (and would probably ensure nobody reaches the end of it). So instead, I want to focus on a few ideas from the debate that stayed with me afterwards.
The first one is agency. The truth is, ultimately, ethical AI use is less about what AI can do, and more about what you choose to delegate to it and about recognising that this remains a choice. You choose whether to verify the output of a model before including it in your work. You choose whether to rely on AI-generated summaries instead of reading papers yourself. You choose whether AI acts as an assistant to your thinking or begins replacing parts of the thinking process altogether.
Scientific integrity cannot be outsourced. Responsibility does not disappear simply because a tool becomes more powerful or more convenient. Convenience matters, and AI systems are extraordinarily good at reducing friction: drafting text, summarising information, generating code, organising ideas. But reducing friction can also reduce reflection. The easier it becomes to automate parts of scientific work, the easier it may become to disengage from them intellectually while still remaining accountable for the result. This takes me to the second idea that stayed with me after the debate: the importance of critical thinking not simply as a scientific skill, but as part of scientific ethics itself.
Science is not only about producing outputs. It is also about understanding how those outputs were reached, recognising uncertainty, identifying limitations, questioning assumptions, and being able to defend conclusions
But there is a meaningful difference between using.
I will conclude by quoting the IUGS report, because I don’t think I can phrase this better than they already have: “Ethics is not just about rules or consequences; it is situational, emotional, empathetic and relational. It is about moral character. Virtue ethics is a habitual disposition to act rightly – what a good and wise person would do.”
Choose to be a good and wise scientist.
Full text of the article.