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Research ethics and attribution

Honest research practice: attribution and citation, priority and authorship, plagiarism, disclosure of computational and AI assistance, handling errors, and clearly separating established results from conjecture.

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Builds on

Reading the mathematical literature

Required prerequisites, all the way down: 12

You will learn to

  • Cite sources accurately, including results learned from talks, software, databases and AI systems.
  • Decide authorship and acknowledgements fairly in a collaborative project, knowing that mathematics commonly lists authors alphabetically.
  • Respond appropriately when an error is found in one's own published work or in someone else's.

Definition

  • Plagiarism is presenting another person's words, ideas or results as one's own.
  • Attribution credits the origin of an idea, result, proof, code or data set.
  • An erratum or corrigendum is a published correction.

Theorem

  • Mathematical truth does not depend on who discovers it, but credit for a discovery belongs to those who made it.
  • Agreement among several AI systems is not a substitute for verification: their errors may be correlated in unknown ways, so proofs must be checked by people or by verified formal methods.

Common misconceptions

  • Thinking well-known results need no citation.
  • Treating AI-generated text or proofs as exempt from attribution and verification.
  • Believing that admitting an error does more harm than concealing it.

Optional discoveries

Learning pathways