Yes. The June 2, 2026 Leiden Declaration on Artificial Intelligence and Mathematics explicitly says mathematicians can choose whether and how to adopt AI, including whether to use it at all. The International Mathematical Union (IMU) endorses the declaration. It recognizes refusal as a legitimate option; it does not claim that refusing is always easy or free of professional consequences.
What the Leiden Declaration actually says
The declaration treats mathematics not only as a collection of results but as a human practice built around proof, understanding, attribution, independent verification, shared standards of evaluation, and the freedom to set research directions. Its individual recommendations ask mathematicians to consider which tools to use “or whether to use them at all.”
That is a choice, not a ban. The declaration also supports selective adoption, careful disclosure of automated-tool use, proper credit for sources, and human responsibility for correctness and citations. It calls for arguments to be presented in ways that make them easier to review and verify. The IMU describes the declaration as a starting point for discussion and recognizes that colleagues may disagree with parts of it.
Why some mathematicians may choose not to use AI
The declaration raises concerns about plausible but unreliable arguments, difficult review, missed attribution, distorted incentives, unequal access, and research priorities shifting toward problems that are easier to automate. These are risks it identifies, not proof that every AI system or use produces those harms.
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It also points to questions of control and values: a mathematician may not want to depend on systems owned by organizations whose priorities they do not share, or may judge that a research partnership has ethical costs. The declaration presents alternatives to blanket adoption, including using smaller, non-proprietary, or more energy-efficient systems when they are adequate, and accepting slower work when that better protects mathematical values.
AI is not one mathematical tool
Refusing “AI” can mean different things because the label covers distinct methods and uses, including formal proof assistants, symbolic reasoning, machine-learning techniques, and general-purpose language models. Jeremy Avigad’s March 2026 essay, revised April 6, says AI-related methods have had a handful of notable mathematical successes while remaining niche; it distinguishes these approaches rather than treating them as interchangeable. See “Mathematicians in the Age of AI”.
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Reliability concerns should be read with similar care. In Oberwolfach Reports 43/2025, mathematician Melanie Matchett Wood described graduate- and research-level language-model mathematics as “disturbingly unreliable,” recounting false or conflicting answers on examples from group theory and other graduate topics. The report also describes proof assistants such as Lean as a possible way to check formalized arguments. Wood’s account is a dated researcher’s assessment, not a controlled benchmark of every current system.
A 2026 Scientific American report describes the declaration’s concerns about subtle errors in AI-assisted proofs and commercial demonstrations appearing before peer-reviewed methods. It also quotes IMU publishing committee chair Ilka Agricola saying responsible AI can be useful, while warning that the surrounding problems can overshadow that benefit. The practical point is not that AI has no mathematical value, but that its usefulness depends on the task and that a result still needs scrutiny.
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What refusing AI can mean in practice
A mathematician can decide not to use AI in their own research, or to draw narrower boundaries—for example, declining to use generated text while considering a tool that checks a formal proof. A deliberate policy can help make that boundary clear:
- Set the scope: Decide whether the choice applies to all AI-related tools or only to particular uses, such as generating arguments, drafting prose, or handling research data.
- Check the obligations around the work: A journal, funder, employer, collaborator, or project may have relevant rules or expectations. The declaration supports disclosure and accountability, but does not establish one universal policy for institutions or publishers.
- Explain the boundary when it matters: In collaborative or submitted work, say what tools were or were not used when disclosure is relevant or required. Keep responsibility for mathematical claims and citations with the human authors.
- Revisit the decision by task: A tool’s suitability for formal verification does not establish that it is suitable for inventing a proof, and experience with one language model does not settle the case for every method.
The declaration’s recommendations make room for personal choice, but the available statements do not show how often mathematicians refuse AI, or whether doing so carries the same costs in every workplace, collaboration, or field. A choice can be recognized without being consequence-free.
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What endorsements and signature counts do—and don’t—show
The IMU endorsement gives the declaration professional visibility, not the force of a universal rule. The declaration website displayed 4,237 signatories on October 3, 2026. That is a live, self-selected count, not a representative survey of mathematicians or a measure of AI use. Likewise, Nature’s May 2025 poll covered 5,000 researchers broadly; it does not establish how mathematicians specifically feel about adopting or refusing AI.
For additional context, the declaration describes a September 2025 conference with around 60 participants from 10 countries. That describes the meeting, not the views of the wider mathematical community.
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How to decide whether to use a tool
For mathematicians who are open to some forms of AI but not others, the declaration suggests weighing a tool against the task rather than adopting it by default. Useful questions include:
- Can the result be independently checked, and does the tool make verification easier or harder?
- Will use be disclosed appropriately, and can sources and contributions be attributed accurately?
- Is the tool accessible and proportionate in cost and resource use, and does reliance on proprietary control fit the researcher’s values?
- Does the tool suit this specific mathematical task, rather than merely producing a plausible-looking answer?
- Could the work or partnership create ethical concerns or steer research toward problems chosen for ease of automation?
These are decision criteria, not a ranking of products or a recommendation to use any particular system. The declaration’s central standard is accountable choice: preserve rigor and human responsibility whether the answer is adoption, selective use, or refusal.
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